Parametric dictionary learning for modeling EAP and ODF in diffusion MRI

Sylvain Merlet 1 Emmanuel Caruyer 1 Rachid Deriche 1
1 ATHENA - Computational Imaging of the Central Nervous System
CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : In this work, we propose an original and efficient approach to exploit the ability of Compressed Sensing (CS) to recover Diffusion MRI (dMRI) signals from a limited number of samples while efficiently recovering important diffusion features such as the Ensemble Average Propagator (EAP) and the Orientation Distribution Function (ODF). Some attempts to sparsely represent the diffusion signal have already been performed. However and contrarly to what has been presented in CS dMRI, in this work we propose and advocate the use of a well adapted learned dictionary and show that it leads to a sparser signal estima- tion as well as to an efficient reconstruction of very important diffusion features. We first propose to learn and design a sparse and paramet- ric dictionary from a set of training diffusion data. Then, we propose a framework to analytically estimate in closed form two important diffu- sion features : the EAP and the ODF. Various experiments on synthetic, phantom and human brain data have been carried out and promising results with reduced number of atoms have been obtained on diffusion signal reconstruction, thus illustrating the added value of our method over state-of-the-art SHORE and SPF based approaches.
Keywords : Dictionary Learning Orientation Distribution Function Diffusion MRI Compressed sensing Ensemble Average Propagator recovery Propagator
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Communication dans un congrès
Medical Image Computing and Computer Assisted Intervention (MICCAI), Oct 2012, Nice, France. Springer, 7512, 8 p., 2012, Lecture notes in computer science
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Dernière modification le : mercredi 7 octobre 2015 - 01:16:00
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Sylvain Merlet, Emmanuel Caruyer, Rachid Deriche. Parametric dictionary learning for modeling EAP and ODF in diffusion MRI. Medical Image Computing and Computer Assisted Intervention (MICCAI), Oct 2012, Nice, France. Springer, 7512, 8 p., 2012, Lecture notes in computer science. <hal-00728067>

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