Low-rank modeling of local k-space neighborhoods: from phase and support constraints to structured sparsity

Justin P. Haldar

doi:10.1117/12.2186705

24 August 2015 Low-rank modeling of local k-space neighborhoods: from phase and support constraints to structured sparsity

Justin P. Haldar

Author Affiliations +

Proceedings Volume 9597, Wavelets and Sparsity XVI; 959710 (2015) https://doi.org/10.1117/12.2186705
Event: SPIE Optical Engineering + Applications, 2015, San Diego, California, United States

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).

Citation Download Citation

Justin P. Haldar "Low-rank modeling of local k-space neighborhoods: from phase and support constraints to structured sparsity", Proc. SPIE 9597, Wavelets and Sparsity XVI, 959710 (24 August 2015); https://doi.org/10.1117/12.2186705

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