Fast reconstruction for multichannel compressed sensing using a hierarchically semiseparable solver
Author(s)Cauley, Stephen F.; Xi, Yuanzhe; Bilgic, Berkin; Xia, Jianlin; Balakrishnan, Venkataramanan; Setsompop, Kawin; Adalsteinsson, Elfar; Wald, Lawrence; ... Show more Show less
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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 math formula speedup when compared to current state-of-the-art reconstruction methods with the same accuracy. Efficient computational scaling is demonstrated for CS+SENSE with respect to image size. The HSS-Inverse method is also shown to have minimal dependency on the number of parallel imaging channels/acceleration factor. Conclusions The proposed HSS-Inverse method is highly efficient and should enable real-time CS reconstruction on standard MRI vendors' computational hardware.
DepartmentHarvard University--MIT Division of Health Sciences and Technology
Magnetic Resonance in Medicine
Cauley, Stephen F.; Xi, Yuanzhe; Bilgic, Berkin et al. “Fast Reconstruction for Multichannel Compressed Sensing Using a Hierarchically Semiseparable Solver.” Magnetic Resonance in Medicine 73, 3 (March 2014): 1034–1040 © 2014 Wiley Periodicals, Inc
Author's final manuscript