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- Title
Accelerating 4 D flow MRI by exploiting low-rank matrix structure and hadamard sparsity.
- Authors
Valvano, Giuseppe; Martini, Nicola; Huber, Adrian; Santelli, Claudio; Binter, Christian; Chiappino, Dante; Landini, Luigi; Kozerke, Sebastian
- Abstract
Purpose To develop accelerated 4D flow MRI by exploiting low-rank matrix structure and Hadamard sparsity. Theory and Methods 4D flow MRI data can be represented as the sum of a low-rank and a sparse component. To optimize the sparse representation of the data, it is proposed to incorporate a Hadamard transform of the velocity-encoding segments. Retrospectively and prospectively, undersampled data of the aorta of healthy subjects are used to assess the reconstruction accuracy of the proposed method relative to k-t SPARSE-SENSE reconstruction. Image reconstruction from eight-fold prospective undersampling is demonstrated and compared with conventional SENSE imaging. Results Simulation results revealed consistently lower errors in velocity estimation when compared with k-t SPARSE-SENSE. In vivo data yielded reduced error of peak flow with the proposed method relative to k-t SPARSE-SENSE when compared with two-fold SENSE (
- Publication
Magnetic Resonance in Medicine, 2017, Vol 78, Issue 4, p1330
- ISSN
0740-3194
- Publication type
Article
- DOI
10.1002/mrm.26508