NDLI: Compressed sensing of diffusion MRI data using spatial regularization and positivity constraints

Content Provider	IEEE Xplore Digital Library
Author	Dolui, S. Michailovich, O.V. Rathi, Y.
Copyright Year	2011
Description	Author affiliation: Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA (Rathi, Y.) \|\| Department of Electrical and Computer Engineering, University of Waterloo, Ontario, N2L 3G1, Canada (Dolui, S.; Michailovich, O.V.)
Abstract	In recent years, there has been an ever increasing number of works reporting the successful application of the theory of compressed sensing (CS) to the problem of time-efficient reconstruction of MRI scans. The CS theory seems to be particularly advantageous in application to diffusion MRI (dMRI), where, for the same region of interest, a number of MRI scans need to be acquired in order to assess the strength of water diffusion along different spatial directions. In this paper, we propose a CS-based reconstruction method which allows a substantial reduction in the number of diffusion encoding gradients required for reliable estimation of high angular resolution diffusion imaging signals. Specifically, the method performs a CS-based reconstruction in the diffusion domain subject to two additional constraints, namely: 1) the diffusion signals have to be spatially regular, and 2) the diffusion signals have to be non-negative valued. Additionally, we detail an efficient numerical solution based on variable splitting and proximity operations, which can be used to perform the proposed reconstruction. The paper is concluded with experimental results which support the practical value of our methodology.
Starting Page	1597
Ending Page	1601
File Size	567440
Page Count	5
File Format	PDF
ISBN	9781424441273
ISSN	19457928
DOI	10.1109/ISBI.2011.5872708
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-03-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image reconstruction Magnetic resonance imaging Compressed sensing Image resolution Arrays Estimation compressed sensing Diffusion MRI HARDI sparse representations spherical ridgelets total variation
Content Type	Text
Resource Type	Article

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