Regularizing GRAPPA using simultaneous sparsity to recover de-noised images
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Adalsteinsson_Regularizing GRAPPA.pdf
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Author(s)
Goyal, Vivek K.
Polimeni, Jonathan R.
Grady, Leo
Wald, Lawrence L.
Adalsteinsson, Elfar
Weller, Daniel Stuart
Date Issued
August 2011
Journal
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
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1117/12.896655
