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SEMINER

 

 

MRG Görüntü Oluşturma Algorithması:

Compressed Sensing & Quantitative Susceptibility Mapping

 

 


Berkin Bilgic

Doktora Öğrencisi, Magnetic Resonance Imaging Grup

Massachusetts Institute of Technology

 

Cuma: 10 Haziran 2011

Yer: UMRAM, Cyberpark, Blok C, 2inci kat Saat: 13:30 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.

web: web.mit.edu/berkin/www

 

Bilkent Laboratuvar Lisesi (BLIS) öğrencileri Psikoloji hocaları ile birlikte UMRAMı ziyaret edip beyinin nasıl çalıştiğını anlamak üzere yapılan araştırmalar hakkında bilgi aldılar.

 

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Yale, SUNY, Acıbadem, Cerrahpaşa, Hacettepe ve Bilkent Üniversitelerinden araştırmacılar beyin gelişiminde malformasyona neden olan genetik bozukluğun kaynağını bulmak için işbirliği yaptılar. Çalışma Nature Genetics 'de yayımlandı. http://www.nature.com/doifinder/10.1038/ng.836

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