Oktay Algin of Ataturk Hospital authored an American Journal of Neuroradiology article using the resourses at UMRAM facilities. The citation of the article is "Algin O., Turkbey B. Evaluation of aqueductal stenosis by three-dimensional sampling perfection with application optimized contrasts using different flip-angle evolutions (3D-SPACE) sequence: Preliminary results with 3 Tesla MRI. American Journal of Neuroradiology AJNR 2011".




High-Resolution Diffusion MRI / Magnetic Particle Imaging




University of California, Berkeley


Tuesday, July 19th, 2011. 13:30 @EE-314




Abstract: Diffusion MRI is a magnetic resonance imaging (MRI) method that provides information about random microscopic motion of water molecules in biological tissues. In addition to providing a higher sensitivity for the diagnosis of white matter related diseases such as stroke and multiple sclerosis, and producing connectivity maps of the brain, it is a promising prognostic tool in the assessment and treatment response monitoring of cancer in the body.


While high in-plane resolution is desirable for all diffusion MRI applications, it is particularly essential for the diffusion-weighted imaging (DWI) of small structures. In the first part of this talk, I will present advanced pulse sequence methods to overcome the problems associated with and enable high-resolution diffusion MRI of targeted regions. In addition to validating the performance of the proposed methods outside the central nervous system, example cases from an extensive clinical evaluation of the high-resolution diffusion MRI of spinal cord will be presented.


In the second part of my talk, I will introduce magnetic particle imaging (MPI): a new and powerful imaging modality with high contrast, high resolution and high sensitivity in detecting the spatial distribution of magnetic tracers in animals and human body. MPI exploits the nonlinear response of super-paramagnetic iron oxide nanoparticles to oscillating magnetic fields. With application ranging from in vivo stem cell tracking to angiography, MPI promises superior contrast and sensitivity over existing medical imaging modalities.


BIO: Emine Ülkü Sarıtaş graduated with a B.S. degree in Electrical & Electronics Engineering from Bilkent University in 2002. She received her Ph.D. degree in Electrical Engineering at Stanford University in 2009, working under the supervision of Prof. Dwight Nishimura on high-resolution diffusion magnetic resonance imaging. She is currently a postdoctoral fellow in Prof. Steven Conolly’s Imaging Systems Lab in the Department of Bioengineering at the University of California, Berkeley. Her current research focuses on the development of magnetic particle imaging techniques, as well as their safety and image reconstruction algorithms.




Imaging and Analysis Strategies for Rapid MRI


University of California, Berkeley

Monday, July 18th, 2011. 13:30 @EE-314

Abstract: Magnetic resonance imaging (MRI) is a powerful modality that depicts the morphology and function of biological tissues noninvasively. Recent hardware developments have opened a venue for reducing the relatively long scan times that typically constrain the spatiotemporal resolution of MRI. As a result, there has been growing interest in rapid imaging and analysis methods that take full advantage of these advances.

In the first part of the talk, I will present fast imaging strategies that employ state-of-the-art steady-state pulse sequences and efficient parallel-imaging/compressed-sensing reconstructions. These strategies can achieve substantial improvements in image quality for numerous applications such as cardiac imaging, positive-contrast cellular imaging, and high-resolution peripheral angiography.

In the second part, I will describe sophisticated post-processing algorithms devised for rapid event-related fMRI. These modern statistical tools are used to build quantitative models that can accurately predict the brain’s response during natural vision. In addition to advancing our understanding of the visual system, these models can also be utilized for reliably decoding brain activity, i.e., mind reading.

BIO: Tolga Çukur graduated from Bilkent University in 2003 with a B.S. degree in Electrical Engineering. He continued his studies at Stanford University under the supervision of Prof. Dwight G. Nishimura, and received his Ph.D. degree in Electrical Engineering in 2009. He is currently a postdoctoral fellow in Prof. Jack L. Gallant’s lab in the Helen Wills Neuroscience Institute at the University of California, Berkeley.

His research interests include rapid data acquisition, image reconstruction, and statistical analysis strategies for magnetic resonance imaging (MRI), with a broad range of applications including angiographic, cardiac, cellular, and functional imaging. His current work focuses on building quantitative models of the human visual system during natural stimulation, using functional MRI measurements.

MRI Image Reconstruction Algorithms:
Compressed Sensing & Quantitative Susceptibility Mapping
Berkin Bilgic
PhD Candidate, Magnetic Resonance Imaging Group
Massachusetts Institute of Technology
Friday, June 10, 2011
At UMRAM, Cyberpark ,Block C, 2nd Floor on13:30 pm
Nonlinear reconstruction techniques that involve regularization gained substantial popularity in medical imaging community recently. This popularity is based on the demonstrated ability of these algorithms to reconstruct data sampled below the Nyquist rate, thus reducing scan times or making certain imaging applications more practical.
This talk presents a Bayesian Compressed Sensing algorithm that exploits similarities between MRI images obtained at different contrast settings and reconstructs them jointly from measurements below the Nyquist rate. The joint inference problem is formulated in a hierarchical Bayesian setting, wherein the variance of image gradients across contrasts for a single voxel is represented with a single hyperparameter. All of the images from the same anatomical region, but with different contrast properties, contribute to the estimation of the hyperparameters, and once they are found, the Fourier space data belonging to each image are used independently to infer the image gradients. Examples demonstrate improved reconstruction quality (up to a factor of 4 in root-mean-square error) compared to the state of the art compressed sensing algorithm.
An immediate application of regularized reconstruction is in Quantitative Susceptibility Mapping (QSM). Iron concentration is strongly correlated with the tissue susceptibility, hence QSM has useful applications such as quantifying tissue iron deposition in vivo or estimating vessel oxygenation. We introduce a QSM algorithm, calledL1-QSM, which estimates the tissue susceptibility based on MRI signal phase. This algorithm solves for the underlying susceptibility distribution that gives rise to the observed signal phase by placing sparsity inducing priors on the susceptibility map in spatial gradient domain. For validation, L1-QSM was tested on a dataset collected from young and elderly subjects, and compared well with publishedpostmortem measurements. Results indicate that the elderly group have significantly more iron in striatal and brain stem ROIs than the young group, offering an explanation to slowness in motor tasks with advancing age.
Short bio:
Berkin received his B.S. degrees in Electrical and Electronics Engineering and Physics from Bogazici University in 2008, and his S.M. degree in Electrical Engineering and Computer Science from MIT in 2010. He joined the MRI group in the Research Laboratory of Electronics in February 2010, and is currently pursuing his Ph.D. degree.His main research interests include developing image reconstruction algorithms to speed up data acquisition, magnetic susceptibility mapping and artifact reduction in spectroscopic imaging.

Bilkent Laboratory and International School students visited UMRAM with their Psychology teach to learn more about investigations on how brain works.

In the 16th annual meeting of the Turkish Magnetic Resonance Society, UMRAM researchers presented 11 scientific studies. Presenters were Oktay Algın, Aslıhan Örs, Davut İbrahim Mahçiçek, Mehmet Can Kerse, Ergin Atalar (as a replacement of Emre Kopanoğlu), Volkan Açıkel, Ali Çağlar Özen, Yiğitcan Eryaman, Esra Abacı, Taner Demir and Özgür Yılmaz. Murat Tümer of Bogazici University presented the catheter design which was developed using UMRAM resources. In addition UMRAM ve Boğaziçi University Life Science Institute opened a stand during the meeting.

Researchers from Yale, SUNY, Acibadem, Cerrahpasa, Hacettepe, and Bilkent Universities have collaborated to find the source of a genetic disorder that causes malformation in brain development. The study was published in Nature Genetics.