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Proceedings Paper

Regularizing GRAPPA using simultaneous sparsity to recover de-noised images
Author(s): Daniel S. Weller; Jonathan R. Polimeni; Leo Grady; Lawrence L. Wald; Elfar Adalsteinsson; Vivek K. Goyal
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Paper Abstract

To enable further acceleration of magnetic resonance (MR) imaging, compressed sensing (CS) is combined with GRAPPA, a parallel imaging method, to reconstruct images from highly undersampled data with significantly improved RMSE compared to reconstructions using GRAPPA alone. This novel combination of GRAPPA and CS regularizes the GRAPPA kernel computation step using a simultaneous sparsity penalty function of the coil images. This approach can be implemented by formulating the problem as the joint optimization of the least squares fit of the kernel to the ACS lines and the sparsity of the images generated using GRAPPA with the kernel.

Paper Details

Date Published: 27 September 2011
PDF: 9 pages
Proc. SPIE 8138, Wavelets and Sparsity XIV, 81381M (27 September 2011); doi: 10.1117/12.896655
Show Author Affiliations
Daniel S. Weller, Massachusetts Institute of Technology (United States)
Jonathan R. Polimeni, Massachusetts General Hospital (United States)
Harvard Medical School (United States)
Leo Grady, Siemens Corporate Research (United States)
Lawrence L. Wald, Massachusetts General Hospital (United States)
Harvard Medical School (United States)
Elfar Adalsteinsson, Massachusetts Institute of Technology (United States)
Vivek K. Goyal, Massachusetts Institute of Technology (United States)


Published in SPIE Proceedings Vol. 8138:
Wavelets and Sparsity XIV
Manos Papadakis; Dimitri Van De Ville; Vivek K. Goyal, Editor(s)

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