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- Title
Fast reconstruction for multichannel compressed sensing using a hierarchically semiseparable solver.
- Authors
Cauley, Stephen F.; Xi, Yuanzhe; Bilgic, Berkin; Xia, Jianlin; Adalsteinsson, Elfar; Balakrishnan, Venkataramanan; Wald, Lawrence L.; Setsompop, Kawin
- Abstract
Purpose The adoption of multichannel compressed sensing (CS) for clinical magnetic resonance imaging (MRI) hinges on the ability to accurately reconstruct images from an undersampled dataset in a reasonable time frame. When CS is combined with SENSE parallel imaging, reconstruction can be computationally intensive. As an alternative to iterative methods that repetitively evaluate a forward CS+SENSE model, we introduce a technique for the fast computation of a compact inverse model solution. Methods A recently proposed hierarchically semiseparable (HSS) solver is used to compactly represent the inverse of the CS+SENSE encoding matrix to a high level of accuracy. To investigate the computational efficiency of the proposed HSS-Inverse method, we compare reconstruction time with the current state-of-the-art. In vivo 3T brain data at multiple image contrasts, resolutions, acceleration factors, and number of receive channels were used for this comparison. Results The HSS-Inverse method allows for
- Publication
Magnetic Resonance in Medicine, 2015, Vol 73, Issue 3, p1034
- ISSN
0740-3194
- Publication type
Article
- DOI
10.1002/mrm.25222