Regularizing GRAPPA using simultaneous sparsity to recover de-noised images
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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.
Date issued
2011-08Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer ScienceJournal
Proceedings of Wavelets and Sparsity XIV, Conference 2011
Publisher
Society of Photo-optical Instrumentation Engineers
Citation
Weller, Daniel S. et al. “Regularizing GRAPPA Using Simultaneous Sparsity to Recover De-noised Images.” Wavelets and sparsity XIV, 21-24 August 2011, San Diego, California, United States. 81381M–81381M–9. (Proceedings of the SPIE ; v. 8138). Web. © 2011 SPIE.
Version: Final published version
ISBN
9780819487483
0819487481
ISSN
0277-786X