Regularizing GRAPPA using simultaneous sparsity to recover de-noised images
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 o...
| Main Authors: | , , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Society of Photo-optical Instrumentation Engineers
2012
|
| Online Access: | http://hdl.handle.net/1721.1/72066 https://orcid.org/0000-0002-7637-2914 |
| _version_ | 1826198692964597760 |
|---|---|
| author | Goyal, Vivek K. Polimeni, Jonathan R. Grady, Leo Wald, Lawrence L. Adalsteinsson, Elfar Weller, Daniel Stuart |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Goyal, Vivek K. Polimeni, Jonathan R. Grady, Leo Wald, Lawrence L. Adalsteinsson, Elfar Weller, Daniel Stuart |
| author_sort | Goyal, Vivek K. |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T11:08:39Z |
| format | Article |
| id | mit-1721.1/72066 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T11:08:39Z |
| publishDate | 2012 |
| publisher | Society of Photo-optical Instrumentation Engineers |
| record_format | dspace |
| spelling | mit-1721.1/720662022-10-01T01:33:20Z Regularizing GRAPPA using simultaneous sparsity to recover de-noised images Goyal, Vivek K. Polimeni, Jonathan R. Grady, Leo Wald, Lawrence L. Adalsteinsson, Elfar Weller, Daniel Stuart Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Adalsteinsson, Elfar Weller, Daniel Stuart Adalsteinsson, Elfar Goyal, Vivek K. 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. National Science Foundation (U.S.) (NSF CAREER 0643836) National Institutes of Health (U.S.) (Grant NIH R01 EB007942) National Institutes of Health (U.S.) (Grant EB006847) National Institutes of Health (U.S.) (NIH NCRR P41 RR014075) Siemens Aktiengesellschaft (Healthcare) 2012-08-09T14:42:52Z 2012-08-09T14:42:52Z 2011-08 Article http://purl.org/eprint/type/ConferencePaper 9780819487483 0819487481 0277-786X http://hdl.handle.net/1721.1/72066 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. https://orcid.org/0000-0002-7637-2914 en_US http://dx.doi.org/10.1117/12.896655 Proceedings of Wavelets and Sparsity XIV, Conference 2011 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. application/pdf Society of Photo-optical Instrumentation Engineers SPIE |
| spellingShingle | Goyal, Vivek K. Polimeni, Jonathan R. Grady, Leo Wald, Lawrence L. Adalsteinsson, Elfar Weller, Daniel Stuart Regularizing GRAPPA using simultaneous sparsity to recover de-noised images |
| title | Regularizing GRAPPA using simultaneous sparsity to recover de-noised images |
| title_full | Regularizing GRAPPA using simultaneous sparsity to recover de-noised images |
| title_fullStr | Regularizing GRAPPA using simultaneous sparsity to recover de-noised images |
| title_full_unstemmed | Regularizing GRAPPA using simultaneous sparsity to recover de-noised images |
| title_short | Regularizing GRAPPA using simultaneous sparsity to recover de-noised images |
| title_sort | regularizing grappa using simultaneous sparsity to recover de noised images |
| url | http://hdl.handle.net/1721.1/72066 https://orcid.org/0000-0002-7637-2914 |
| work_keys_str_mv | AT goyalvivekk regularizinggrappausingsimultaneoussparsitytorecoverdenoisedimages AT polimenijonathanr regularizinggrappausingsimultaneoussparsitytorecoverdenoisedimages AT gradyleo regularizinggrappausingsimultaneoussparsitytorecoverdenoisedimages AT waldlawrencel regularizinggrappausingsimultaneoussparsitytorecoverdenoisedimages AT adalsteinssonelfar regularizinggrappausingsimultaneoussparsitytorecoverdenoisedimages AT wellerdanielstuart regularizinggrappausingsimultaneoussparsitytorecoverdenoisedimages |