{"id":13396,"date":"2026-11-02T14:52:09","date_gmt":"2026-11-02T11:52:09","guid":{"rendered":"https:\/\/umram.bilkent.edu.tr\/index.php\/mr-glossary\/"},"modified":"2026-11-02T14:52:11","modified_gmt":"2026-11-02T11:52:11","slug":"mr-glossary","status":"publish","type":"page","link":"https:\/\/umram.bilkent.edu.tr\/index.php\/mr-glossary\/","title":{"rendered":"MR Glossary"},"content":{"rendered":"\t<div class=\"mrg\" id=\"mrg\" data-lang=\"en\">\n\t\t<p class=\"mrg-lead\">Core concepts of magnetic resonance imaging, of fMRI and diffusion MRI analysis, and of the AI methods used in MRI, with their Turkish equivalents.<\/p>\n\t\t<div class=\"mrg-bar\">\n\t\t\t<div class=\"mrg-search\">\n\t\t\t\t<svg viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" aria-hidden=\"true\"><circle cx=\"11\" cy=\"11\" r=\"7\"\/><path d=\"m20 20-3.5-3.5\"\/><\/svg>\n\t\t\t\t\t\t\t\t<input type=\"search\" id=\"mrg-q\" autocomplete=\"off\" spellcheck=\"false\" placeholder=\"Search a term, abbreviation or Turkish equivalent\" aria-label=\"Search a term, abbreviation or Turkish equivalent\">\n\t\t\t\t<button class=\"mrg-clear\" type=\"button\" hidden aria-label=\"Clear search\">&times;<\/button>\n\t\t\t<\/div>\n\t\t\t<div class=\"mrg-chips\" role=\"group\" aria-label=\"Categories\">\n\t\t\t\t<button type=\"button\" class=\"mrg-chip\" data-cat=\"\" aria-pressed=\"true\">All <span class=\"mrg-n\">203<\/span><\/button>\n\t\t\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-chip mrg-c-fizik\" data-cat=\"fizik\" aria-pressed=\"false\"><span class=\"mrg-dot\"><\/span>Basic physics <span class=\"mrg-n\">18<\/span><\/button>\n\t\t\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-chip mrg-c-goruntu\" data-cat=\"goruntu\" aria-pressed=\"false\"><span class=\"mrg-dot\"><\/span>Image formation <span class=\"mrg-n\">17<\/span><\/button>\n\t\t\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-chip mrg-c-sekans\" data-cat=\"sekans\" aria-pressed=\"false\"><span class=\"mrg-dot\"><\/span>Sequences and contrast <span class=\"mrg-n\">22<\/span><\/button>\n\t\t\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-chip mrg-c-hizlandirma\" data-cat=\"hizlandirma\" aria-pressed=\"false\"><span class=\"mrg-dot\"><\/span>Acceleration and reconstruction <span class=\"mrg-n\">8<\/span><\/button>\n\t\t\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-chip mrg-c-fmri\" data-cat=\"fmri\" aria-pressed=\"false\"><span class=\"mrg-dot\"><\/span>Functional MRI <span class=\"mrg-n\">32<\/span><\/button>\n\t\t\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-chip mrg-c-donanim\" data-cat=\"donanim\" aria-pressed=\"false\"><span class=\"mrg-dot\"><\/span>Hardware <span class=\"mrg-n\">10<\/span><\/button>\n\t\t\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-chip mrg-c-guvenlik\" data-cat=\"guvenlik\" aria-pressed=\"false\"><span class=\"mrg-dot\"><\/span>Safety <span class=\"mrg-n\">11<\/span><\/button>\n\t\t\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-chip mrg-c-artefakt\" data-cat=\"artefakt\" aria-pressed=\"false\"><span class=\"mrg-dot\"><\/span>Artifacts <span class=\"mrg-n\">7<\/span><\/button>\n\t\t\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-chip mrg-c-yz\" data-cat=\"yz\" aria-pressed=\"false\"><span class=\"mrg-dot\"><\/span>Artificial intelligence <span class=\"mrg-n\">59<\/span><\/button>\n\t\t\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-chip mrg-c-difuzyon\" data-cat=\"difuzyon\" aria-pressed=\"false\"><span class=\"mrg-dot\"><\/span>Diffusion MRI and analysis <span class=\"mrg-n\">19<\/span><\/button>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t<p class=\"mrg-status\" aria-live=\"polite\"><\/p>\n\t\t<div class=\"mrg-body\">\n\t\t\t<div class=\"mrg-list\">\n\t\t\t\t<h3 class=\"mrg-letter\" id=\"harf-%23\">#<\/h3>\t\t\t\t\t<article class=\"mrg-entry\" id=\"3b-goruntuleme\" data-c=\"goruntu\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#3b-goruntuleme\">3D imaging<\/a><\/h2><span class=\"mrg-abbr\">3B, 3D<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> \u00dc\u00e7 boyutlu g\u00f6r\u00fcnt\u00fcleme<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Imaging in which a whole volume is excited and encoded with phase encoding in two directions, instead of slice by slice. It gives thin, gap-free slices and high SNR, MPRAGE being an example.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-goruntu\" data-cat=\"goruntu\"><span class=\"mrg-dot\"><\/span>Image formation<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#mprage\" data-go=\"mprage\">MPRAGE<\/a><a href=\"#faz-kodlama\" data-go=\"faz-kodlama\">Phase encoding<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t<h3 class=\"mrg-letter\" id=\"harf-A\">A<\/h3>\t\t\t\t\t<article class=\"mrg-entry\" id=\"akustik-gurultu\" data-c=\"guvenlik\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#akustik-gurultu\">Acoustic noise<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Akustik g\u00fcr\u00fclt\u00fc<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Loud sound from vibrating gradient coils. It can exceed 100 dB, so hearing protection is used in every scan.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-guvenlik\" data-cat=\"guvenlik\"><span class=\"mrg-dot\"><\/span>Safety<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#gradyan-bobini\" data-go=\"gradyan-bobini\">Gradient coil<\/a><a href=\"#epi\" data-go=\"epi\">Echo planar imaging<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"katlanma-artefakti\" data-c=\"artefakt\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#katlanma-artefakti\">Aliasing (wrap-around)<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Katlanma artefakt\u0131<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Tissue outside the field of view folding onto the opposite side of the image. It usually happens along the phase-encoding direction. A larger field of view or oversampling fixes it.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-artefakt\" data-cat=\"artefakt\"><span class=\"mrg-dot\"><\/span>Artifacts<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#fov\" data-go=\"fov\">Field of view<\/a><a href=\"#faz-kodlama\" data-go=\"faz-kodlama\">Phase encoding<\/a><a href=\"#sense\" data-go=\"sense\">SENSE<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"anomali-tespiti\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#anomali-tespiti\">Anomaly detection<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Anomali tespiti<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A model trained only on normal images flagging regions that depart from that norm. It is used to find lesions when labelled patient data are scarce.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#normatif-model\" data-go=\"normatif-model\">Normative modelling<\/a><a href=\"#uretken-model\" data-go=\"uretken-model\">Generative model<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"adc\" data-c=\"difuzyon\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#adc\">Apparent diffusion coefficient<\/a><\/h2><span class=\"mrg-abbr\">ADC<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> G\u00f6r\u00fcn\u00fcr dif\u00fczyon katsay\u0131s\u0131<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A quantitative measure of how fast water diffuses in tissue, in mm\u00b2\/s. In acute stroke diffusion is restricted and ADC drops.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-difuzyon\" data-cat=\"difuzyon\"><span class=\"mrg-dot\"><\/span>Diffusion MRI and analysis<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#dwi\" data-go=\"dwi\">Diffusion-weighted imaging<\/a><a href=\"#b-degeri\" data-go=\"b-degeri\">b-value<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"asl\" data-c=\"sekans\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#asl\">Arterial spin labelling<\/a><\/h2><span class=\"mrg-abbr\">ASL<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Arteriyel spin etiketleme<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A perfusion method that magnetically labels water in arterial blood and measures it once it reaches tissue. It quantifies cerebral blood flow without a contrast agent.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-sekans\" data-cat=\"sekans\"><span class=\"mrg-dot\"><\/span>Sequences and contrast<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#gadolinyum\" data-go=\"gadolinyum\">Gadolinium-based contrast agent<\/a><a href=\"#bold\" data-go=\"bold\">Blood oxygenation level dependent signal<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"yapay-zeka-mr\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#yapay-zeka-mr\">Artificial intelligence<\/a><\/h2><span class=\"mrg-abbr\">YZ, AI<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Yapay zeka<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">The broad name for computer methods that learn patterns from data to make decisions or produce outputs. In MRI it is used to speed up scans, improve image quality, segment structures and support diagnosis.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#derin-ogrenme\" data-go=\"derin-ogrenme\">Deep learning<\/a><a href=\"#derin-ogrenme-geri-catim\" data-go=\"derin-ogrenme-geri-catim\">Deep learning reconstruction<\/a><a href=\"#bolutleme\" data-go=\"bolutleme\">Segmentation<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"atlas\" data-c=\"fmri\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#atlas\">Atlas and parcellation<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Atlas ve parselasyon<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A ready-made map that divides the brain into anatomical or functional regions. It lets data in a common space be summarised by region and connectivity networks be built.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-fmri\" data-cat=\"fmri\"><span class=\"mrg-dot\"><\/span>Functional MRI<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#uzamsal-normalizasyon\" data-go=\"uzamsal-normalizasyon\">Spatial normalisation<\/a><a href=\"#ilgi-bolgesi\" data-go=\"ilgi-bolgesi\">Region of interest<\/a><a href=\"#fonksiyonel-baglantisallik\" data-go=\"fonksiyonel-baglantisallik\">Functional connectivity<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"dikkat-mekanizmasi\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#dikkat-mekanizmasi\">Attention mechanism<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Dikkat mekanizmas\u0131<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A way for a network to learn which parts of the input matter most for each output and weight them accordingly. Its cost grows with the square of the input length, so it can be expensive for high-resolution images.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#donusturucu\" data-go=\"donusturucu\">Transformer<\/a><a href=\"#durum-uzayi-modeli\" data-go=\"durum-uzayi-modeli\">State-space model<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t<h3 class=\"mrg-letter\" id=\"harf-B\">B<\/h3>\t\t\t\t\t<article class=\"mrg-entry\" id=\"b-degeri\" data-c=\"difuzyon\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#b-degeri\">b-value<\/a><\/h2><span class=\"mrg-abbr\">b<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> b de\u011feri<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">The strength of diffusion weighting, in s\/mm\u00b2. It depends on gradient strength, duration and spacing. A b = 0 image has no diffusion weighting. Around 1000 s\/mm\u00b2 is common in brain imaging.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-difuzyon\" data-cat=\"difuzyon\"><span class=\"mrg-dot\"><\/span>Diffusion MRI and analysis<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#dwi\" data-go=\"dwi\">Diffusion-weighted imaging<\/a><a href=\"#b-vektoru\" data-go=\"b-vektoru\">Diffusion directions (b-vectors)<\/a><a href=\"#cok-kabuklu\" data-go=\"cok-kabuklu\">Multi-shell diffusion<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"bids\" data-c=\"fmri\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#bids\">BIDS<\/a><\/h2><span class=\"mrg-abbr\">BIDS<\/span><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<p class=\"mrg-def\">A set of rules for organising brain imaging data with standard file names and folders (Brain Imaging Data Structure). It makes data easier to share and to process with automated pipelines.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-fmri\" data-cat=\"fmri\"><span class=\"mrg-dot\"><\/span>Functional MRI<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#on-isleme\" data-go=\"on-isleme\">Preprocessing<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"blok-tasarim\" data-c=\"fmri\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#blok-tasarim\">Block design<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Blok tasar\u0131m<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">An experimental layout in which conditions alternate in blocks, typically of 15\u201330 seconds. It has high statistical power.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-fmri\" data-cat=\"fmri\"><span class=\"mrg-dot\"><\/span>Functional MRI<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#olay-iliskili-tasarim\" data-go=\"olay-iliskili-tasarim\">Event-related design<\/a><a href=\"#glm\" data-go=\"glm\">General linear model<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"bold\" data-c=\"fmri\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#bold\">Blood oxygenation level dependent signal<\/a><\/h2><span class=\"mrg-abbr\">BOLD<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Kan oksijenlenme d\u00fczeyine ba\u011fl\u0131 sinyal<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Deoxyhaemoglobin is paramagnetic and lowers T2* signal. Blood flow to an active region rises more than oxygen use, so the deoxyhaemoglobin fraction falls and the signal rises by a few percent.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-fmri\" data-cat=\"fmri\"><span class=\"mrg-dot\"><\/span>Functional MRI<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#t2-yildiz\" data-go=\"t2-yildiz\">T2* relaxation<\/a><a href=\"#hrf\" data-go=\"hrf\">Haemodynamic response function<\/a><a href=\"#fmrg\" data-go=\"fmrg\">Functional MRI<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"beyin-yasi\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#beyin-yasi\">Brain age prediction<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Beyin ya\u015f\u0131 tahmini<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A model trained on MRI scans of healthy people predicting the apparent age of a new person&#039;s brain. The gap between predicted and chronological age is studied as a marker of ageing and disease.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#regresyon-ml\" data-go=\"regresyon-ml\">Regression<\/a><a href=\"#normatif-model\" data-go=\"normatif-model\">Normative modelling<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t<h3 class=\"mrg-letter\" id=\"harf-C\">C<\/h3>\t\t\t\t\t<article class=\"mrg-entry\" id=\"tetikleme\" data-c=\"goruntu\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#tetikleme\">Cardiac and respiratory gating<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Kardiyak ve solunum tetikleme<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Synchronising data acquisition with the heartbeat or breathing, using ECG, a pulse sensor, a respiratory belt or navigator signals. It reduces motion artifacts.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-goruntu\" data-cat=\"goruntu\"><span class=\"mrg-dot\"><\/span>Image formation<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#hareket-artefakti\" data-go=\"hareket-artefakti\">Motion artifact<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"kimyasal-kayma\" data-c=\"fizik\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#kimyasal-kayma\">Chemical shift<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Kimyasal kayma<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">The slightly different resonance frequency of the same nucleus in different molecules. Water and fat protons differ by about 3.4 ppm, roughly 440 Hz at 3 T.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-fizik\" data-cat=\"fizik\"><span class=\"mrg-dot\"><\/span>Basic physics<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#kimyasal-kayma-artefakti\" data-go=\"kimyasal-kayma-artefakti\">Chemical shift artifact<\/a><a href=\"#mr-spektroskopi\" data-go=\"mr-spektroskopi\">MR spectroscopy<\/a><a href=\"#stir\" data-go=\"stir\">Short tau inversion recovery<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"kimyasal-kayma-artefakti\" data-c=\"artefakt\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#kimyasal-kayma-artefakti\">Chemical shift artifact<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Kimyasal kayma artefakt\u0131<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Fat signal shifting along the readout direction because of the fat-water frequency difference. Bright and dark bands appear at tissue boundaries. Raising the bandwidth reduces it.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-artefakt\" data-cat=\"artefakt\"><span class=\"mrg-dot\"><\/span>Artifacts<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#kimyasal-kayma\" data-go=\"kimyasal-kayma\">Chemical shift<\/a><a href=\"#bant-genisligi\" data-go=\"bant-genisligi\">Receiver bandwidth<\/a><a href=\"#frekans-kodlama\" data-go=\"frekans-kodlama\">Frequency encoding<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"sinif-dengesizligi\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#sinif-dengesizligi\">Class imbalance<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> S\u0131n\u0131f dengesizli\u011fi<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">One class having far fewer examples than the others, as with a rare disease. Accuracy becomes misleading, and weighting, resampling and balanced metrics are needed.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#siniflandirma\" data-go=\"siniflandirma\">Classification<\/a><a href=\"#roc-auc\" data-go=\"roc-auc\">ROC curve and AUC<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"siniflandirma\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#siniflandirma\">Classification<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> S\u0131n\u0131fland\u0131rma<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A task that assigns an image or a person to one of several predefined classes, such as patient versus healthy control. Performance is measured with accuracy, sensitivity, specificity and AUC.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#roc-auc\" data-go=\"roc-auc\">ROC curve and AUC<\/a><a href=\"#tanisal-olcutler\" data-go=\"tanisal-olcutler\">Diagnostic sensitivity and specificity<\/a><a href=\"#capraz-dogrulama\" data-go=\"capraz-dogrulama\">Cross-validation<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"kume-cikarimi\" data-c=\"fmri\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#kume-cikarimi\">Cluster-based inference<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> K\u00fcme tabanl\u0131 \u00e7\u0131kar\u0131m<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Applying a voxel threshold first and then declaring large enough clusters of adjacent voxels significant. A lenient initial threshold inflates the false-positive rate.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-fmri\" data-cat=\"fmri\"><span class=\"mrg-dot\"><\/span>Functional MRI<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#coklu-karsilastirma\" data-go=\"coklu-karsilastirma\">Multiple comparisons correction<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"bobin-birlestirme\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#bobin-birlestirme\">Coil combination<\/a><\/h2><span class=\"mrg-abbr\">RSS<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Bobin birle\u015ftirme<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Merging the images from each channel of a multichannel receiver into one image. The simplest method is the root sum of squares (RSS). Combination with coil sensitivity maps preserves phase.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#alici-bobin\" data-go=\"alici-bobin\">Receive array coil<\/a><a href=\"#sense\" data-go=\"sense\">SENSE<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"sikistirilmis-algilama\" data-c=\"hizlandirma\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#sikistirilmis-algilama\">Compressed sensing<\/a><\/h2><span class=\"mrg-abbr\">CS<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> S\u0131k\u0131\u015ft\u0131r\u0131lm\u0131\u015f alg\u0131lama<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A method that exploits the sparsity of the image in a transform domain to reconstruct, iteratively, from incoherently undersampled data.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-hizlandirma\" data-cat=\"hizlandirma\"><span class=\"mrg-dot\"><\/span>Acceleration and reconstruction<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#geri-catim\" data-go=\"geri-catim\">Image reconstruction<\/a><a href=\"#k-uzayi\" data-go=\"k-uzayi\">k-space<\/a><a href=\"#derin-ogrenme-geri-catim\" data-go=\"derin-ogrenme-geri-catim\">Deep learning reconstruction<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"csd\" data-c=\"difuzyon\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#csd\">Constrained spherical deconvolution<\/a><\/h2><span class=\"mrg-abbr\">CSD<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> K\u0131s\u0131tl\u0131 k\u00fcresel ters evri\u015fim<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A method that deconvolves the diffusion signal with the response of a single fibre bundle to estimate the fibre orientation distribution (FOD) in each voxel. It can resolve crossing fibres and is a basis for tractography.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-difuzyon\" data-cat=\"difuzyon\"><span class=\"mrg-dot\"><\/span>Diffusion MRI and analysis<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#kesisen-lifler\" data-go=\"kesisen-lifler\">Crossing fibres<\/a><a href=\"#traktografi\" data-go=\"traktografi\">Tractography<\/a><a href=\"#hardi\" data-go=\"hardi\">High angular resolution diffusion imaging<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"cnr\" data-c=\"goruntu\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#cnr\">Contrast-to-noise ratio<\/a><\/h2><span class=\"mrg-abbr\">CNR<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Kontrast-g\u00fcr\u00fclt\u00fc oran\u0131<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">The signal difference between two tissues divided by the noise. It measures how well a structure stands out from its surroundings.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-goruntu\" data-cat=\"goruntu\"><span class=\"mrg-dot\"><\/span>Image formation<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#snr\" data-go=\"snr\">Signal-to-noise ratio<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"cnn\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#cnn\">Convolutional neural network<\/a><\/h2><span class=\"mrg-abbr\">CNN<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Evri\u015fimli sinir a\u011f\u0131<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A neural network that learns local features with small filters sliding over the image. It has long been the standard architecture for image tasks such as denoising, reconstruction and segmentation.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#u-net\" data-go=\"u-net\">U-Net<\/a><a href=\"#derin-ogrenme\" data-go=\"derin-ogrenme\">Deep learning<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"capraz-dogrulama\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#capraz-dogrulama\">Cross-validation<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> \u00c7apraz do\u011frulama<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Splitting data into folds and repeatedly training and testing the model, holding out a different fold each time. If model settings are also being tuned, nested cross-validation should be used.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#egitim-test\" data-go=\"egitim-test\">Training, validation and test sets<\/a><a href=\"#veri-sizintisi\" data-go=\"veri-sizintisi\">Data leakage<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"kesisen-lifler\" data-c=\"difuzyon\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#kesisen-lifler\">Crossing fibres<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Kesi\u015fen lifler<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">More than one fibre bundle running in different directions within a voxel. It occurs in most white-matter voxels and is the main limitation of the single-direction tensor model.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-difuzyon\" data-cat=\"difuzyon\"><span class=\"mrg-dot\"><\/span>Diffusion MRI and analysis<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#csd\" data-go=\"csd\">Constrained spherical deconvolution<\/a><a href=\"#fa\" data-go=\"fa\">Fractional anisotropy<\/a><a href=\"#traktografi\" data-go=\"traktografi\">Tractography<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t<h3 class=\"mrg-letter\" id=\"harf-D\">D<\/h3>\t\t\t\t\t<article class=\"mrg-entry\" id=\"veri-artirma\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#veri-artirma\">Data augmentation<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Veri art\u0131rma<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Varying training images with rotations, shifts, contrast changes and similar operations so the model learns more general features.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#asiri-ogrenme\" data-go=\"asiri-ogrenme\">Overfitting<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"veri-tutarliligi\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#veri-tutarliligi\">Data consistency<\/a><\/h2><span class=\"mrg-abbr\">DC<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Veri tutarl\u0131l\u0131\u011f\u0131<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A step that keeps the network&#039;s output consistent with the k-space data that were actually measured. Measured points are kept, and the network only fills in what is missing. It reduces the risk of hallucination.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#fizik-gudumlu-ag\" data-go=\"fizik-gudumlu-ag\">Physics-guided network<\/a><a href=\"#k-uzayi\" data-go=\"k-uzayi\">k-space<\/a><a href=\"#halusinasyon\" data-go=\"halusinasyon\">Hallucination<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"veri-sizintisi\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#veri-sizintisi\">Data leakage<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Veri s\u0131z\u0131nt\u0131s\u0131<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Information from the test data entering the training process, for example selecting features on the whole dataset or having the same person&#039;s scans in both training and test sets. It makes performance look far better than it is.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#capraz-dogrulama\" data-go=\"capraz-dogrulama\">Cross-validation<\/a><a href=\"#egitim-test\" data-go=\"egitim-test\">Training, validation and test sets<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"derin-ogrenme\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#derin-ogrenme\">Deep learning<\/a><\/h2><span class=\"mrg-abbr\">DL<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Derin \u00f6\u011frenme<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A machine learning approach that learns directly from data with multi-layer neural networks, without hand-crafted features. Most current MRI applications build on it.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#yapay-zeka-mr\" data-go=\"yapay-zeka-mr\">Artificial intelligence<\/a><a href=\"#cnn\" data-go=\"cnn\">Convolutional neural network<\/a><a href=\"#donusturucu\" data-go=\"donusturucu\">Transformer<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"derin-ogrenme-geri-catim\" data-c=\"hizlandirma\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#derin-ogrenme-geri-catim\">Deep learning reconstruction<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Derin \u00f6\u011frenme tabanl\u0131 geri\u00e7at\u0131m<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Reconstruction that uses trained neural networks to recover images from undersampled or noisy data. Methods that build the physics model into the network tend to be more reliable. Results should be checked for structures the network may hallucinate.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-hizlandirma\" data-cat=\"hizlandirma\"><span class=\"mrg-dot\"><\/span>Acceleration and reconstruction<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#geri-catim\" data-go=\"geri-catim\">Image reconstruction<\/a><a href=\"#sikistirilmis-algilama\" data-go=\"sikistirilmis-algilama\">Compressed sensing<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"gurultu-giderme\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#gurultu-giderme\">Denoising<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> G\u00fcr\u00fclt\u00fc giderme<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Reducing random noise in an image while keeping detail. Learning-based denoising is used to make up for the SNR lost in short or high-resolution scans.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#snr\" data-go=\"snr\">Signal-to-noise ratio<\/a><a href=\"#super-cozunurluk\" data-go=\"super-cozunurluk\">Super-resolution<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"tanisal-olcutler\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#tanisal-olcutler\">Diagnostic sensitivity and specificity<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Tan\u0131sal duyarl\u0131l\u0131k ve \u00f6zg\u00fcll\u00fck<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Sensitivity is the share of truly affected cases that are correctly detected, specificity the share of unaffected cases that are correctly ruled out. Not to be confused with magnetic susceptibility in MRI.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#roc-auc\" data-go=\"roc-auc\">ROC curve and AUC<\/a><a href=\"#siniflandirma\" data-go=\"siniflandirma\">Classification<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"dice\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#dice\">Dice coefficient<\/a><\/h2><span class=\"mrg-abbr\">DSC<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Dice katsay\u0131s\u0131<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A measure of overlap between two segmentations, from 0 to 1, where 1 means perfect overlap. It is used to compare automatic segmentation with expert outlines.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#bolutleme\" data-go=\"bolutleme\">Segmentation<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"mppca\" data-c=\"difuzyon\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#mppca\">Diffusion denoising<\/a><\/h2><span class=\"mrg-abbr\">MP-PCA<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Dif\u00fczyon verisinde g\u00fcr\u00fclt\u00fc giderme<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Reducing noise by exploiting the shared structure across many diffusion images. Marchenko-Pastur principal component analysis (MP-PCA) is a common method and is usually the first preprocessing step.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-difuzyon\" data-cat=\"difuzyon\"><span class=\"mrg-dot\"><\/span>Diffusion MRI and analysis<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#gurultu-giderme\" data-go=\"gurultu-giderme\">Denoising<\/a><a href=\"#on-isleme\" data-go=\"on-isleme\">Preprocessing<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"b-vektoru\" data-c=\"difuzyon\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#b-vektoru\">Diffusion directions (b-vectors)<\/a><\/h2><span class=\"mrg-abbr\">bvec<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Dif\u00fczyon y\u00f6nleri (b vekt\u00f6rleri)<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">The directions along which diffusion gradients are applied. Each diffusion-weighted image corresponds to one direction. A tensor needs at least 6, and many more are used to resolve crossing fibres. Directions must be rotated when images are registered.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-difuzyon\" data-cat=\"difuzyon\"><span class=\"mrg-dot\"><\/span>Diffusion MRI and analysis<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#b-degeri\" data-go=\"b-degeri\">b-value<\/a><a href=\"#dti\" data-go=\"dti\">Diffusion tensor imaging<\/a><a href=\"#hardi\" data-go=\"hardi\">High angular resolution diffusion imaging<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"dki\" data-c=\"difuzyon\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#dki\">Diffusion kurtosis imaging<\/a><\/h2><span class=\"mrg-abbr\">DKI<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Dif\u00fczyon kurtosis g\u00f6r\u00fcnt\u00fcleme<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">An extension of the tensor model that measures how far water diffusion departs from a Gaussian distribution. It is sensitive to microstructural complexity and needs at least two non-zero b-values.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-difuzyon\" data-cat=\"difuzyon\"><span class=\"mrg-dot\"><\/span>Diffusion MRI and analysis<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#cok-kabuklu\" data-go=\"cok-kabuklu\">Multi-shell diffusion<\/a><a href=\"#dti\" data-go=\"dti\">Diffusion tensor imaging<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"difuzyon-modeli\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#difuzyon-modeli\">Diffusion model<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Dif\u00fczyon modeli<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A generative model that learns to reverse a process that adds noise to an image step by step. It produces an image by starting from pure noise and removing it gradually. Quality is high, but the many steps can make it slow. It is unrelated to diffusion-weighted MRI.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#uretken-model\" data-go=\"uretken-model\">Generative model<\/a><a href=\"#gan\" data-go=\"gan\">Generative adversarial network<\/a><a href=\"#dwi\" data-go=\"dwi\">Diffusion-weighted imaging<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"dti\" data-c=\"difuzyon\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#dti\">Diffusion tensor imaging<\/a><\/h2><span class=\"mrg-abbr\">DTI<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Dif\u00fczyon tens\u00f6r g\u00f6r\u00fcnt\u00fcleme<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A method that measures diffusion along many gradient directions and estimates a tensor in each voxel. Fractional anisotropy (FA) maps and white-matter tractography are derived from it.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-difuzyon\" data-cat=\"difuzyon\"><span class=\"mrg-dot\"><\/span>Diffusion MRI and analysis<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#dwi\" data-go=\"dwi\">Diffusion-weighted imaging<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"dwi\" data-c=\"difuzyon\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#dwi\">Diffusion-weighted imaging<\/a><\/h2><span class=\"mrg-abbr\">DWI<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Dif\u00fczyon a\u011f\u0131rl\u0131kl\u0131 g\u00f6r\u00fcnt\u00fcleme<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Imaging that is sensitive to the random motion of water molecules. Strong gradients reduce the signal of moving spins. Sensitivity is set by the b-value (s\/mm\u00b2), and results are also shown as apparent diffusion coefficient (ADC) maps.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-difuzyon\" data-cat=\"difuzyon\"><span class=\"mrg-dot\"><\/span>Diffusion MRI and analysis<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#dti\" data-go=\"dti\">Diffusion tensor imaging<\/a><a href=\"#epi\" data-go=\"epi\">Echo planar imaging<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"dixon\" data-c=\"sekans\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#dixon\">Dixon method<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Dixon y\u00f6ntemi<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A method that uses the phase difference between water and fat to compute separate water and fat images from images taken at a few echo times. It gives robust fat suppression and quantifies fat fraction.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-sekans\" data-cat=\"sekans\"><span class=\"mrg-dot\"><\/span>Sequences and contrast<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#yag-baskilama\" data-go=\"yag-baskilama\">Fat suppression<\/a><a href=\"#kimyasal-kayma\" data-go=\"kimyasal-kayma\">Chemical shift<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"alan-uyarlamasi\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#alan-uyarlamasi\">Domain adaptation<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Alan uyarlamas\u0131<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Methods that adapt a model trained in one domain to another, with few examples or unlabelled data. They matter for models that must work on data from different sites.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#dagilim-kaymasi\" data-go=\"dagilim-kaymasi\">Domain shift<\/a><a href=\"#ince-ayar\" data-go=\"ince-ayar\">Fine-tuning<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"dagilim-kaymasi\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#dagilim-kaymasi\">Domain shift<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Da\u011f\u0131l\u0131m kaymas\u0131<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">The data a model meets differing from its training data, for example another scanner, field strength, sequence or patient group. Performance can drop sharply.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#alan-uyarlamasi\" data-go=\"alan-uyarlamasi\">Domain adaptation<\/a><a href=\"#federe-ogrenme\" data-go=\"federe-ogrenme\">Federated learning<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t<h3 class=\"mrg-letter\" id=\"harf-E\">E<\/h3>\t\t\t\t\t<article class=\"mrg-entry\" id=\"eko\" data-c=\"fizik\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#eko\">Echo<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Eko<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A regrowth of decayed signal at a set time, created by restoring phase coherence. Refocusing uses a 180\u00b0 RF pulse (spin echo) or a reversal of the gradient (gradient echo).<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-fizik\" data-cat=\"fizik\"><span class=\"mrg-dot\"><\/span>Basic physics<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#spin-eko\" data-go=\"spin-eko\">Spin echo<\/a><a href=\"#gradyan-eko\" data-go=\"gradyan-eko\">Gradient echo<\/a><a href=\"#te-zamani\" data-go=\"te-zamani\">Echo time<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"epi\" data-c=\"sekans\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#epi\">Echo planar imaging<\/a><\/h2><span class=\"mrg-abbr\">EPI<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Ekoplanar g\u00f6r\u00fcnt\u00fcleme<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A sequence that fills all or most of k-space rapidly after a single excitation, imaging a slice in well under a second. It underpins fMRI and diffusion imaging but is prone to distortion.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-sekans\" data-cat=\"sekans\"><span class=\"mrg-dot\"><\/span>Sequences and contrast<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#bold\" data-go=\"bold\">Blood oxygenation level dependent signal<\/a><a href=\"#dwi\" data-go=\"dwi\">Diffusion-weighted imaging<\/a><a href=\"#epi-bozulmasi\" data-go=\"epi-bozulmasi\">EPI distortion<\/a><a href=\"#gradyan-eko\" data-go=\"gradyan-eko\">Gradient echo<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"te-zamani\" data-c=\"sekans\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#te-zamani\">Echo time<\/a><\/h2><span class=\"mrg-abbr\">TE<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Eko zaman\u0131<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">The time from excitation to the centre of the echo. A long TE emphasises T2 (or T2*) differences.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-sekans\" data-cat=\"sekans\"><span class=\"mrg-dot\"><\/span>Sequences and contrast<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#tr-zamani\" data-go=\"tr-zamani\">Repetition time<\/a><a href=\"#t2-agirlikli\" data-go=\"t2-agirlikli\">T2-weighted image<\/a><a href=\"#eko\" data-go=\"eko\">Echo<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"girdap-akimi\" data-c=\"difuzyon\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#girdap-akimi\">Eddy current correction<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Girdap ak\u0131m\u0131 d\u00fczeltmesi<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Removing direction-dependent image distortions caused by eddy currents that strong diffusion gradients induce in conductive parts. It is usually done together with head motion correction.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-difuzyon\" data-cat=\"difuzyon\"><span class=\"mrg-dot\"><\/span>Diffusion MRI and analysis<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#bozulma-duzeltmesi\" data-go=\"bozulma-duzeltmesi\">Susceptibility distortion correction<\/a><a href=\"#hareket-duzeltme\" data-go=\"hareket-duzeltme\">Motion correction<\/a><a href=\"#dwi\" data-go=\"dwi\">Diffusion-weighted imaging<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"kodlama-modeli\" data-c=\"fmri\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#kodlama-modeli\">Encoding model<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Kodlama modeli<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A model that predicts each voxel&#039;s response from features of the stimulus or task. With rich stimuli such as natural films or speech it maps which information the brain represents where. It is the reverse of decoding.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-fmri\" data-cat=\"fmri\"><span class=\"mrg-dot\"><\/span>Functional MRI<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#mvpa\" data-go=\"mvpa\">Multivoxel pattern analysis<\/a><a href=\"#rsa\" data-go=\"rsa\">Representational similarity analysis<\/a><a href=\"#glm\" data-go=\"glm\">General linear model<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"epi-bozulmasi\" data-c=\"artefakt\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#epi-bozulmasi\">EPI distortion<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> EPI bozulmas\u0131<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Stretching or compression of EPI images caused by field inhomogeneity and the low bandwidth along the phase-encoding direction. It can be corrected with an extra scan using reversed phase encoding.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-artefakt\" data-cat=\"artefakt\"><span class=\"mrg-dot\"><\/span>Artifacts<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#epi\" data-go=\"epi\">Echo planar imaging<\/a><a href=\"#duyarlilik-artefakti\" data-go=\"duyarlilik-artefakti\">Susceptibility artifact<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"olay-iliskili-tasarim\" data-c=\"fmri\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#olay-iliskili-tasarim\">Event-related design<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Olay ili\u015fkili tasar\u0131m<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">An experimental layout in which brief stimuli are presented one at a time, often with jittered intervals. It lets responses to individual events be separated.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-fmri\" data-cat=\"fmri\"><span class=\"mrg-dot\"><\/span>Functional MRI<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#blok-tasarim\" data-go=\"blok-tasarim\">Block design<\/a><a href=\"#hrf\" data-go=\"hrf\">Haemodynamic response function<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"aciklanabilirlik\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#aciklanabilirlik\">Explainability<\/a><\/h2><span class=\"mrg-abbr\">XAI<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> A\u00e7\u0131klanabilirlik<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Showing which image regions or features an AI model based its decision on. It matters for trust and debugging in clinical use.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#belirsizlik\" data-go=\"belirsizlik\">Uncertainty estimation<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t<h3 class=\"mrg-letter\" id=\"harf-F\">F<\/h3>\t\t\t\t\t<article class=\"mrg-entry\" id=\"yag-baskilama\" data-c=\"sekans\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#yag-baskilama\">Fat suppression<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Ya\u011f bask\u0131lama<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Techniques that reduce fat signal so nearby tissues stand out. Frequency-selective saturation, STIR and Dixon methods are the most common.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-sekans\" data-cat=\"sekans\"><span class=\"mrg-dot\"><\/span>Sequences and contrast<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#stir\" data-go=\"stir\">Short tau inversion recovery<\/a><a href=\"#dixon\" data-go=\"dixon\">Dixon method<\/a><a href=\"#kimyasal-kayma\" data-go=\"kimyasal-kayma\">Chemical shift<\/a><a href=\"#shim\" data-go=\"shim\">Shimming<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"oznitelik\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#oznitelik\">Feature extraction<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> \u00d6znitelik \u00e7\u0131kar\u0131m\u0131<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Deriving numerical descriptors from images for a model to use. Regional volumes, cortical thickness and connectivity values are hand-crafted features. Deep networks learn features directly from data.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#radyomik\" data-go=\"radyomik\">Radiomics<\/a><a href=\"#siniflandirma\" data-go=\"siniflandirma\">Classification<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"federe-ogrenme\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#federe-ogrenme\">Federated learning<\/a><\/h2><span class=\"mrg-abbr\">FL<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Federe \u00f6\u011frenme<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Several institutions training a shared model without exchanging patient data. Each site updates the model on its own data, and only the updates are combined. It allows learning from more varied data while protecting privacy.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#dagilim-kaymasi\" data-go=\"dagilim-kaymasi\">Domain shift<\/a><a href=\"#mahremiyet\" data-go=\"mahremiyet\">Privacy-preserving learning<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"alan-haritasi\" data-c=\"fmri\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#alan-haritasi\">Field map<\/a><\/h2><span class=\"mrg-abbr\">B\u2080 haritas\u0131<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Alan haritas\u0131<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">An extra scan that measures main-field inhomogeneity voxel by voxel. It is used to correct distortion in EPI images. It can also be estimated from a pair of EPI images with reversed phase encoding.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-fmri\" data-cat=\"fmri\"><span class=\"mrg-dot\"><\/span>Functional MRI<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#epi-bozulmasi\" data-go=\"epi-bozulmasi\">EPI distortion<\/a><a href=\"#bozulma-duzeltmesi\" data-go=\"bozulma-duzeltmesi\">Susceptibility distortion correction<\/a><a href=\"#b0\" data-go=\"b0\">Main magnetic field<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"fov\" data-c=\"goruntu\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#fov\">Field of view<\/a><\/h2><span class=\"mrg-abbr\">FOV<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> G\u00f6r\u00fc\u015f alan\u0131<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">The physical size of the imaged region, usually in millimetres. If the object is larger than the field of view, aliasing can occur.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-goruntu\" data-cat=\"goruntu\"><span class=\"mrg-dot\"><\/span>Image formation<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#matris\" data-go=\"matris\">Matrix<\/a><a href=\"#voksel\" data-go=\"voksel\">Voxel<\/a><a href=\"#katlanma-artefakti\" data-go=\"katlanma-artefakti\">Aliasing (wrap-around)<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"ince-ayar\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#ince-ayar\">Fine-tuning<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> \u0130nce ayar<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Adapting a pretrained model to a new task or dataset with further training. Parameter-efficient methods such as LoRA train only a small set of added parameters, cutting compute and memory needs.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#temel-model\" data-go=\"temel-model\">Foundation model<\/a><a href=\"#alan-uyarlamasi\" data-go=\"alan-uyarlamasi\">Domain adaptation<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"cevirme-acisi\" data-c=\"fizik\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#cevirme-acisi\">Flip angle<\/a><\/h2><span class=\"mrg-abbr\">FA, \u03b1<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> \u00c7evirme a\u00e7\u0131s\u0131<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">How far an RF pulse tips the net magnetization away from B\u2080. A 90\u00b0 pulse moves it fully into the transverse plane. Gradient echo sequences usually use smaller angles.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-fizik\" data-cat=\"fizik\"><span class=\"mrg-dot\"><\/span>Basic physics<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#rf-darbesi\" data-go=\"rf-darbesi\">RF pulse<\/a><a href=\"#gradyan-eko\" data-go=\"gradyan-eko\">Gradient echo<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"flair\" data-c=\"sekans\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#flair\">Fluid-attenuated inversion recovery<\/a><\/h2><span class=\"mrg-abbr\">FLAIR<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> S\u0131v\u0131 bask\u0131l\u0131 evirme geri kazan\u0131m\u0131<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A T2-weighted sequence whose inversion time nulls the cerebrospinal fluid signal. Lesions next to the ventricles become easier to see.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-sekans\" data-cat=\"sekans\"><span class=\"mrg-dot\"><\/span>Sequences and contrast<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#evirme-geri-kazanim\" data-go=\"evirme-geri-kazanim\">Inversion recovery<\/a><a href=\"#ti-zamani\" data-go=\"ti-zamani\">Inversion time<\/a><a href=\"#t2-agirlikli\" data-go=\"t2-agirlikli\">T2-weighted image<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"ileri-model\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#ileri-model\">Forward model<\/a><\/h2><span class=\"mrg-abbr\">kodlama operat\u00f6r\u00fc<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> \u0130leri model<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">The mathematical model of the physical process from image to measured data. In MRI it consists of coil sensitivities, the Fourier transform and the sampling mask. Physics-guided networks and data consistency rely on it.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#ters-problem\" data-go=\"ters-problem\">Inverse problem<\/a><a href=\"#veri-tutarliligi\" data-go=\"veri-tutarliligi\">Data consistency<\/a><a href=\"#fizik-gudumlu-ag\" data-go=\"fizik-gudumlu-ag\">Physics-guided network<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"temel-model\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#temel-model\">Foundation model<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Temel model<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A large model pretrained on vast and varied data that can be adapted to many tasks with little data. Such models are now being developed for medical imaging too.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#ince-ayar\" data-go=\"ince-ayar\">Fine-tuning<\/a><a href=\"#gorme-dil-modeli\" data-go=\"gorme-dil-modeli\">Vision-language model<\/a><a href=\"#donusturucu\" data-go=\"donusturucu\">Transformer<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"fourier-donusumu\" data-c=\"goruntu\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#fourier-donusumu\">Fourier transform<\/a><\/h2><span class=\"mrg-abbr\">FT<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Fourier d\u00f6n\u00fc\u015f\u00fcm\u00fc<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A mathematical operation that splits a signal into its frequency components. In MR the inverse Fourier transform turns k-space data into an image.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-goruntu\" data-cat=\"goruntu\"><span class=\"mrg-dot\"><\/span>Image formation<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#k-uzayi\" data-go=\"k-uzayi\">k-space<\/a><a href=\"#geri-catim\" data-go=\"geri-catim\">Image reconstruction<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"fa\" data-c=\"difuzyon\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#fa\">Fractional anisotropy<\/a><\/h2><span class=\"mrg-abbr\">FA<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Fraksiyonel anizotropi<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A measure from 0 to 1 of how strongly diffusion favours one direction. It is high in coherent white-matter bundles. It can be low where fibres cross, even in healthy tissue.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-difuzyon\" data-cat=\"difuzyon\"><span class=\"mrg-dot\"><\/span>Diffusion MRI and analysis<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#dti\" data-go=\"dti\">Diffusion tensor imaging<\/a><a href=\"#difuzivite\" data-go=\"difuzivite\">Mean, axial and radial diffusivity<\/a><a href=\"#kesisen-lifler\" data-go=\"kesisen-lifler\">Crossing fibres<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"cerceve-yer-degistirmesi\" data-c=\"fmri\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#cerceve-yer-degistirmesi\">Framewise displacement<\/a><\/h2><span class=\"mrg-abbr\">FD<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> \u00c7er\u00e7eve yer de\u011fi\u015ftirmesi<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A single-number summary of head motion between consecutive volumes, in millimetres. Volumes above a threshold can be removed from analysis (scrubbing) or flagged in the model.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-fmri\" data-cat=\"fmri\"><span class=\"mrg-dot\"><\/span>Functional MRI<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#hareket-duzeltme\" data-go=\"hareket-duzeltme\">Motion correction<\/a><a href=\"#gurultu-regresorleri\" data-go=\"gurultu-regresorleri\">Nuisance regressors<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"fid\" data-c=\"fizik\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#fid\">Free induction decay<\/a><\/h2><span class=\"mrg-abbr\">FID<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Serbest ind\u00fcksiyon s\u00f6n\u00fcm\u00fc<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">The decaying signal picked up by the receive coil right after excitation. With no refocusing it decays with T2*.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-fizik\" data-cat=\"fizik\"><span class=\"mrg-dot\"><\/span>Basic physics<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#eko\" data-go=\"eko\">Echo<\/a><a href=\"#t2-yildiz\" data-go=\"t2-yildiz\">T2* relaxation<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"frekans-kodlama\" data-c=\"goruntu\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#frekans-kodlama\">Frequency encoding<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Frekans kodlama<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Keeping a gradient on while the signal is read so that position maps to frequency. This direction is also called the readout direction.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-goruntu\" data-cat=\"goruntu\"><span class=\"mrg-dot\"><\/span>Image formation<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#faz-kodlama\" data-go=\"faz-kodlama\">Phase encoding<\/a><a href=\"#k-uzayi\" data-go=\"k-uzayi\">k-space<\/a><a href=\"#bant-genisligi\" data-go=\"bant-genisligi\">Receiver bandwidth<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"sacak-alan\" data-c=\"donanim\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#sacak-alan\">Fringe field<\/a><\/h2><span class=\"mrg-abbr\">5 gauss \u00e7izgisi<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Sa\u00e7ak alan<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">The magnetic field that extends outside the magnet. People with active implants such as pacemakers are kept outside the 0.5 mT (5 gauss) line.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-donanim\" data-cat=\"donanim\"><span class=\"mrg-dot\"><\/span>Hardware<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#b0\" data-go=\"b0\">Main magnetic field<\/a><a href=\"#guvenlik-bolgeleri\" data-go=\"guvenlik-bolgeleri\">MR safety zones<\/a><a href=\"#mr-kosullu\" data-go=\"mr-kosullu\">MR Safe, MR Conditional, MR Unsafe<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"fonksiyonel-baglantisallik\" data-c=\"fmri\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#fonksiyonel-baglantisallik\">Functional connectivity<\/a><\/h2><span class=\"mrg-abbr\">FC<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Fonksiyonel ba\u011flant\u0131sall\u0131k<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">The temporal relation between signals from different brain regions, usually measured as correlation. It shows regions working together, not direct anatomical links.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-fmri\" data-cat=\"fmri\"><span class=\"mrg-dot\"><\/span>Functional MRI<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#dinlenim-fmrg\" data-go=\"dinlenim-fmrg\">Resting-state fMRI<\/a><a href=\"#dti\" data-go=\"dti\">Diffusion tensor imaging<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"fmrg\" data-c=\"fmri\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#fmrg\">Functional MRI<\/a><\/h2><span class=\"mrg-abbr\">fMRG, fMRI<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Fonksiyonel MRG<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">MR imaging that tracks activity-related changes in blood oxygenation over time. It mostly uses BOLD contrast and EPI.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-fmri\" data-cat=\"fmri\"><span class=\"mrg-dot\"><\/span>Functional MRI<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#bold\" data-go=\"bold\">Blood oxygenation level dependent signal<\/a><a href=\"#hrf\" data-go=\"hrf\">Haemodynamic response function<\/a><a href=\"#epi\" data-go=\"epi\">Echo planar imaging<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t<h3 class=\"mrg-letter\" id=\"harf-G\">G<\/h3>\t\t\t\t\t<article class=\"mrg-entry\" id=\"gadolinyum\" data-c=\"guvenlik\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#gadolinyum\">Gadolinium-based contrast agent<\/a><\/h2><span class=\"mrg-abbr\">GBCA<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Gadolinyum bazl\u0131 kontrast madde<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">An intravenous contrast agent that shortens T1 and makes the tissue it reaches bright on T1-weighted images. Its use needs care in people with poor kidney function and in pregnancy.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-guvenlik\" data-cat=\"guvenlik\"><span class=\"mrg-dot\"><\/span>Safety<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#t1\" data-go=\"t1\">T1 relaxation<\/a><a href=\"#t1-agirlikli\" data-go=\"t1-agirlikli\">T1-weighted image<\/a><a href=\"#asl\" data-go=\"asl\">Arterial spin labelling<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"glm\" data-c=\"fmri\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#glm\">General linear model<\/a><\/h2><span class=\"mrg-abbr\">GLM<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Genel do\u011frusal model<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A statistical framework that explains each voxel&#039;s time series as a linear sum of the task conditions convolved with the HRF. Activation maps come from the model&#039;s coefficients.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-fmri\" data-cat=\"fmri\"><span class=\"mrg-dot\"><\/span>Functional MRI<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#hrf\" data-go=\"hrf\">Haemodynamic response function<\/a><a href=\"#blok-tasarim\" data-go=\"blok-tasarim\">Block design<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"gan\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#gan\">Generative adversarial network<\/a><\/h2><span class=\"mrg-abbr\">GAN<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> \u00c7eki\u015fmeli \u00fcretici a\u011f<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A generative model made of two networks trained against each other: a generator that tries to make realistic images and a discriminator that tries to tell real from generated. It gives sharp images, but training can be unstable.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#uretken-model\" data-go=\"uretken-model\">Generative model<\/a><a href=\"#difuzyon-modeli\" data-go=\"difuzyon-modeli\">Diffusion model<\/a><a href=\"#goruntu-sentezi\" data-go=\"goruntu-sentezi\">Image synthesis<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"uretken-model\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#uretken-model\">Generative model<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> \u00dcretken model<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A model that learns the probability distribution of data and can draw new samples from it. In MRI it is used to synthesise missing contrasts, recover images from undersampled data and augment training sets.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#gan\" data-go=\"gan\">Generative adversarial network<\/a><a href=\"#difuzyon-modeli\" data-go=\"difuzyon-modeli\">Diffusion model<\/a><a href=\"#goruntu-sentezi\" data-go=\"goruntu-sentezi\">Image synthesis<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"uretken-onsel\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#uretken-onsel\">Generative prior reconstruction<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> \u00dcretken \u00f6nselli geri\u00e7at\u0131m<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Guiding a generative model that has learned the image distribution, usually a diffusion model, to produce images consistent with the measured data. It adapts flexibly to acquisition settings, but can be slow and needs care against hallucination.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#difuzyon-modeli\" data-go=\"difuzyon-modeli\">Diffusion model<\/a><a href=\"#veri-tutarliligi\" data-go=\"veri-tutarliligi\">Data consistency<\/a><a href=\"#halusinasyon\" data-go=\"halusinasyon\">Hallucination<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"gibbs-artefakti\" data-c=\"artefakt\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#gibbs-artefakti\">Gibbs ringing<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Gibbs halkalanmas\u0131<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Fine parallel lines next to sharp signal edges. They come from truncating k-space. A larger matrix or filtering reduces them.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-artefakt\" data-cat=\"artefakt\"><span class=\"mrg-dot\"><\/span>Artifacts<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#k-uzayi\" data-go=\"k-uzayi\">k-space<\/a><a href=\"#matris\" data-go=\"matris\">Matrix<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"gradyan\" data-c=\"goruntu\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#gradyan\">Gradient<\/a><\/h2><span class=\"mrg-abbr\">G<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Gradyan<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">An added field that changes magnetic field strength linearly along one direction. It encodes where the signal comes from. Strength is given in mT\/m and slew rate in T\/m\/s.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-goruntu\" data-cat=\"goruntu\"><span class=\"mrg-dot\"><\/span>Image formation<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#gradyan-bobini\" data-go=\"gradyan-bobini\">Gradient coil<\/a><a href=\"#kesit-secimi\" data-go=\"kesit-secimi\">Slice selection<\/a><a href=\"#frekans-kodlama\" data-go=\"frekans-kodlama\">Frequency encoding<\/a><a href=\"#faz-kodlama\" data-go=\"faz-kodlama\">Phase encoding<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"gradyan-bobini\" data-c=\"donanim\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#gradyan-bobini\">Gradient coil<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Gradyan bobini<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">The set of coils that produces gradient fields along x, y and z. Rapid current switching causes the loud scanner noise and can cause peripheral nerve stimulation.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-donanim\" data-cat=\"donanim\"><span class=\"mrg-dot\"><\/span>Hardware<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#gradyan\" data-go=\"gradyan\">Gradient<\/a><a href=\"#akustik-gurultu\" data-go=\"akustik-gurultu\">Acoustic noise<\/a><a href=\"#periferik-sinir-uyarimi\" data-go=\"periferik-sinir-uyarimi\">Peripheral nerve stimulation<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"gradyan-eko\" data-c=\"sekans\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#gradyan-eko\">Gradient echo<\/a><\/h2><span class=\"mrg-abbr\">GRE<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Gradyan eko<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A sequence that forms the echo by reversing a gradient instead of applying a 180\u00b0 pulse. With a small flip angle and short TR it is fast. Its contrast is sensitive to T2*.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-sekans\" data-cat=\"sekans\"><span class=\"mrg-dot\"><\/span>Sequences and contrast<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#t2-yildiz\" data-go=\"t2-yildiz\">T2* relaxation<\/a><a href=\"#cevirme-acisi\" data-go=\"cevirme-acisi\">Flip angle<\/a><a href=\"#epi\" data-go=\"epi\">Echo planar imaging<\/a><a href=\"#mprage\" data-go=\"mprage\">MPRAGE<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"cizge-sinir-agi\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#cizge-sinir-agi\">Graph neural network<\/a><\/h2><span class=\"mrg-abbr\">GNN<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> \u00c7izge sinir a\u011f\u0131<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A neural network that works on graph data made of nodes and edges. It is used to make predictions from connectome data, treating brain regions as nodes and connectivity values as edges.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#yapisal-baglantisallik\" data-go=\"yapisal-baglantisallik\">Structural connectivity<\/a><a href=\"#fonksiyonel-baglantisallik\" data-go=\"fonksiyonel-baglantisallik\">Functional connectivity<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"grappa\" data-c=\"hizlandirma\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#grappa\">GRAPPA<\/a><\/h2><span class=\"mrg-abbr\">GRAPPA<\/span><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<p class=\"mrg-def\">A parallel imaging method that fills missing k-space lines from neighbouring lines, using weights learned from a fully sampled calibration region.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-hizlandirma\" data-cat=\"hizlandirma\"><span class=\"mrg-dot\"><\/span>Acceleration and reconstruction<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#paralel-goruntuleme\" data-go=\"paralel-goruntuleme\">Parallel imaging<\/a><a href=\"#sense\" data-go=\"sense\">SENSE<\/a><a href=\"#k-uzayi\" data-go=\"k-uzayi\">k-space<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"grup-analizi\" data-c=\"fmri\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#grup-analizi\">Group-level analysis<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Grup analizi<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Combining individual contrast maps in a second-level model. Mixed-effects models that account for between-subject variability let results generalise to the population.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-fmri\" data-cat=\"fmri\"><span class=\"mrg-dot\"><\/span>Functional MRI<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#glm\" data-go=\"glm\">General linear model<\/a><a href=\"#kontrast-istatistik\" data-go=\"kontrast-istatistik\">Statistical contrast<\/a><a href=\"#uzamsal-normalizasyon\" data-go=\"uzamsal-normalizasyon\">Spatial normalisation<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"jiromanyetik-oran\" data-c=\"fizik\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#jiromanyetik-oran\">Gyromagnetic ratio<\/a><\/h2><span class=\"mrg-abbr\">\u03b3<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Jiromanyetik oran<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">The constant linking a nucleus&#039;s resonance frequency to field strength. For hydrogen, \u03b3\/2\u03c0 is about 42.58 MHz\/T. Each nucleus has its own value, so the scanner is tuned to a specific nucleus.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-fizik\" data-cat=\"fizik\"><span class=\"mrg-dot\"><\/span>Basic physics<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#larmor-frekansi\" data-go=\"larmor-frekansi\">Larmor frequency<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t<h3 class=\"mrg-letter\" id=\"harf-H\">H<\/h3>\t\t\t\t\t<article class=\"mrg-entry\" id=\"hrf\" data-c=\"fmri\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#hrf\">Haemodynamic response function<\/a><\/h2><span class=\"mrg-abbr\">HRF<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Hemodinamik yan\u0131t fonksiyonu<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">The time course of the BOLD signal after a brief neural event. It peaks at about 5\u20136 seconds, then returns to baseline after a brief undershoot.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-fmri\" data-cat=\"fmri\"><span class=\"mrg-dot\"><\/span>Functional MRI<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#bold\" data-go=\"bold\">Blood oxygenation level dependent signal<\/a><a href=\"#olay-iliskili-tasarim\" data-go=\"olay-iliskili-tasarim\">Event-related design<\/a><a href=\"#glm\" data-go=\"glm\">General linear model<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"halusinasyon\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#halusinasyon\">Hallucination<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Hal\u00fcsinasyon<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">An AI model producing a realistic-looking structure that is not in the real data, or removing one that is. In medical imaging, a small lesion appearing or disappearing can have serious consequences.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#veri-tutarliligi\" data-go=\"veri-tutarliligi\">Data consistency<\/a><a href=\"#belirsizlik\" data-go=\"belirsizlik\">Uncertainty estimation<\/a><a href=\"#derin-ogrenme-geri-catim\" data-go=\"derin-ogrenme-geri-catim\">Deep learning reconstruction<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"harmonizasyon\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#harmonizasyon\">Harmonisation<\/a><\/h2><span class=\"mrg-abbr\">ComBat<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Harmonizasyon<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Reducing systematic differences between data from different scanners and sites while keeping biological differences. Statistical methods such as ComBat or deep learning image translation are used.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#dagilim-kaymasi\" data-go=\"dagilim-kaymasi\">Domain shift<\/a><a href=\"#radyomik\" data-go=\"radyomik\">Radiomics<\/a><a href=\"#federe-ogrenme\" data-go=\"federe-ogrenme\">Federated learning<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"hardi\" data-c=\"difuzyon\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#hardi\">High angular resolution diffusion imaging<\/a><\/h2><span class=\"mrg-abbr\">HARDI<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Y\u00fcksek a\u00e7\u0131sal \u00e7\u00f6z\u00fcn\u00fcrl\u00fckl\u00fc dif\u00fczyon<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Acquiring data with many diffusion directions, often 60 or more. It makes it possible to resolve more than one fibre direction in a voxel.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-difuzyon\" data-cat=\"difuzyon\"><span class=\"mrg-dot\"><\/span>Diffusion MRI and analysis<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#b-vektoru\" data-go=\"b-vektoru\">Diffusion directions (b-vectors)<\/a><a href=\"#csd\" data-go=\"csd\">Constrained spherical deconvolution<\/a><a href=\"#kesisen-lifler\" data-go=\"kesisen-lifler\">Crossing fibres<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"yuksek-geciren-filtre\" data-c=\"fmri\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#yuksek-geciren-filtre\">High-pass filtering<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Y\u00fcksek ge\u00e7iren filtre<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Removing slow drifts in the fMRI time series caused by scanner heating and physiology. The cutoff is chosen so it does not remove the slowest change in the experimental design.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-fmri\" data-cat=\"fmri\"><span class=\"mrg-dot\"><\/span>Functional MRI<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#glm\" data-go=\"glm\">General linear model<\/a><a href=\"#gurultu-regresorleri\" data-go=\"gurultu-regresorleri\">Nuisance regressors<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t<h3 class=\"mrg-letter\" id=\"harf-I\">I<\/h3>\t\t\t\t\t<article class=\"mrg-entry\" id=\"geri-catim\" data-c=\"hizlandirma\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#geri-catim\">Image reconstruction<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> G\u00f6r\u00fcnt\u00fc geri\u00e7at\u0131m\u0131<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Computing the image from the measured raw data. For fully sampled Cartesian data this is an inverse Fourier transform. Undersampled data need extra information or models.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-hizlandirma\" data-cat=\"hizlandirma\"><span class=\"mrg-dot\"><\/span>Acceleration and reconstruction<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#fourier-donusumu\" data-go=\"fourier-donusumu\">Fourier transform<\/a><a href=\"#paralel-goruntuleme\" data-go=\"paralel-goruntuleme\">Parallel imaging<\/a><a href=\"#sikistirilmis-algilama\" data-go=\"sikistirilmis-algilama\">Compressed sensing<\/a><a href=\"#derin-ogrenme-geri-catim\" data-go=\"derin-ogrenme-geri-catim\">Deep learning reconstruction<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"cakistirma\" data-c=\"fmri\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#cakistirma\">Image registration<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> G\u00f6r\u00fcnt\u00fc \u00e7ak\u0131\u015ft\u0131rma<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Estimating the transform that brings two images into spatial alignment. It can be rigid (6 degrees of freedom), affine (12) or nonlinear. Aligning a functional image to the same person&#039;s anatomical image is called coregistration.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-fmri\" data-cat=\"fmri\"><span class=\"mrg-dot\"><\/span>Functional MRI<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#uzamsal-normalizasyon\" data-go=\"uzamsal-normalizasyon\">Spatial normalisation<\/a><a href=\"#hareket-duzeltme\" data-go=\"hareket-duzeltme\">Motion correction<\/a><a href=\"#on-isleme\" data-go=\"on-isleme\">Preprocessing<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"goruntu-sentezi\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#goruntu-sentezi\">Image synthesis<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> G\u00f6r\u00fcnt\u00fc sentezi<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Producing an image of a contrast or modality that was not acquired, from the images that were. For example, FLAIR can be synthesised from T1- and T2-weighted images, or a CT-like image from MRI. It can shorten exams, but results must be validated before diagnostic use.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#uretken-model\" data-go=\"uretken-model\">Generative model<\/a><a href=\"#goruntuden-goruntuye\" data-go=\"goruntuden-goruntuye\">Image-to-image translation<\/a><a href=\"#halusinasyon\" data-go=\"halusinasyon\">Hallucination<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"goruntuden-goruntuye\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#goruntuden-goruntuye\">Image-to-image translation<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> G\u00f6r\u00fcnt\u00fcden g\u00f6r\u00fcnt\u00fcye \u00e7eviri<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A learning task that maps an image to another domain. It can be trained with paired data (two contrasts from the same person) or unpaired data. Image synthesis and denoising are examples.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#goruntu-sentezi\" data-go=\"goruntu-sentezi\">Image synthesis<\/a><a href=\"#gurultu-giderme\" data-go=\"gurultu-giderme\">Denoising<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"inr\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#inr\">Implicit neural representation<\/a><\/h2><span class=\"mrg-abbr\">INR<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> \u00d6rt\u00fck sinirsel temsil<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Representing an image not as a pixel array but as a small neural network that takes a coordinate and returns the intensity there. It can be used for reconstruction from a single scan&#039;s data, with no training set.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#oz-gozetimli\" data-go=\"oz-gozetimli\">Self-supervised learning<\/a><a href=\"#geri-catim\" data-go=\"geri-catim\">Image reconstruction<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"ica\" data-c=\"fmri\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#ica\">Independent component analysis<\/a><\/h2><span class=\"mrg-abbr\">ICA<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Ba\u011f\u0131ms\u0131z bile\u015fen analizi<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A method that splits data into statistically independent spatial or temporal components. In fMRI it is used to find resting-state networks and to identify components caused by motion or physiological noise.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-fmri\" data-cat=\"fmri\"><span class=\"mrg-dot\"><\/span>Functional MRI<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#dinlenim-fmrg\" data-go=\"dinlenim-fmrg\">Resting-state fMRI<\/a><a href=\"#gurultu-regresorleri\" data-go=\"gurultu-regresorleri\">Nuisance regressors<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"ters-problem\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#ters-problem\">Inverse problem<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Ters problem<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">The problem of recovering the unknown object that produced the measured data. MR reconstruction is an inverse problem that recovers an image from incomplete, noisy k-space data and usually has no unique solution without prior information.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#ileri-model\" data-go=\"ileri-model\">Forward model<\/a><a href=\"#duzenlilestirme\" data-go=\"duzenlilestirme\">Regularisation<\/a><a href=\"#geri-catim\" data-go=\"geri-catim\">Image reconstruction<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"evirme-geri-kazanim\" data-c=\"sekans\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#evirme-geri-kazanim\">Inversion recovery<\/a><\/h2><span class=\"mrg-abbr\">IR<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Evirme geri kazan\u0131m\u0131<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A preparation that applies a 180\u00b0 inversion pulse and waits for magnetization to recover. It is used to boost T1 contrast or to null a chosen tissue.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-sekans\" data-cat=\"sekans\"><span class=\"mrg-dot\"><\/span>Sequences and contrast<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#ti-zamani\" data-go=\"ti-zamani\">Inversion time<\/a><a href=\"#flair\" data-go=\"flair\">Fluid-attenuated inversion recovery<\/a><a href=\"#stir\" data-go=\"stir\">Short tau inversion recovery<\/a><a href=\"#mprage\" data-go=\"mprage\">MPRAGE<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"ti-zamani\" data-c=\"sekans\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#ti-zamani\">Inversion time<\/a><\/h2><span class=\"mrg-abbr\">TI<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Evirme zaman\u0131<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">The time between the inversion pulse and the excitation pulse. Exciting when a tissue&#039;s signal crosses zero suppresses that tissue.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-sekans\" data-cat=\"sekans\"><span class=\"mrg-dot\"><\/span>Sequences and contrast<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#evirme-geri-kazanim\" data-go=\"evirme-geri-kazanim\">Inversion recovery<\/a><a href=\"#flair\" data-go=\"flair\">Fluid-attenuated inversion recovery<\/a><a href=\"#stir\" data-go=\"stir\">Short tau inversion recovery<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t<h3 class=\"mrg-letter\" id=\"harf-K\">K<\/h3>\t\t\t\t\t<article class=\"mrg-entry\" id=\"k-uzayi\" data-c=\"goruntu\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#k-uzayi\">k-space<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> k-uzay\u0131<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">The spatial-frequency domain where raw data are stored. The image is obtained by a Fourier transform of k-space. The centre carries contrast, the edges carry fine detail.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-goruntu\" data-cat=\"goruntu\"><span class=\"mrg-dot\"><\/span>Image formation<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#fourier-donusumu\" data-go=\"fourier-donusumu\">Fourier transform<\/a><a href=\"#kismi-fourier\" data-go=\"kismi-fourier\">Partial Fourier<\/a><a href=\"#sikistirilmis-algilama\" data-go=\"sikistirilmis-algilama\">Compressed sensing<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t<h3 class=\"mrg-letter\" id=\"harf-L\">L<\/h3>\t\t\t\t\t<article class=\"mrg-entry\" id=\"larmor-frekansi\" data-c=\"fizik\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#larmor-frekansi\">Larmor frequency<\/a><\/h2><span class=\"mrg-abbr\">\u03c9\u2080 = \u03b3B\u2080<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Larmor frekans\u0131<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">The frequency at which spins precess around the magnetic field. It is proportional to field strength. For hydrogen it is about 64 MHz at 1.5 T and about 128 MHz at 3 T.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-fizik\" data-cat=\"fizik\"><span class=\"mrg-dot\"><\/span>Basic physics<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#presesyon\" data-go=\"presesyon\">Precession<\/a><a href=\"#jiromanyetik-oran\" data-go=\"jiromanyetik-oran\">Gyromagnetic ratio<\/a><a href=\"#rf-darbesi\" data-go=\"rf-darbesi\">RF pulse<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"helyum\" data-c=\"donanim\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#helyum\">Liquid helium<\/a><\/h2><span class=\"mrg-abbr\">He<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> S\u0131v\u0131 helyum<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">The coolant that keeps superconducting windings at operating temperature. It boils at about 4.2 K. Closed cooling systems keep losses to a minimum.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-donanim\" data-cat=\"donanim\"><span class=\"mrg-dot\"><\/span>Hardware<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#superiletken-miknatis\" data-go=\"superiletken-miknatis\">Superconducting magnet<\/a><a href=\"#sondurme\" data-go=\"sondurme\">Quench<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t<h3 class=\"mrg-letter\" id=\"harf-M\">M<\/h3>\t\t\t\t\t<article class=\"mrg-entry\" id=\"mpi\" data-c=\"sekans\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#mpi\">Magnetic particle imaging<\/a><\/h2><span class=\"mrg-abbr\">MPI<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Manyetik par\u00e7ac\u0131k g\u00f6r\u00fcnt\u00fcleme<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A modality distinct from MRI that directly measures the magnetic response of superparamagnetic iron oxide nanoparticles. A field-free point or line is scanned to map where they are and how concentrated. There is no tissue background.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-sekans\" data-cat=\"sekans\"><span class=\"mrg-dot\"><\/span>Sequences and contrast<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#gradyan\" data-go=\"gradyan\">Gradient<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"manyetik-rezonans\" data-c=\"fizik\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#manyetik-rezonans\">Magnetic resonance<\/a><\/h2><span class=\"mrg-abbr\">MR<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Manyetik rezonans<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Nuclei placed in a strong magnetic field absorb and re-emit radio waves at their resonance frequency. MR imaging measures this signal, mostly from hydrogen nuclei in the water and fat of tissue.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-fizik\" data-cat=\"fizik\"><span class=\"mrg-dot\"><\/span>Basic physics<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#larmor-frekansi\" data-go=\"larmor-frekansi\">Larmor frequency<\/a><a href=\"#net-manyetizasyon\" data-go=\"net-manyetizasyon\">Net magnetization<\/a><a href=\"#rf-darbesi\" data-go=\"rf-darbesi\">RF pulse<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"manyetizasyon-transferi\" data-c=\"sekans\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#manyetizasyon-transferi\">Magnetization transfer<\/a><\/h2><span class=\"mrg-abbr\">MT<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Manyetizasyon transferi<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">The transfer of saturation from protons bound to large molecules to the free water signal. It gives contrast sensitive to macromolecular content such as myelin.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-sekans\" data-cat=\"sekans\"><span class=\"mrg-dot\"><\/span>Sequences and contrast<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#t1\" data-go=\"t1\">T1 relaxation<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"b0\" data-c=\"fizik\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#b0\">Main magnetic field<\/a><\/h2><span class=\"mrg-abbr\">B\u2080<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Ana manyetik alan<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">The scanner&#039;s strong, static magnetic field, which is always on. Its strength is given in tesla (T). UMRAM runs 3 T and 1.5 T research scanners.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-fizik\" data-cat=\"fizik\"><span class=\"mrg-dot\"><\/span>Basic physics<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#tesla\" data-go=\"tesla\">Tesla<\/a><a href=\"#superiletken-miknatis\" data-go=\"superiletken-miknatis\">Superconducting magnet<\/a><a href=\"#shim\" data-go=\"shim\">Shimming<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"matris\" data-c=\"goruntu\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#matris\">Matrix<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Matris<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">The number of pixels in each direction of the image, for example 256 \u00d7 256. A larger matrix at the same field of view gives finer detail but lowers SNR and can lengthen the scan.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-goruntu\" data-cat=\"goruntu\"><span class=\"mrg-dot\"><\/span>Image formation<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#fov\" data-go=\"fov\">Field of view<\/a><a href=\"#voksel\" data-go=\"voksel\">Voxel<\/a><a href=\"#snr\" data-go=\"snr\">Signal-to-noise ratio<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"difuzivite\" data-c=\"difuzyon\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#difuzivite\">Mean, axial and radial diffusivity<\/a><\/h2><span class=\"mrg-abbr\">MD, AD, RD<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Ortalama, eksenel ve radyal dif\u00fczivite<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Measures derived from the tensor. MD is the average diffusion over all directions, AD the diffusion along the main fibre direction and RD across it. They can help tell myelin changes from axonal changes.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-difuzyon\" data-cat=\"difuzyon\"><span class=\"mrg-dot\"><\/span>Diffusion MRI and analysis<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#fa\" data-go=\"fa\">Fractional anisotropy<\/a><a href=\"#dti\" data-go=\"dti\">Diffusion tensor imaging<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"hareket-artefakti\" data-c=\"artefakt\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#hareket-artefakti\">Motion artifact<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Hareket artefakt\u0131<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Blurring and ghosting from patient motion, breathing, heartbeat or blood flow during the scan. Ghosts usually appear along the phase-encoding direction.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-artefakt\" data-cat=\"artefakt\"><span class=\"mrg-dot\"><\/span>Artifacts<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#hareket-duzeltme\" data-go=\"hareket-duzeltme\">Motion correction<\/a><a href=\"#faz-kodlama\" data-go=\"faz-kodlama\">Phase encoding<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"hareket-duzeltme\" data-c=\"fmri\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#hareket-duzeltme\">Motion correction<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Hareket d\u00fczeltme<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Aligning the images of a time series to remove head motion. Even a few millimetres of motion can create spurious activation or connectivity.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-fmri\" data-cat=\"fmri\"><span class=\"mrg-dot\"><\/span>Functional MRI<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#hareket-artefakti\" data-go=\"hareket-artefakti\">Motion artifact<\/a><a href=\"#uzamsal-normalizasyon\" data-go=\"uzamsal-normalizasyon\">Spatial normalisation<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"mprage\" data-c=\"sekans\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#mprage\">MPRAGE<\/a><\/h2><span class=\"mrg-abbr\">MPRAGE<\/span><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<p class=\"mrg-def\">Magnetization-prepared rapid gradient echo: a high-resolution 3D T1-weighted sequence prepared with an inversion pulse. It is the usual anatomical reference scan in brain research.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-sekans\" data-cat=\"sekans\"><span class=\"mrg-dot\"><\/span>Sequences and contrast<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#t1-agirlikli\" data-go=\"t1-agirlikli\">T1-weighted image<\/a><a href=\"#gradyan-eko\" data-go=\"gradyan-eko\">Gradient echo<\/a><a href=\"#evirme-geri-kazanim\" data-go=\"evirme-geri-kazanim\">Inversion recovery<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"mr-kosullu\" data-c=\"guvenlik\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#mr-kosullu\">MR Safe, MR Conditional, MR Unsafe<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> MR g\u00fcvenli, MR ko\u015fullu, MR g\u00fcvensiz<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Standard labels for implants and devices. MR Safe poses no risk in any MR environment. MR Conditional may be scanned only under the conditions the manufacturer specifies, such as field strength and SAR. MR Unsafe must never enter the magnet room.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-guvenlik\" data-cat=\"guvenlik\"><span class=\"mrg-dot\"><\/span>Safety<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#tarama-formu\" data-go=\"tarama-formu\">Safety screening form<\/a><a href=\"#sar\" data-go=\"sar\">Specific absorption rate<\/a><a href=\"#sacak-alan\" data-go=\"sacak-alan\">Fringe field<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"guvenlik-bolgeleri\" data-c=\"guvenlik\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#guvenlik-bolgeleri\">MR safety zones<\/a><\/h2><span class=\"mrg-abbr\">B\u00f6lge I\u2013IV<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> MR g\u00fcvenlik b\u00f6lgeleri<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">The division of an MR unit into four zones by access level. Zone IV is the magnet room, entered only by screened people accompanied by trained staff.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-guvenlik\" data-cat=\"guvenlik\"><span class=\"mrg-dot\"><\/span>Safety<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#firlatma-etkisi\" data-go=\"firlatma-etkisi\">Projectile effect<\/a><a href=\"#tarama-formu\" data-go=\"tarama-formu\">Safety screening form<\/a><a href=\"#sacak-alan\" data-go=\"sacak-alan\">Fringe field<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"mr-spektroskopi\" data-c=\"sekans\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#mr-spektroskopi\">MR spectroscopy<\/a><\/h2><span class=\"mrg-abbr\">MRS<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> MR spektroskopi<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A method that measures the spectrum of chemical compounds in a volume instead of an image. In the brain, metabolites such as NAA, choline, creatine and lactate are told apart by their chemical shifts.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-sekans\" data-cat=\"sekans\"><span class=\"mrg-dot\"><\/span>Sequences and contrast<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#kimyasal-kayma\" data-go=\"kimyasal-kayma\">Chemical shift<\/a><a href=\"#shim\" data-go=\"shim\">Shimming<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"gebelik-mr\" data-c=\"guvenlik\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#gebelik-mr\">MRI in pregnancy<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Gebelikte MR<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Whether a pregnant patient has a clinical MRI is a physician&#039;s decision, and gadolinium-based contrast is generally avoided. In research scans, pregnancy is usually an exclusion criterion and is asked on the screening form.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-guvenlik\" data-cat=\"guvenlik\"><span class=\"mrg-dot\"><\/span>Safety<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#tarama-formu\" data-go=\"tarama-formu\">Safety screening form<\/a><a href=\"#gadolinyum\" data-go=\"gadolinyum\">Gadolinium-based contrast agent<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"cok-kabuklu\" data-c=\"difuzyon\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#cok-kabuklu\">Multi-shell diffusion<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> \u00c7ok kabuklu dif\u00fczyon<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Acquiring data at more than one b-value, each with many directions. Models beyond the tensor (NODDI, DKI) and methods that separate tissue types need such data.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-difuzyon\" data-cat=\"difuzyon\"><span class=\"mrg-dot\"><\/span>Diffusion MRI and analysis<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#b-degeri\" data-go=\"b-degeri\">b-value<\/a><a href=\"#noddi\" data-go=\"noddi\">NODDI<\/a><a href=\"#dki\" data-go=\"dki\">Diffusion kurtosis imaging<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"cok-kipli\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#cok-kipli\">Multimodal learning<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> \u00c7ok kipli \u00f6\u011frenme<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Learning that combines different MR contrasts, other imaging modalities, clinical data or text. The sources complement each other. Coping with missing modalities is an active research topic.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#goruntu-sentezi\" data-go=\"goruntu-sentezi\">Image synthesis<\/a><a href=\"#gorme-dil-modeli\" data-go=\"gorme-dil-modeli\">Vision-language model<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"coklu-karsilastirma\" data-c=\"fmri\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#coklu-karsilastirma\">Multiple comparisons correction<\/a><\/h2><span class=\"mrg-abbr\">FWE, FDR<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> \u00c7oklu kar\u015f\u0131la\u015ft\u0131rma d\u00fczeltmesi<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Adjusting the threshold to limit chance findings when tens of thousands of voxels are tested separately. Either the family-wise error rate (FWE) or the false discovery rate (FDR) is controlled. Cluster-based and permutation tests are also common.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-fmri\" data-cat=\"fmri\"><span class=\"mrg-dot\"><\/span>Functional MRI<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#kontrast-istatistik\" data-go=\"kontrast-istatistik\">Statistical contrast<\/a><a href=\"#kume-cikarimi\" data-go=\"kume-cikarimi\">Cluster-based inference<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"mvpa\" data-c=\"fmri\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#mvpa\">Multivoxel pattern analysis<\/a><\/h2><span class=\"mrg-abbr\">MVPA<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> \u00c7ok vokselli \u00f6r\u00fcnt\u00fc analizi<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">An analysis that decodes information, such as which stimulus was seen, from the joint activity pattern of many voxels instead of single voxels. Machine learning classifiers are often used.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-fmri\" data-cat=\"fmri\"><span class=\"mrg-dot\"><\/span>Functional MRI<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#glm\" data-go=\"glm\">General linear model<\/a><a href=\"#yapay-zeka-mr\" data-go=\"yapay-zeka-mr\">Artificial intelligence<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t<h3 class=\"mrg-letter\" id=\"harf-N\">N<\/h3>\t\t\t\t\t<article class=\"mrg-entry\" id=\"net-manyetizasyon\" data-c=\"fizik\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#net-manyetizasyon\">Net magnetization<\/a><\/h2><span class=\"mrg-abbr\">M\u2080<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Net manyetizasyon<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">The summed magnetic moment of all spins in a volume. At equilibrium it points along B\u2080. The measured MR signal arises when this vector is tipped into the transverse plane.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-fizik\" data-cat=\"fizik\"><span class=\"mrg-dot\"><\/span>Basic physics<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#rf-darbesi\" data-go=\"rf-darbesi\">RF pulse<\/a><a href=\"#cevirme-acisi\" data-go=\"cevirme-acisi\">Flip angle<\/a><a href=\"#t1\" data-go=\"t1\">T1 relaxation<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"noddi\" data-c=\"difuzyon\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#noddi\">NODDI<\/a><\/h2><span class=\"mrg-abbr\">NODDI<\/span><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<p class=\"mrg-def\">Neurite orientation dispersion and density imaging: a biophysical model that estimates more tissue-specific measures, such as neurite density and orientation dispersion, from multi-shell data.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-difuzyon\" data-cat=\"difuzyon\"><span class=\"mrg-dot\"><\/span>Diffusion MRI and analysis<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#cok-kabuklu\" data-go=\"cok-kabuklu\">Multi-shell diffusion<\/a><a href=\"#fa\" data-go=\"fa\">Fractional anisotropy<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"kartezyen-olmayan\" data-c=\"goruntu\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#kartezyen-olmayan\">Non-Cartesian sampling<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Kartezyen olmayan \u00f6rnekleme<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Sampling k-space along radial spokes or spirals instead of straight lines. It can be more robust to motion or faster, but reconstruction is more complex.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-goruntu\" data-cat=\"goruntu\"><span class=\"mrg-dot\"><\/span>Image formation<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#k-uzayi\" data-go=\"k-uzayi\">k-space<\/a><a href=\"#geri-catim\" data-go=\"geri-catim\">Image reconstruction<\/a><a href=\"#hareket-artefakti\" data-go=\"hareket-artefakti\">Motion artifact<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"normatif-model\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#normatif-model\">Normative modelling<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Normatif modelleme<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Learning the normal distribution of a brain measure by age and sex from a large healthy sample, then measuring how far individuals deviate from it. It lets patients be studied as individuals rather than as group averages.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#beyin-yasi\" data-go=\"beyin-yasi\">Brain age prediction<\/a><a href=\"#anomali-tespiti\" data-go=\"anomali-tespiti\">Anomaly detection<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"gurultu-regresorleri\" data-c=\"fmri\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#gurultu-regresorleri\">Nuisance regressors<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> G\u00fcr\u00fclt\u00fc regres\u00f6rleri<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Variables of no interest added to the model so they can be removed from the signal, such as motion parameters, white matter and cerebrospinal fluid signals, or CompCor components. They matter especially in resting-state analysis.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-fmri\" data-cat=\"fmri\"><span class=\"mrg-dot\"><\/span>Functional MRI<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#glm\" data-go=\"glm\">General linear model<\/a><a href=\"#fizyolojik-gurultu\" data-go=\"fizyolojik-gurultu\">Physiological noise<\/a><a href=\"#cerceve-yer-degistirmesi\" data-go=\"cerceve-yer-degistirmesi\">Framewise displacement<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t<h3 class=\"mrg-letter\" id=\"harf-O\">O<\/h3>\t\t\t\t\t<article class=\"mrg-entry\" id=\"asiri-ogrenme\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#asiri-ogrenme\">Overfitting<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> A\u015f\u0131r\u0131 \u00f6\u011frenme<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A model memorising its training data and failing on new data. It is common when large models are trained on little data. Data augmentation and regularisation reduce the risk.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#egitim-test\" data-go=\"egitim-test\">Training, validation and test sets<\/a><a href=\"#veri-artirma\" data-go=\"veri-artirma\">Data augmentation<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t<h3 class=\"mrg-letter\" id=\"harf-P\">P<\/h3>\t\t\t\t\t<article class=\"mrg-entry\" id=\"paralel-goruntuleme\" data-c=\"hizlandirma\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#paralel-goruntuleme\">Parallel imaging<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Paralel g\u00f6r\u00fcnt\u00fcleme<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">An acceleration method that skips part of k-space by using the different spatial sensitivities of multichannel receive coils. The speed-up is given as the factor R.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-hizlandirma\" data-cat=\"hizlandirma\"><span class=\"mrg-dot\"><\/span>Acceleration and reconstruction<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#sense\" data-go=\"sense\">SENSE<\/a><a href=\"#grappa\" data-go=\"grappa\">GRAPPA<\/a><a href=\"#alici-bobin\" data-go=\"alici-bobin\">Receive array coil<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"paralel-iletim\" data-c=\"donanim\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#paralel-iletim\">Parallel transmission<\/a><\/h2><span class=\"mrg-abbr\">pTx<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Paralel iletim<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Delivering the RF pulse through several independently controlled transmit channels. It is used at high field to even out B\u2081 and manage SAR. UMRAM&#039;s 3 T scanner supports a transmit array.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-donanim\" data-cat=\"donanim\"><span class=\"mrg-dot\"><\/span>Hardware<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#b1\" data-go=\"b1\">RF magnetic field<\/a><a href=\"#sar\" data-go=\"sar\">Specific absorption rate<\/a><a href=\"#rf-bobini\" data-go=\"rf-bobini\">RF coil<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"kismi-fourier\" data-c=\"hizlandirma\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#kismi-fourier\">Partial Fourier<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> K\u0131smi Fourier<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">An acceleration method that uses k-space symmetry to measure only a little over half of it. Scan time drops, with a small loss of SNR.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-hizlandirma\" data-cat=\"hizlandirma\"><span class=\"mrg-dot\"><\/span>Acceleration and reconstruction<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#k-uzayi\" data-go=\"k-uzayi\">k-space<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"kismi-hacim\" data-c=\"goruntu\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#kismi-hacim\">Partial volume effect<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> K\u0131smi hacim etkisi<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A voxel containing more than one tissue, so its signal is a mixture. It blurs the edges of small structures and affects volume measurements.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-goruntu\" data-cat=\"goruntu\"><span class=\"mrg-dot\"><\/span>Image formation<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#voksel\" data-go=\"voksel\">Voxel<\/a><a href=\"#kesit-kalinligi\" data-go=\"kesit-kalinligi\">Slice thickness<\/a><a href=\"#bolutleme\" data-go=\"bolutleme\">Segmentation<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"periferik-sinir-uyarimi\" data-c=\"guvenlik\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#periferik-sinir-uyarimi\">Peripheral nerve stimulation<\/a><\/h2><span class=\"mrg-abbr\">PNS<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Periferik sinir uyar\u0131m\u0131<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Tingling or muscle twitching caused by currents that rapid gradient switching induces in tissue. The scanner keeps gradient switching below this threshold.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-guvenlik\" data-cat=\"guvenlik\"><span class=\"mrg-dot\"><\/span>Safety<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#gradyan-bobini\" data-go=\"gradyan-bobini\">Gradient coil<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"faz-kodlama\" data-c=\"goruntu\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#faz-kodlama\">Phase encoding<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Faz kodlama<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Applying a brief gradient before readout so that position is stored in the spin phase. Each phase-encoding step fills one line of k-space, and the number of steps largely sets scan time.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-goruntu\" data-cat=\"goruntu\"><span class=\"mrg-dot\"><\/span>Image formation<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#frekans-kodlama\" data-go=\"frekans-kodlama\">Frequency encoding<\/a><a href=\"#k-uzayi\" data-go=\"k-uzayi\">k-space<\/a><a href=\"#katlanma-artefakti\" data-go=\"katlanma-artefakti\">Aliasing (wrap-around)<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"fizik-gudumlu-ag\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#fizik-gudumlu-ag\">Physics-guided network<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Fizik g\u00fcd\u00fcml\u00fc a\u011f<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A deep learning method that builds the MR measurement model, the encoding and coil sensitivities, into the network. The steps of an iterative reconstruction algorithm are unrolled into network layers. It is more reliable and generalises better than purely data-driven networks.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#derin-ogrenme-geri-catim\" data-go=\"derin-ogrenme-geri-catim\">Deep learning reconstruction<\/a><a href=\"#veri-tutarliligi\" data-go=\"veri-tutarliligi\">Data consistency<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"fizyolojik-gurultu\" data-c=\"fmri\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#fizyolojik-gurultu\">Physiological noise<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Fizyolojik g\u00fcr\u00fclt\u00fc<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Fluctuations in the BOLD signal caused by breathing and heartbeat. They can be recorded with a respiratory belt and pulse sensor and removed with methods such as RETROICOR.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-fmri\" data-cat=\"fmri\"><span class=\"mrg-dot\"><\/span>Functional MRI<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#gurultu-regresorleri\" data-go=\"gurultu-regresorleri\">Nuisance regressors<\/a><a href=\"#tetikleme\" data-go=\"tetikleme\">Cardiac and respiratory gating<\/a><a href=\"#bold\" data-go=\"bold\">Blood oxygenation level dependent signal<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"tak-calistir\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#tak-calistir\">Plug-and-play priors<\/a><\/h2><span class=\"mrg-abbr\">PnP<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Tak-\u00e7al\u0131\u015ft\u0131r \u00f6nseller<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A method that replaces the regularisation step of an iterative reconstruction with a pretrained denoising network. The same network can be used with different sampling patterns without retraining.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#duzenlilestirme\" data-go=\"duzenlilestirme\">Regularisation<\/a><a href=\"#gurultu-giderme\" data-go=\"gurultu-giderme\">Denoising<\/a><a href=\"#uretken-onsel\" data-go=\"uretken-onsel\">Generative prior reconstruction<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"presesyon\" data-c=\"fizik\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#presesyon\">Precession<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Presesyon<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">The rotation of a spin&#039;s axis around the field direction, like the axis of a spinning top. Its rate is the Larmor frequency.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-fizik\" data-cat=\"fizik\"><span class=\"mrg-dot\"><\/span>Basic physics<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#larmor-frekansi\" data-go=\"larmor-frekansi\">Larmor frequency<\/a><a href=\"#spin\" data-go=\"spin\">Spin<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"on-isleme\" data-c=\"fmri\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#on-isleme\">Preprocessing<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> \u00d6n i\u015fleme<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">The chain of steps that prepares raw fMRI or diffusion data for statistical analysis. For fMRI a typical order is motion correction, slice timing correction, distortion correction, registration to the anatomical image, normalisation and smoothing.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-fmri\" data-cat=\"fmri\"><span class=\"mrg-dot\"><\/span>Functional MRI<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#hareket-duzeltme\" data-go=\"hareket-duzeltme\">Motion correction<\/a><a href=\"#dilim-zamanlamasi\" data-go=\"dilim-zamanlamasi\">Slice timing correction<\/a><a href=\"#cakistirma\" data-go=\"cakistirma\">Image registration<\/a><a href=\"#uzamsal-yumusatma\" data-go=\"uzamsal-yumusatma\">Spatial smoothing<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"mahremiyet\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#mahremiyet\">Privacy-preserving learning<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Mahremiyet koruyan \u00f6\u011frenme<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Methods that make it hard to recover personal data from a model or from shared information, during or after training. Federated learning, differential privacy and de-identification are among them.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#federe-ogrenme\" data-go=\"federe-ogrenme\">Federated learning<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"firlatma-etkisi\" data-c=\"guvenlik\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#firlatma-etkisi\">Projectile effect<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> F\u0131rlatma etkisi<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Ferromagnetic objects being pulled into the magnet with great force. Scissors, oxygen cylinders or chairs can cause fatal injury. Because the magnet is always on, the risk exists outside scans too.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-guvenlik\" data-cat=\"guvenlik\"><span class=\"mrg-dot\"><\/span>Safety<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#guvenlik-bolgeleri\" data-go=\"guvenlik-bolgeleri\">MR safety zones<\/a><a href=\"#superiletken-miknatis\" data-go=\"superiletken-miknatis\">Superconducting magnet<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"proton-yogunlugu\" data-c=\"fizik\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#proton-yogunlugu\">Proton density<\/a><\/h2><span class=\"mrg-abbr\">PD<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Proton yo\u011funlu\u011fu<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">The amount of signal-producing hydrogen nuclei in a tissue. Proton density weighted images use a long TR and short TE to reduce T1 and T2 effects and bring out this difference.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-fizik\" data-cat=\"fizik\"><span class=\"mrg-dot\"><\/span>Basic physics<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#tr-zamani\" data-go=\"tr-zamani\">Repetition time<\/a><a href=\"#te-zamani\" data-go=\"te-zamani\">Echo time<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"psnr-ssim\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#psnr-ssim\">PSNR and SSIM<\/a><\/h2><span class=\"mrg-abbr\">PSNR, SSIM<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> PSNR ve SSIM<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Two common metrics for how closely a generated image matches a reference. PSNR looks at pixel differences, SSIM at structural similarity. High values do not always guarantee diagnostic value.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#gurultu-giderme\" data-go=\"gurultu-giderme\">Denoising<\/a><a href=\"#goruntu-sentezi\" data-go=\"goruntu-sentezi\">Image synthesis<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"ppi\" data-c=\"fmri\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#ppi\">Psychophysiological interaction<\/a><\/h2><span class=\"mrg-abbr\">PPI<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Psikofizyolojik etkile\u015fim<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">An analysis that tests how a region&#039;s connectivity with other regions changes with the task condition.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-fmri\" data-cat=\"fmri\"><span class=\"mrg-dot\"><\/span>Functional MRI<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#fonksiyonel-baglantisallik\" data-go=\"fonksiyonel-baglantisallik\">Functional connectivity<\/a><a href=\"#glm\" data-go=\"glm\">General linear model<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"acik-veri\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#acik-veri\">Public datasets<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> A\u00e7\u0131k veri setleri<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Openly shared image collections that let methods be compared. For example, fastMRI offers raw k-space data, IXI multi-contrast brain images and BraTS tumour segmentation data. Their terms of use and ethics approvals must be respected.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#egitim-test\" data-go=\"egitim-test\">Training, validation and test sets<\/a><a href=\"#bolutleme\" data-go=\"bolutleme\">Segmentation<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t<h3 class=\"mrg-letter\" id=\"harf-Q\">Q<\/h3>\t\t\t\t\t<article class=\"mrg-entry\" id=\"qsm\" data-c=\"sekans\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#qsm\">Quantitative susceptibility mapping<\/a><\/h2><span class=\"mrg-abbr\">QSM<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Say\u0131sal duyarl\u0131l\u0131k haritalama<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A method that computes tissue magnetic susceptibility quantitatively from gradient echo phase data. It is used to study iron and myelin content.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-sekans\" data-cat=\"sekans\"><span class=\"mrg-dot\"><\/span>Sequences and contrast<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#swi\" data-go=\"swi\">Susceptibility-weighted imaging<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"sondurme\" data-c=\"guvenlik\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#sondurme\">Quench<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> S\u00f6nd\u00fcrme<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">The rapid loss of superconductivity in the magnet. The helium boils off and is vented through the quench pipe. It can be triggered on purpose to shut down the field in an emergency. Gas leaking into the room creates a risk of asphyxiation.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-guvenlik\" data-cat=\"guvenlik\"><span class=\"mrg-dot\"><\/span>Safety<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#superiletken-miknatis\" data-go=\"superiletken-miknatis\">Superconducting magnet<\/a><a href=\"#helyum\" data-go=\"helyum\">Liquid helium<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t<h3 class=\"mrg-letter\" id=\"harf-R\">R<\/h3>\t\t\t\t\t<article class=\"mrg-entry\" id=\"radyomik\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#radyomik\">Radiomics<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Radyomik<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Extracting many texture, shape and intensity features from medical images and using them for diagnosis or outcome prediction. Because features depend on scanner and sequence settings, harmonisation and external validation are needed.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#oznitelik\" data-go=\"oznitelik\">Feature extraction<\/a><a href=\"#harmonizasyon\" data-go=\"harmonizasyon\">Harmonisation<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"alici-bobin\" data-c=\"donanim\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#alici-bobin\">Receive array coil<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Al\u0131c\u0131 dizi bobin<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A receiver built from many small coil elements, each with its own channel. It raises SNR and makes parallel imaging possible.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-donanim\" data-cat=\"donanim\"><span class=\"mrg-dot\"><\/span>Hardware<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#rf-bobini\" data-go=\"rf-bobini\">RF coil<\/a><a href=\"#paralel-goruntuleme\" data-go=\"paralel-goruntuleme\">Parallel imaging<\/a><a href=\"#snr\" data-go=\"snr\">Signal-to-noise ratio<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"bant-genisligi\" data-c=\"goruntu\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#bant-genisligi\">Receiver bandwidth<\/a><\/h2><span class=\"mrg-abbr\">BW<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Al\u0131c\u0131 bant geni\u015fli\u011fi<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">The range of frequencies sampled during readout, often given in Hz\/pixel. A higher bandwidth shortens readout and reduces chemical shift artifact, but lowers SNR.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-goruntu\" data-cat=\"goruntu\"><span class=\"mrg-dot\"><\/span>Image formation<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#frekans-kodlama\" data-go=\"frekans-kodlama\">Frequency encoding<\/a><a href=\"#kimyasal-kayma-artefakti\" data-go=\"kimyasal-kayma-artefakti\">Chemical shift artifact<\/a><a href=\"#snr\" data-go=\"snr\">Signal-to-noise ratio<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"ilgi-bolgesi\" data-c=\"fmri\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#ilgi-bolgesi\">Region of interest<\/a><\/h2><span class=\"mrg-abbr\">ROI<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> \u0130lgi b\u00f6lgesi<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Restricting analysis to a predefined brain region. The region can come from an anatomical atlas, a separate localiser scan or earlier studies. It raises statistical power and reduces the multiple comparisons burden.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-fmri\" data-cat=\"fmri\"><span class=\"mrg-dot\"><\/span>Functional MRI<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#atlas\" data-go=\"atlas\">Atlas and parcellation<\/a><a href=\"#coklu-karsilastirma\" data-go=\"coklu-karsilastirma\">Multiple comparisons correction<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"regresyon-ml\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#regresyon-ml\">Regression<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Regresyon<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Predicting a continuous value from images, such as age, a cognitive score or disease severity. Performance is measured with mean absolute error or correlation.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#beyin-yasi\" data-go=\"beyin-yasi\">Brain age prediction<\/a><a href=\"#capraz-dogrulama\" data-go=\"capraz-dogrulama\">Cross-validation<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"duzenlilestirme\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#duzenlilestirme\">Regularisation<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> D\u00fczenlile\u015ftirme<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A prior added to an inverse problem to pick a plausible solution among the many that fit the measurements. Total variation and wavelet sparsity are classic examples. In deep learning the prior is learned from data.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#ters-problem\" data-go=\"ters-problem\">Inverse problem<\/a><a href=\"#sikistirilmis-algilama\" data-go=\"sikistirilmis-algilama\">Compressed sensing<\/a><a href=\"#tak-calistir\" data-go=\"tak-calistir\">Plug-and-play priors<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"tr-zamani\" data-c=\"sekans\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#tr-zamani\">Repetition time<\/a><\/h2><span class=\"mrg-abbr\">TR<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Tekrarlama zaman\u0131<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">The time between successive excitation pulses. A short TR emphasises T1 differences. Scan time is close to TR times the number of phase-encoding steps.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-sekans\" data-cat=\"sekans\"><span class=\"mrg-dot\"><\/span>Sequences and contrast<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#te-zamani\" data-go=\"te-zamani\">Echo time<\/a><a href=\"#t1-agirlikli\" data-go=\"t1-agirlikli\">T1-weighted image<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"rsa\" data-c=\"fmri\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#rsa\">Representational similarity analysis<\/a><\/h2><span class=\"mrg-abbr\">RSA<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Temsili benzerlik analizi<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">An analysis that summarises how similar the activity patterns for different stimuli are in a matrix and compares that structure with models or other regions.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-fmri\" data-cat=\"fmri\"><span class=\"mrg-dot\"><\/span>Functional MRI<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#mvpa\" data-go=\"mvpa\">Multivoxel pattern analysis<\/a><a href=\"#kodlama-modeli\" data-go=\"kodlama-modeli\">Encoding model<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"dinlenim-fmrg\" data-c=\"fmri\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#dinlenim-fmrg\">Resting-state fMRI<\/a><\/h2><span class=\"mrg-abbr\">rs-fMRI<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Dinlenim durumu fMRG<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">fMRI acquired while the participant performs no task. The synchrony of slow signal fluctuations between regions is studied as functional connectivity.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-fmri\" data-cat=\"fmri\"><span class=\"mrg-dot\"><\/span>Functional MRI<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#fmrg\" data-go=\"fmrg\">Functional MRI<\/a><a href=\"#bold\" data-go=\"bold\">Blood oxygenation level dependent signal<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"retinotopi\" data-c=\"fmri\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#retinotopi\">Retinotopic mapping<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Retinotopik haritalama<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Mapping with fMRI how positions in the visual field are laid out across visual cortex. Rotating wedge and expanding ring stimuli or population receptive field (pRF) models are used.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-fmri\" data-cat=\"fmri\"><span class=\"mrg-dot\"><\/span>Functional MRI<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#fmrg\" data-go=\"fmrg\">Functional MRI<\/a><a href=\"#kodlama-modeli\" data-go=\"kodlama-modeli\">Encoding model<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"rf-yanigi\" data-c=\"guvenlik\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#rf-yanigi\">RF burn<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> RF yan\u0131\u011f\u0131<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A burn caused by RF energy concentrating at skin contact points, cables or conductive loops formed by the body. Keeping arms and legs from touching, keeping cables off the skin and using proper pads reduce the risk.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-guvenlik\" data-cat=\"guvenlik\"><span class=\"mrg-dot\"><\/span>Safety<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#sar\" data-go=\"sar\">Specific absorption rate<\/a><a href=\"#rf-darbesi\" data-go=\"rf-darbesi\">RF pulse<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"rf-bobini\" data-c=\"donanim\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#rf-bobini\">RF coil<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> RF bobini<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">The antenna that transmits RF pulses and receives the MR signal. The body coil is built into the bore. Dedicated coils placed close to the head, knee and other regions give higher SNR.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-donanim\" data-cat=\"donanim\"><span class=\"mrg-dot\"><\/span>Hardware<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#alici-bobin\" data-go=\"alici-bobin\">Receive array coil<\/a><a href=\"#paralel-iletim\" data-go=\"paralel-iletim\">Parallel transmission<\/a><a href=\"#b1\" data-go=\"b1\">RF magnetic field<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"b1\" data-c=\"fizik\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#b1\">RF magnetic field<\/a><\/h2><span class=\"mrg-abbr\">B\u2081<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> RF manyetik alan\u0131<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">The weak magnetic field produced during an RF pulse, perpendicular to B\u2080 and rotating at the Larmor frequency. At high field it may not spread evenly through the body.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-fizik\" data-cat=\"fizik\"><span class=\"mrg-dot\"><\/span>Basic physics<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#rf-darbesi\" data-go=\"rf-darbesi\">RF pulse<\/a><a href=\"#paralel-iletim\" data-go=\"paralel-iletim\">Parallel transmission<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"rf-darbesi\" data-c=\"fizik\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#rf-darbesi\">RF pulse<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> RF darbesi<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A short burst of radiofrequency energy at the Larmor frequency. It tips the net magnetization away from equilibrium to create signal. Different pulses are used for excitation, refocusing and inversion.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-fizik\" data-cat=\"fizik\"><span class=\"mrg-dot\"><\/span>Basic physics<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#b1\" data-go=\"b1\">RF magnetic field<\/a><a href=\"#cevirme-acisi\" data-go=\"cevirme-acisi\">Flip angle<\/a><a href=\"#sar\" data-go=\"sar\">Specific absorption rate<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"rf-kafes\" data-c=\"donanim\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#rf-kafes\">RF shield<\/a><\/h2><span class=\"mrg-abbr\">Faraday kafesi<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> RF kafesi<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A conductive enclosure around the magnet room that keeps outside radio signals from entering the image. If the door is not fully closed, interference lines can appear.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-donanim\" data-cat=\"donanim\"><span class=\"mrg-dot\"><\/span>Hardware<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#fermuar-artefakti\" data-go=\"fermuar-artefakti\">Zipper artifact<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"roc-auc\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#roc-auc\">ROC curve and AUC<\/a><\/h2><span class=\"mrg-abbr\">AUC<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> ROC e\u011frisi ve AUC<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A curve showing a classifier&#039;s sensitivity against false-positive rate across decision thresholds, and the area under it. An AUC of 0.5 means chance, 1 means perfect separation.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#siniflandirma\" data-go=\"siniflandirma\">Classification<\/a><a href=\"#tanisal-olcutler\" data-go=\"tanisal-olcutler\">Diagnostic sensitivity and specificity<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t<h3 class=\"mrg-letter\" id=\"harf-S\">S<\/h3>\t\t\t\t\t<article class=\"mrg-entry\" id=\"tarama-formu\" data-c=\"guvenlik\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#tarama-formu\">Safety screening form<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> G\u00fcvenlik tarama formu<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A form completed before scanning that asks about implants, metal fragments, pregnancy and other risks. It is filled in for the participant and for anyone who will enter the magnet room with them.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-guvenlik\" data-cat=\"guvenlik\"><span class=\"mrg-dot\"><\/span>Safety<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#guvenlik-bolgeleri\" data-go=\"guvenlik-bolgeleri\">MR safety zones<\/a><a href=\"#mr-kosullu\" data-go=\"mr-kosullu\">MR Safe, MR Conditional, MR Unsafe<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"bolutleme\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#bolutleme\">Segmentation<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> B\u00f6l\u00fctleme<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Assigning every voxel to a structure or tissue, for example outlining brain tissues, a tumour or the hippocampus. It is a basic step for volume measurement and treatment planning.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#u-net\" data-go=\"u-net\">U-Net<\/a><a href=\"#dice\" data-go=\"dice\">Dice coefficient<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"oz-gozetimli\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#oz-gozetimli\">Self-supervised learning<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> \u00d6z-g\u00f6zetimli \u00f6\u011frenme<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">An approach that derives a training signal from the structure of the data itself, with no labels or references. For example, measured k-space is split in two and the model learns to predict one part from the other. It is valuable when fully sampled data do not exist.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#gozetimli-ogrenme\" data-go=\"gozetimli-ogrenme\">Supervised learning<\/a><a href=\"#derin-ogrenme-geri-catim\" data-go=\"derin-ogrenme-geri-catim\">Deep learning reconstruction<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"sense\" data-c=\"hizlandirma\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#sense\">SENSE<\/a><\/h2><span class=\"mrg-abbr\">SENSE<\/span><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<p class=\"mrg-def\">Sensitivity encoding: a parallel imaging method that unfolds aliased images in image space using coil sensitivity maps.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-hizlandirma\" data-cat=\"hizlandirma\"><span class=\"mrg-dot\"><\/span>Acceleration and reconstruction<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#paralel-goruntuleme\" data-go=\"paralel-goruntuleme\">Parallel imaging<\/a><a href=\"#grappa\" data-go=\"grappa\">GRAPPA<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"shim\" data-c=\"donanim\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#shim\">Shimming<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> \u015eim<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Adjusting the main field to make it more uniform. Passive shimming uses pieces of metal, active shimming uses extra coils. Good shimming directly affects fat suppression, EPI and spectroscopy.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-donanim\" data-cat=\"donanim\"><span class=\"mrg-dot\"><\/span>Hardware<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#b0\" data-go=\"b0\">Main magnetic field<\/a><a href=\"#duyarlilik-artefakti\" data-go=\"duyarlilik-artefakti\">Susceptibility artifact<\/a><a href=\"#mr-spektroskopi\" data-go=\"mr-spektroskopi\">MR spectroscopy<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"stir\" data-c=\"sekans\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#stir\">Short tau inversion recovery<\/a><\/h2><span class=\"mrg-abbr\">STIR<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> K\u0131sa TI evirme geri kazan\u0131m\u0131<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A sequence that suppresses fat signal using a short inversion time. It is less sensitive to field inhomogeneity than frequency-selective fat suppression.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-sekans\" data-cat=\"sekans\"><span class=\"mrg-dot\"><\/span>Sequences and contrast<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#evirme-geri-kazanim\" data-go=\"evirme-geri-kazanim\">Inversion recovery<\/a><a href=\"#kimyasal-kayma\" data-go=\"kimyasal-kayma\">Chemical shift<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"snr\" data-c=\"goruntu\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#snr\">Signal-to-noise ratio<\/a><\/h2><span class=\"mrg-abbr\">SNR<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Sinyal-g\u00fcr\u00fclt\u00fc oran\u0131<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">The ratio of image signal to noise. Field strength, voxel volume, coil, receiver bandwidth and number of averages all affect it. Quadrupling the averages doubles SNR.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-goruntu\" data-cat=\"goruntu\"><span class=\"mrg-dot\"><\/span>Image formation<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#cnr\" data-go=\"cnr\">Contrast-to-noise ratio<\/a><a href=\"#voksel\" data-go=\"voksel\">Voxel<\/a><a href=\"#alici-bobin\" data-go=\"alici-bobin\">Receive array coil<\/a><a href=\"#bant-genisligi\" data-go=\"bant-genisligi\">Receiver bandwidth<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"sms\" data-c=\"hizlandirma\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#sms\">Simultaneous multi-slice<\/a><\/h2><span class=\"mrg-abbr\">SMS, multiband<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> E\u015fzamanl\u0131 \u00e7ok kesitli g\u00f6r\u00fcnt\u00fcleme<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Exciting several slices at once and separating them with receive coil sensitivities. In fMRI and diffusion imaging it gives whole-brain coverage at short TR.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-hizlandirma\" data-cat=\"hizlandirma\"><span class=\"mrg-dot\"><\/span>Acceleration and reconstruction<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#paralel-goruntuleme\" data-go=\"paralel-goruntuleme\">Parallel imaging<\/a><a href=\"#epi\" data-go=\"epi\">Echo planar imaging<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"kesit-secimi\" data-c=\"goruntu\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#kesit-secimi\">Slice selection<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Kesit se\u00e7imi<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Applying a gradient during the RF pulse so that only spins in one slice are excited. Slice thickness is set by the pulse bandwidth and the gradient strength.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-goruntu\" data-cat=\"goruntu\"><span class=\"mrg-dot\"><\/span>Image formation<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#gradyan\" data-go=\"gradyan\">Gradient<\/a><a href=\"#kesit-kalinligi\" data-go=\"kesit-kalinligi\">Slice thickness<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"kesit-kalinligi\" data-c=\"goruntu\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#kesit-kalinligi\">Slice thickness<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Kesit kal\u0131nl\u0131\u011f\u0131<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">The thickness of the excited slice in 2D imaging. Thin slices reduce partial volume effects but lower SNR.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-goruntu\" data-cat=\"goruntu\"><span class=\"mrg-dot\"><\/span>Image formation<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#kesit-secimi\" data-go=\"kesit-secimi\">Slice selection<\/a><a href=\"#voksel\" data-go=\"voksel\">Voxel<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"dilim-zamanlamasi\" data-c=\"fmri\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#dilim-zamanlamasi\">Slice timing correction<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Dilim zamanlamas\u0131 d\u00fczeltmesi<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Removing the timing differences caused by slices of a volume being acquired at different moments, by shifting each slice&#039;s signal to a common time point. Its impact is smaller with short TR and simultaneous multi-slice imaging.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-fmri\" data-cat=\"fmri\"><span class=\"mrg-dot\"><\/span>Functional MRI<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#on-isleme\" data-go=\"on-isleme\">Preprocessing<\/a><a href=\"#sms\" data-go=\"sms\">Simultaneous multi-slice<\/a><a href=\"#tr-zamani\" data-go=\"tr-zamani\">Repetition time<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"uzamsal-normalizasyon\" data-c=\"fmri\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#uzamsal-normalizasyon\">Spatial normalisation<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Uzamsal normalizasyon<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Transforming different people&#039;s brain images into a common template space, such as MNI space. It is a prerequisite for group analysis.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-fmri\" data-cat=\"fmri\"><span class=\"mrg-dot\"><\/span>Functional MRI<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#hareket-duzeltme\" data-go=\"hareket-duzeltme\">Motion correction<\/a><a href=\"#mprage\" data-go=\"mprage\">MPRAGE<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"uzamsal-yumusatma\" data-c=\"fmri\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#uzamsal-yumusatma\">Spatial smoothing<\/a><\/h2><span class=\"mrg-abbr\">FWHM<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Uzamsal yumu\u015fatma<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Blurring the image with a Gaussian kernel. The kernel width is given as full width at half maximum (FWHM), often 4\u20138 mm. It raises SNR and evens out anatomical differences across subjects, at the cost of spatial detail.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-fmri\" data-cat=\"fmri\"><span class=\"mrg-dot\"><\/span>Functional MRI<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#on-isleme\" data-go=\"on-isleme\">Preprocessing<\/a><a href=\"#mvpa\" data-go=\"mvpa\">Multivoxel pattern analysis<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"sar\" data-c=\"guvenlik\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#sar\">Specific absorption rate<\/a><\/h2><span class=\"mrg-abbr\">SAR<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> \u00d6zg\u00fcl so\u011furma oran\u0131<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">The rate at which the body absorbs RF energy, in W\/kg. The scanner monitors SAR continuously to limit tissue heating. In normal operating mode the whole-body limit is 2 W\/kg.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-guvenlik\" data-cat=\"guvenlik\"><span class=\"mrg-dot\"><\/span>Safety<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#rf-darbesi\" data-go=\"rf-darbesi\">RF pulse<\/a><a href=\"#paralel-iletim\" data-go=\"paralel-iletim\">Parallel transmission<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"spin\" data-c=\"fizik\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#spin\">Spin<\/a><\/h2><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<p class=\"mrg-def\">The quantum-mechanical angular momentum of a nucleus. Because of its spin, the hydrogen nucleus (a proton) behaves like a tiny magnet and interacts with an external magnetic field.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-fizik\" data-cat=\"fizik\"><span class=\"mrg-dot\"><\/span>Basic physics<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#net-manyetizasyon\" data-go=\"net-manyetizasyon\">Net magnetization<\/a><a href=\"#presesyon\" data-go=\"presesyon\">Precession<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"spin-eko\" data-c=\"sekans\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#spin-eko\">Spin echo<\/a><\/h2><span class=\"mrg-abbr\">SE<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Spin eko<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A sequence that forms an echo with a 180\u00b0 refocusing pulse after a 90\u00b0 excitation. It undoes field inhomogeneity effects, so it gives T2 contrast and is more robust to distortion from metal and air.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-sekans\" data-cat=\"sekans\"><span class=\"mrg-dot\"><\/span>Sequences and contrast<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#eko\" data-go=\"eko\">Echo<\/a><a href=\"#hizli-spin-eko\" data-go=\"hizli-spin-eko\">Turbo or fast spin echo<\/a><a href=\"#gradyan-eko\" data-go=\"gradyan-eko\">Gradient echo<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"durum-uzayi-modeli\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#durum-uzayi-modeli\">State-space model<\/a><\/h2><span class=\"mrg-abbr\">SSM, Mamba<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Durum uzay\u0131 modeli<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A network type that processes long sequences with compute that grows linearly with input length. Selective state-space models such as Mamba offer transformer-like long-range context at lower cost and are now used in medical image synthesis and reconstruction.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#donusturucu\" data-go=\"donusturucu\">Transformer<\/a><a href=\"#dikkat-mekanizmasi\" data-go=\"dikkat-mekanizmasi\">Attention mechanism<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"kontrast-istatistik\" data-c=\"fmri\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#kontrast-istatistik\">Statistical contrast<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> \u0130statistiksel kontrast<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A linear combination of GLM coefficients that expresses the question being tested, for example &quot;faces minus houses&quot;. It yields a t or F statistic per voxel, shown as a statistical map.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-fmri\" data-cat=\"fmri\"><span class=\"mrg-dot\"><\/span>Functional MRI<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#glm\" data-go=\"glm\">General linear model<\/a><a href=\"#coklu-karsilastirma\" data-go=\"coklu-karsilastirma\">Multiple comparisons correction<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"yapisal-baglantisallik\" data-c=\"difuzyon\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#yapisal-baglantisallik\">Structural connectivity<\/a><\/h2><span class=\"mrg-abbr\">konnektom<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Yap\u0131sal ba\u011flant\u0131sall\u0131k<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Turning tractography pathways into a connection matrix between atlas regions. The brain&#039;s network structure is studied with graph-theory measures.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-difuzyon\" data-cat=\"difuzyon\"><span class=\"mrg-dot\"><\/span>Diffusion MRI and analysis<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#traktografi\" data-go=\"traktografi\">Tractography<\/a><a href=\"#atlas\" data-go=\"atlas\">Atlas and parcellation<\/a><a href=\"#fonksiyonel-baglantisallik\" data-go=\"fonksiyonel-baglantisallik\">Functional connectivity<\/a><a href=\"#cizge-sinir-agi\" data-go=\"cizge-sinir-agi\">Graph neural network<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"super-cozunurluk\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#super-cozunurluk\">Super-resolution<\/a><\/h2><span class=\"mrg-abbr\">SR<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> S\u00fcper \u00e7\u00f6z\u00fcn\u00fcrl\u00fck<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Estimating a higher-resolution image from a lower-resolution one. It is used to obtain thin-slice images from thick-slice scans. Whether the estimated detail is real must be assessed carefully.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#gurultu-giderme\" data-go=\"gurultu-giderme\">Denoising<\/a><a href=\"#halusinasyon\" data-go=\"halusinasyon\">Hallucination<\/a><a href=\"#voksel\" data-go=\"voksel\">Voxel<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"superiletken-miknatis\" data-c=\"donanim\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#superiletken-miknatis\">Superconducting magnet<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> S\u00fcperiletken m\u0131knat\u0131s<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A magnet made of niobium-titanium windings cooled to about 4 kelvin by liquid helium. Current flows without resistance, so the field is always on, also when no scan is running.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-donanim\" data-cat=\"donanim\"><span class=\"mrg-dot\"><\/span>Hardware<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#b0\" data-go=\"b0\">Main magnetic field<\/a><a href=\"#sondurme\" data-go=\"sondurme\">Quench<\/a><a href=\"#helyum\" data-go=\"helyum\">Liquid helium<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"gozetimli-ogrenme\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#gozetimli-ogrenme\">Supervised learning<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> G\u00f6zetimli \u00f6\u011frenme<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A model learning from examples where each input is paired with the correct output. For MR reconstruction this needs fully sampled reference images, which are not always available.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#oz-gozetimli\" data-go=\"oz-gozetimli\">Self-supervised learning<\/a><a href=\"#egitim-test\" data-go=\"egitim-test\">Training, validation and test sets<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"duyarlilik-artefakti\" data-c=\"artefakt\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#duyarlilik-artefakti\">Susceptibility artifact<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Duyarl\u0131l\u0131k artefakt\u0131<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Signal loss and distortion where materials of different magnetic susceptibility meet, such as at air-tissue boundaries or around metal. It is pronounced in gradient echo and EPI.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-artefakt\" data-cat=\"artefakt\"><span class=\"mrg-dot\"><\/span>Artifacts<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#t2-yildiz\" data-go=\"t2-yildiz\">T2* relaxation<\/a><a href=\"#epi-bozulmasi\" data-go=\"epi-bozulmasi\">EPI distortion<\/a><a href=\"#shim\" data-go=\"shim\">Shimming<\/a><a href=\"#spin-eko\" data-go=\"spin-eko\">Spin echo<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"bozulma-duzeltmesi\" data-c=\"difuzyon\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#bozulma-duzeltmesi\">Susceptibility distortion correction<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Duyarl\u0131l\u0131k bozulmas\u0131 d\u00fczeltmesi<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Removing the stretching and compression that field inhomogeneity causes along the phase-encoding direction in EPI. It uses the field estimated from a pair of b = 0 images with reversed phase encoding, or from a field map.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-difuzyon\" data-cat=\"difuzyon\"><span class=\"mrg-dot\"><\/span>Diffusion MRI and analysis<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#epi-bozulmasi\" data-go=\"epi-bozulmasi\">EPI distortion<\/a><a href=\"#alan-haritasi\" data-go=\"alan-haritasi\">Field map<\/a><a href=\"#girdap-akimi\" data-go=\"girdap-akimi\">Eddy current correction<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"swi\" data-c=\"sekans\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#swi\">Susceptibility-weighted imaging<\/a><\/h2><span class=\"mrg-abbr\">SWI<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Duyarl\u0131l\u0131k a\u011f\u0131rl\u0131kl\u0131 g\u00f6r\u00fcnt\u00fcleme<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A method that combines magnitude and phase of a gradient echo image to emphasise susceptibility differences. It is very sensitive to veins, microbleeds and iron deposits.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-sekans\" data-cat=\"sekans\"><span class=\"mrg-dot\"><\/span>Sequences and contrast<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#qsm\" data-go=\"qsm\">Quantitative susceptibility mapping<\/a><a href=\"#gradyan-eko\" data-go=\"gradyan-eko\">Gradient echo<\/a><a href=\"#t2-yildiz\" data-go=\"t2-yildiz\">T2* relaxation<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t<h3 class=\"mrg-letter\" id=\"harf-T\">T<\/h3>\t\t\t\t\t<article class=\"mrg-entry\" id=\"t1\" data-c=\"fizik\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#t1\">T1 relaxation<\/a><\/h2><span class=\"mrg-abbr\">T1<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> T1 gev\u015femesi<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">The recovery of longitudinal magnetization after excitation (spin-lattice relaxation). T1 is the time for about 63 percent of that recovery. Fat has a short T1, cerebrospinal fluid a long one.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-fizik\" data-cat=\"fizik\"><span class=\"mrg-dot\"><\/span>Basic physics<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#t2\" data-go=\"t2\">T2 relaxation<\/a><a href=\"#t1-agirlikli\" data-go=\"t1-agirlikli\">T1-weighted image<\/a><a href=\"#tr-zamani\" data-go=\"tr-zamani\">Repetition time<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"t1-agirlikli\" data-c=\"sekans\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#t1-agirlikli\">T1-weighted image<\/a><\/h2><span class=\"mrg-abbr\">T1w<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> T1 a\u011f\u0131rl\u0131kl\u0131 g\u00f6r\u00fcnt\u00fc<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">An image whose contrast comes mainly from T1 differences, made with a short TR and short TE. Fat appears bright and fluid dark, and it is widely used for anatomy.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-sekans\" data-cat=\"sekans\"><span class=\"mrg-dot\"><\/span>Sequences and contrast<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#t1\" data-go=\"t1\">T1 relaxation<\/a><a href=\"#mprage\" data-go=\"mprage\">MPRAGE<\/a><a href=\"#gadolinyum\" data-go=\"gadolinyum\">Gadolinium-based contrast agent<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"t2\" data-c=\"fizik\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#t2\">T2 relaxation<\/a><\/h2><span class=\"mrg-abbr\">T2<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> T2 gev\u015femesi<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">The decay of transverse magnetization as spins lose phase coherence through spin-spin interactions. T2 is the time for the signal to fall to about 37 percent, and in tissue it is always shorter than T1.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-fizik\" data-cat=\"fizik\"><span class=\"mrg-dot\"><\/span>Basic physics<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#t1\" data-go=\"t1\">T1 relaxation<\/a><a href=\"#t2-yildiz\" data-go=\"t2-yildiz\">T2* relaxation<\/a><a href=\"#t2-agirlikli\" data-go=\"t2-agirlikli\">T2-weighted image<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"t2-agirlikli\" data-c=\"sekans\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#t2-agirlikli\">T2-weighted image<\/a><\/h2><span class=\"mrg-abbr\">T2w<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> T2 a\u011f\u0131rl\u0131kl\u0131 g\u00f6r\u00fcnt\u00fc<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">An image whose contrast comes mainly from T2 differences, made with a long TR and long TE. Fluid appears bright, so oedema and many lesions stand out.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-sekans\" data-cat=\"sekans\"><span class=\"mrg-dot\"><\/span>Sequences and contrast<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#t2\" data-go=\"t2\">T2 relaxation<\/a><a href=\"#hizli-spin-eko\" data-go=\"hizli-spin-eko\">Turbo or fast spin echo<\/a><a href=\"#flair\" data-go=\"flair\">Fluid-attenuated inversion recovery<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"t2-yildiz\" data-c=\"fizik\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#t2-yildiz\">T2* relaxation<\/a><\/h2><span class=\"mrg-abbr\">T2*<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> T2* gev\u015femesi<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Faster signal loss that adds the effect of magnetic field inhomogeneity to T2 decay. It sets the contrast of gradient echo images and BOLD fMRI.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-fizik\" data-cat=\"fizik\"><span class=\"mrg-dot\"><\/span>Basic physics<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#t2\" data-go=\"t2\">T2 relaxation<\/a><a href=\"#gradyan-eko\" data-go=\"gradyan-eko\">Gradient echo<\/a><a href=\"#bold\" data-go=\"bold\">Blood oxygenation level dependent signal<\/a><a href=\"#duyarlilik-artefakti\" data-go=\"duyarlilik-artefakti\">Susceptibility artifact<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"tesla\" data-c=\"fizik\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#tesla\">Tesla<\/a><\/h2><span class=\"mrg-abbr\">T<\/span><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<p class=\"mrg-def\">The SI unit of magnetic flux density. Clinical and research scanners mostly run between 1.5 T and 7 T. For comparison, the Earth&#039;s field is about 50 microtesla.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-fizik\" data-cat=\"fizik\"><span class=\"mrg-dot\"><\/span>Basic physics<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#b0\" data-go=\"b0\">Main magnetic field<\/a><a href=\"#sacak-alan\" data-go=\"sacak-alan\">Fringe field<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"test-zamani-uyarlama\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#test-zamani-uyarlama\">Test-time adaptation<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Test zaman\u0131 uyarlamas\u0131<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Briefly updating a trained model at inference time using only the incoming data itself. In reconstruction, aiming for consistency with the measured k-space can reduce the impact of domain shift.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#dagilim-kaymasi\" data-go=\"dagilim-kaymasi\">Domain shift<\/a><a href=\"#veri-tutarliligi\" data-go=\"veri-tutarliligi\">Data consistency<\/a><a href=\"#alan-uyarlamasi\" data-go=\"alan-uyarlamasi\">Domain adaptation<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"tof\" data-c=\"sekans\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#tof\">Time-of-flight angiography<\/a><\/h2><span class=\"mrg-abbr\">TOF<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> U\u00e7u\u015f zaman\u0131 anjiyografisi<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A contrast-free vascular imaging method that relies on fresh, unsaturated blood flowing into the slice and appearing bright.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-sekans\" data-cat=\"sekans\"><span class=\"mrg-dot\"><\/span>Sequences and contrast<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#gradyan-eko\" data-go=\"gradyan-eko\">Gradient echo<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"tbss\" data-c=\"difuzyon\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#tbss\">Tract-based spatial statistics<\/a><\/h2><span class=\"mrg-abbr\">TBSS<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Demet tabanl\u0131 uzaysal istatistik<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A method that projects each subject&#039;s FA map onto a common white-matter skeleton for group comparison. It reduces sensitivity to registration errors and smoothing choices.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-difuzyon\" data-cat=\"difuzyon\"><span class=\"mrg-dot\"><\/span>Diffusion MRI and analysis<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#fa\" data-go=\"fa\">Fractional anisotropy<\/a><a href=\"#grup-analizi\" data-go=\"grup-analizi\">Group-level analysis<\/a><a href=\"#cakistirma\" data-go=\"cakistirma\">Image registration<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"traktografi\" data-c=\"difuzyon\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#traktografi\">Tractography<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Traktografi<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Reconstructing white-matter pathways in 3D by following fibre directions estimated voxel by voxel. It can be deterministic or probabilistic. Results are indirect estimates of real axonal connections and can include false-positive pathways.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-difuzyon\" data-cat=\"difuzyon\"><span class=\"mrg-dot\"><\/span>Diffusion MRI and analysis<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#csd\" data-go=\"csd\">Constrained spherical deconvolution<\/a><a href=\"#dti\" data-go=\"dti\">Diffusion tensor imaging<\/a><a href=\"#yapisal-baglantisallik\" data-go=\"yapisal-baglantisallik\">Structural connectivity<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"egitim-test\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#egitim-test\">Training, validation and test sets<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> E\u011fitim, do\u011frulama ve test k\u00fcmeleri<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Splitting data into three parts: to train the model, to choose its settings and to measure its final performance. If the same person&#039;s data appear in more than one set, performance looks better than it is.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#asiri-ogrenme\" data-go=\"asiri-ogrenme\">Overfitting<\/a><a href=\"#dagilim-kaymasi\" data-go=\"dagilim-kaymasi\">Domain shift<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"donusturucu\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#donusturucu\">Transformer<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> D\u00f6n\u00fc\u015ft\u00fcr\u00fcc\u00fc<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A network architecture that models relations between parts of the input with an attention mechanism. It can capture context between distant regions. For images it works on small patches (vision transformer, ViT).<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#dikkat-mekanizmasi\" data-go=\"dikkat-mekanizmasi\">Attention mechanism<\/a><a href=\"#durum-uzayi-modeli\" data-go=\"durum-uzayi-modeli\">State-space model<\/a><a href=\"#temel-model\" data-go=\"temel-model\">Foundation model<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"hizli-spin-eko\" data-c=\"sekans\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#hizli-spin-eko\">Turbo or fast spin echo<\/a><\/h2><span class=\"mrg-abbr\">TSE, FSE<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> H\u0131zl\u0131 spin eko<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A spin echo variant that collects several echoes after one excitation using a train of refocusing pulses. It speeds up the scan by the echo train length and is the standard way to acquire T2-weighted images.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-sekans\" data-cat=\"sekans\"><span class=\"mrg-dot\"><\/span>Sequences and contrast<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#spin-eko\" data-go=\"spin-eko\">Spin echo<\/a><a href=\"#t2-agirlikli\" data-go=\"t2-agirlikli\">T2-weighted image<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t<h3 class=\"mrg-letter\" id=\"harf-U\">U<\/h3>\t\t\t\t\t<article class=\"mrg-entry\" id=\"u-net\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#u-net\">U-Net<\/a><\/h2><span class=\"mrg-abbr\">U-Net<\/span><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<p class=\"mrg-def\">A convolutional network that first downsamples the image to gather context, then upsamples to recover detail, with skip connections between the two paths. It was designed for medical image segmentation and also serves as the backbone of many reconstruction and diffusion models.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#cnn\" data-go=\"cnn\">Convolutional neural network<\/a><a href=\"#bolutleme\" data-go=\"bolutleme\">Segmentation<\/a><a href=\"#difuzyon-modeli\" data-go=\"difuzyon-modeli\">Diffusion model<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"ultra-yuksek-alan\" data-c=\"donanim\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#ultra-yuksek-alan\">Ultra-high field<\/a><\/h2><span class=\"mrg-abbr\">UHF, 7 T<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Ultra y\u00fcksek alan<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Field strengths of 7 tesla and above. They give higher SNR and resolution, while B\u2081 non-uniformity, SAR and susceptibility artifacts become more prominent.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-donanim\" data-cat=\"donanim\"><span class=\"mrg-dot\"><\/span>Hardware<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#b0\" data-go=\"b0\">Main magnetic field<\/a><a href=\"#paralel-iletim\" data-go=\"paralel-iletim\">Parallel transmission<\/a><a href=\"#sar\" data-go=\"sar\">Specific absorption rate<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"belirsizlik\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#belirsizlik\">Uncertainty estimation<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Belirsizlik kestirimi<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Measuring how far a model&#039;s output can be trusted, at voxel or image level. It shows readers which regions deserve a careful look.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#halusinasyon\" data-go=\"halusinasyon\">Hallucination<\/a><a href=\"#aciklanabilirlik\" data-go=\"aciklanabilirlik\">Explainability<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"eksik-ornekleme\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#eksik-ornekleme\">Undersampling<\/a><\/h2><span class=\"mrg-abbr\">R<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Eksik \u00f6rnekleme<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">Measuring only part of k-space to speed up a scan. The speed-up is given as the factor R. The sampling mask that decides which points are measured directly affects reconstruction quality and can itself be learned.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#k-uzayi\" data-go=\"k-uzayi\">k-space<\/a><a href=\"#paralel-goruntuleme\" data-go=\"paralel-goruntuleme\">Parallel imaging<\/a><a href=\"#sikistirilmis-algilama\" data-go=\"sikistirilmis-algilama\">Compressed sensing<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t<h3 class=\"mrg-letter\" id=\"harf-V\">V<\/h3>\t\t\t\t\t<article class=\"mrg-entry\" id=\"gorme-dil-modeli\" data-c=\"yz\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#gorme-dil-modeli\">Vision-language model<\/a><\/h2><span class=\"mrg-abbr\">VLM<\/span><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> G\u00f6rme-dil modeli<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A model that processes images and text together. It can be used to match radiology reports with images, answer questions about an image or draft a report.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-yz\" data-cat=\"yz\"><span class=\"mrg-dot\"><\/span>Artificial intelligence<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#temel-model\" data-go=\"temel-model\">Foundation model<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t\t<article class=\"mrg-entry\" id=\"voksel\" data-c=\"goruntu\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#voksel\">Voxel<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Voksel<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">The smallest volume element of an image. Its size is the field of view divided by the matrix, times slice thickness. Smaller voxels give less signal.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-goruntu\" data-cat=\"goruntu\"><span class=\"mrg-dot\"><\/span>Image formation<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#fov\" data-go=\"fov\">Field of view<\/a><a href=\"#matris\" data-go=\"matris\">Matrix<\/a><a href=\"#kesit-kalinligi\" data-go=\"kesit-kalinligi\">Slice thickness<\/a><a href=\"#snr\" data-go=\"snr\">Signal-to-noise ratio<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t<h3 class=\"mrg-letter\" id=\"harf-Z\">Z<\/h3>\t\t\t\t\t<article class=\"mrg-entry\" id=\"fermuar-artefakti\" data-c=\"artefakt\">\n\t\t\t\t\t\t<div class=\"mrg-head\"><h2><a href=\"#fermuar-artefakti\">Zipper artifact<\/a><\/h2><\/div>\n\t\t\t\t\t\t<p class=\"mrg-other\"><b>Turkish:<\/b> Fermuar artefakt\u0131<\/p>\t\t\t\t\t\t<p class=\"mrg-def\">A zipper-like line running across the image. It usually comes from outside RF interference, for example a door left open or an electronic device brought into the room.<\/p>\n\t\t\t\t\t\t<div class=\"mrg-meta\">\n\t\t\t\t\t\t\t<button type=\"button\" class=\"mrg-cat mrg-c-artefakt\" data-cat=\"artefakt\"><span class=\"mrg-dot\"><\/span>Artifacts<\/button>\n\t\t\t\t\t\t\t<span class=\"mrg-rel\">Related: <a href=\"#rf-kafes\" data-go=\"rf-kafes\">RF shield<\/a><\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/article>\n\t\t\t\t\t\t\t\t\t<div class=\"mrg-empty\" hidden>No terms match this search.<br><button type=\"button\" class=\"mrg-reset\">Clear filters<\/button><\/div>\n\t\t\t<\/div>\n\t\t\t<nav class=\"mrg-az\" aria-label=\"A\u2013Z\">\n\t\t\t\t\t\t\t\t\t<a href=\"#harf-%23\">#<\/a>\n\t\t\t\t\t\t\t\t\t<a href=\"#harf-A\">A<\/a>\n\t\t\t\t\t\t\t\t\t<a href=\"#harf-B\">B<\/a>\n\t\t\t\t\t\t\t\t\t<a href=\"#harf-C\">C<\/a>\n\t\t\t\t\t\t\t\t\t<a href=\"#harf-D\">D<\/a>\n\t\t\t\t\t\t\t\t\t<a href=\"#harf-E\">E<\/a>\n\t\t\t\t\t\t\t\t\t<a href=\"#harf-F\">F<\/a>\n\t\t\t\t\t\t\t\t\t<a href=\"#harf-G\">G<\/a>\n\t\t\t\t\t\t\t\t\t<a href=\"#harf-H\">H<\/a>\n\t\t\t\t\t\t\t\t\t<a href=\"#harf-I\">I<\/a>\n\t\t\t\t\t\t\t\t\t<a href=\"#harf-K\">K<\/a>\n\t\t\t\t\t\t\t\t\t<a href=\"#harf-L\">L<\/a>\n\t\t\t\t\t\t\t\t\t<a href=\"#harf-M\">M<\/a>\n\t\t\t\t\t\t\t\t\t<a href=\"#harf-N\">N<\/a>\n\t\t\t\t\t\t\t\t\t<a href=\"#harf-O\">O<\/a>\n\t\t\t\t\t\t\t\t\t<a href=\"#harf-P\">P<\/a>\n\t\t\t\t\t\t\t\t\t<a href=\"#harf-Q\">Q<\/a>\n\t\t\t\t\t\t\t\t\t<a href=\"#harf-R\">R<\/a>\n\t\t\t\t\t\t\t\t\t<a href=\"#harf-S\">S<\/a>\n\t\t\t\t\t\t\t\t\t<a href=\"#harf-T\">T<\/a>\n\t\t\t\t\t\t\t\t\t<a href=\"#harf-U\">U<\/a>\n\t\t\t\t\t\t\t\t\t<a href=\"#harf-V\">V<\/a>\n\t\t\t\t\t\t\t\t\t<a href=\"#harf-Z\">Z<\/a>\n\t\t\t\t\t\t\t<\/nav>\n\t\t<\/div>\n\t\t<p class=\"mrg-note\">This page is for general information. The rules for anyone scanning at UMRAM are on the <a href=\"https:\/\/umram.bilkent.edu.tr\/index.php\/mr-safety-and-user-training\/\">MR Safety and User Training<\/a> page.<\/p>\n\t<\/div>\n\t\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":9,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-13396","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/umram.bilkent.edu.tr\/index.php\/wp-json\/wp\/v2\/pages\/13396","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/umram.bilkent.edu.tr\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/umram.bilkent.edu.tr\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/umram.bilkent.edu.tr\/index.php\/wp-json\/wp\/v2\/users\/9"}],"replies":[{"embeddable":true,"href":"https:\/\/umram.bilkent.edu.tr\/index.php\/wp-json\/wp\/v2\/comments?post=13396"}],"version-history":[{"count":1,"href":"https:\/\/umram.bilkent.edu.tr\/index.php\/wp-json\/wp\/v2\/pages\/13396\/revisions"}],"predecessor-version":[{"id":13399,"href":"https:\/\/umram.bilkent.edu.tr\/index.php\/wp-json\/wp\/v2\/pages\/13396\/revisions\/13399"}],"wp:attachment":[{"href":"https:\/\/umram.bilkent.edu.tr\/index.php\/wp-json\/wp\/v2\/media?parent=13396"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}