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Proceedings Paper

Low-rank modeling of local k-space neighborhoods: from phase and support constraints to structured sparsity
Author(s): Justin P. Haldar
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Paper Abstract

Low-rank modeling of local k-space neighborhoods (LORAKS) is a recent novel framework for constrained MRI reconstruction. LORAKS relies on embedding MRI data into carefully-constructed matrices, which will have low-rank structure when the MRI image has sparse support or slowly-varying phase. Low-rank matrix representation allows MRI images to be reconstructed from undersampled data using modern low-rank matrix techniques, and enables data acquisition strategies that are incompatible with more traditional representations. This paper reviews LORAKS, and describes extensions that allow LORAKS to additionally impose structured transform-domain sparsity constraints (e.g., structured sparsity of the image derivatives or wavelet coefficients).

Paper Details

Date Published: 24 August 2015
PDF: 12 pages
Proc. SPIE 9597, Wavelets and Sparsity XVI, 959710 (24 August 2015); doi: 10.1117/12.2186705
Show Author Affiliations
Justin P. Haldar, Univ. of Southern California (United States)


Published in SPIE Proceedings Vol. 9597:
Wavelets and Sparsity XVI
Manos Papadakis; Vivek K. Goyal; Dimitri Van De Ville, Editor(s)

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