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A computational diffusion MRI and parametric dictionary learning framework for modeling the diffusion signal and its features.

Abstract：

:In this work, we first propose an original and efficient computational framework to model continuous diffusion MRI (dMRI) signals and analytically recover important diffusion features such as the Ensemble Average Propagator (EAP) and the Orientation Distribution Function (ODF). Then, we develop an efficient parametric dictionary learning algorithm and exploit the sparse property of a well-designed dictionary to recover the diffusion signal and its features with a reduced number of measurements. The properties and potentials of the technique are demonstrated using various simulations on synthetic data and on human brain data acquired from 7T and 3T scanners. It is shown that the technique can clearly recover the dMRI signal and its features with a much better accuracy compared to state-of-the-art approaches, even with a small and reduced number of measurements. In particular, we can accurately recover the ODF in regions of multiple fiber crossing, which could open new perspectives for some dMRI applications such as fiber tractography.

journal_name

Med Image Anal

journal_title

Medical image analysis

authors

Merlet S,Caruyer E,Ghosh A,Deriche R

doi

10.1016/j.media.2013.04.011

subject

Has Abstract

pub_date

2013-10-01 00:00:00

pages

830-43

issue

7

eissn

1361-8415

issn

1361-8423

pii

S1361-8415(13)00064-9

journal_volume

17

pub_type

杂志文章

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