An Accelerated Iterative Reweighted Least Squares Algorithm for Compressed Sensing MRI
dc.contributor.author | Ramani, Sathish | en_US |
dc.contributor.author | Fessler, Jeffrey A. | en_US |
dc.date.accessioned | 2011-08-18T18:21:13Z | |
dc.date.available | 2011-08-18T18:21:13Z | |
dc.date.issued | 2010-04-14 | en_US |
dc.identifier.citation | Ramani, S.; Fessler, J.A. (2010). "An Accelerated Iterative Reweighted Least Squares Algorithm for Compressed Sensing MRI." IEEE International Symposium on Biomedical Imaging: From Nano to Macro: 257-260. <http://hdl.handle.net/2027.42/85957> | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/85957 | |
dc.description.abstract | Compressed sensing for MRI (CS-MRI) attempts to recover an object from undersampled k-space data by minimizing sparsity-promoting regularization criteria. The iterative reweighted least squares (IRLS) algorithm can perform the minimization task by solving iteration-dependent linear systems, recursively. However, this process can be slow as the associated linear system is often poorly conditioned for ill-posed problems. We propose a new scheme based on the matrix inversion lemma (MIL) to accelerate the solving process. We demonstrate numerically for CS-MRI that our method provides significant speed-up compared to linear and nonlinear conjugate gradient algorithms, thus making it a promising alternative for such applications. | en_US |
dc.publisher | IEEE | en_US |
dc.title | An Accelerated Iterative Reweighted Least Squares Algorithm for Compressed Sensing MRI | en_US |
dc.type | Article | en_US |
dc.subject.hlbsecondlevel | Biomedical Engineering | en_US |
dc.subject.hlbtoplevel | Engineering | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | EECS Dept | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/85957/1/Fessler250.pdf | |
dc.identifier.doi | 10.1109/ISBI.2010.5490364 | en_US |
dc.identifier.source | IEEE International Symposium on Biomedical Imaging: From Nano to Macro | en_US |
dc.owningcollname | Electrical Engineering and Computer Science, Department of (EECS) |
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