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Proceedings Paper

Low-rank + sparse (L+S) reconstruction for accelerated dynamic MRI with seperation of background and dynamic components
Author(s): Ricardo Otazo; Daniel K. Sodickson; Emmanuel J. Candès
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Paper Abstract

L+S matrix decomposition finds the low-rank (L) and sparse (S) components of a matrix M by solving the following convex optimization problem: min‖L‖*L+S matrix decomposition finds the low-rank (L) and sparse (S) components of a matrix M by solving the following convex optimization problem: ‖L ‖* + λ‖S‖1, subject to M=L+S, where ‖L‖* is the nuclear-norm or sum of singular values of L and ‖S‖1 is the 11-norm| or sum of absolute values of S. This work presents the application of the L+S decomposition to reconstruct incoherently undersampled dynamic MRI data as a superposition of a slowly or coherently changing background and sparse innovations. Feasibility of the method was tested in several accelerated dynamic MRI experiments including cardiac perfusion, time-resolved peripheral angiography and liver perfusion using Cartesian and radial sampling. The high acceleration and background separation enabled by L+S reconstruction promises to enhance spatial and temporal resolution and to enable background suppression without the need of subtraction or modeling.

Paper Details

Date Published: 26 September 2013
PDF: 8 pages
Proc. SPIE 8858, Wavelets and Sparsity XV, 88581Z (26 September 2013); doi: 10.1117/12.2023359
Show Author Affiliations
Ricardo Otazo, New York Univ. School of Medicine (United States)
Daniel K. Sodickson, New York Univ. School of Medicine (United States)
Emmanuel J. Candès, Stanford Univ. (United States)


Published in SPIE Proceedings Vol. 8858:
Wavelets and Sparsity XV
Dimitri Van De Ville; Vivek K. Goyal; Manos Papadakis, Editor(s)

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