Parallel MR Image Reconstruction Using Augmented Lagrangian Methods
dc.contributor.author | Ramani, Sathish | en_US |
dc.contributor.author | Fessler, Jeffrey A. | en_US |
dc.date.accessioned | 2011-08-18T18:20:53Z | |
dc.date.available | 2011-08-18T18:20:53Z | |
dc.date.issued | 2010-11-18 | en_US |
dc.identifier.citation | Ramani, S.; Fessler, J.A. (2011). "Parallel MR Image Reconstruction Using Augmented Lagrangian Methods." IEEE Transactions on Medical Imaging 30(3): 694-706. <http://hdl.handle.net/2027.42/85846> | en_US |
dc.identifier.issn | 0278-0062 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/85846 | |
dc.description.abstract | Magnetic resonance image (MRI) reconstruction using SENSitivity Encoding (SENSE) requires regularization to suppress noise and aliasing effects. Edge-preserving and sparsity-based regularization criteria can improve image quality, but they demand computation-intensive nonlinear optimization. In this paper, we present novel methods for regularized MRI reconstruction from undersampled sensitivity encoded data-SENSE-reconstruction-using the augmented Lagrangian (AL) framework for solving large-scale constrained optimization problems. We first formulate regularized SENSE-reconstruction as an unconstrained optimization task and then convert it to a set of (equivalent) constrained problems using variable splitting. We then attack these constrained versions in an AL framework using an alternating minimization method, leading to algorithms that can be implemented easily. The proposed methods are applicable to a general class of regularizers that includes popular edge-preserving (e.g., total-variation) and sparsity-promoting (e.g., -norm of wavelet coefficients) criteria and combinations thereof. Numerical experiments with synthetic and in vivo human data illustrate that the proposed AL algorithms converge faster than both general-purpose optimization algorithms such as nonlinear conjugate gradient (NCG) and state-of-the-art MFISTA. | en_US |
dc.publisher | IEEE | en_US |
dc.title | Parallel MR Image Reconstruction Using Augmented Lagrangian Methods | 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 | Department of Electrical Engineering and Computer Science. | en_US |
dc.identifier.pmid | 21095861 | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/85846/1/Fessler4.pdf | |
dc.identifier.doi | 10.1109/TMI.2010.2093536 | en_US |
dc.identifier.source | IEEE Transactions on Medical Imaging | en_US |
dc.owningcollname | Electrical Engineering and Computer Science, Department of (EECS) |
Files in this item
Remediation of Harmful Language
The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.
Accessibility
If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.