Accelerating 4D flow MRI by exploiting low-rank matrix structure and hadamard sparsity

作者：Valvano Giuseppe^*; Martini Nicola; Huber Adrian; Santelli Claudio; Binter Christian; Chiappino Dante; Landini Luigi; Kozerke Sebastian

来源：Magnetic Resonance in Medicine, 2017, 78(4): 1330-1341.

DOI：10.1002/mrm.26508

• 4D flow MRI
• compressed sensing
• low-rank and sparse decomposition
• complex differences
• Hadamard transform

PurposeTo develop accelerated 4D flow MRI by exploiting low-rank matrix structure and Hadamard sparsity.
Theory and Methods4D 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.
ResultsSimulation 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 ( 2.54.6% versus 10.2 +/- 8.5% in the ascending aorta, 3.6 +/- 8.4% versus 9.2 +/- 9.0% in the descending aorta). Streamline visualization showed more consistent flow fields with the proposed technique relative to the benchmark methods.
ConclusionImage reconstruction by exploiting low-rank structure and Hadamard sparsity of 4D flow MRI data improves the reconstruction accuracy relative to current state-of-the-art methods and holds promise to reduce the long scan times of 4D flow MRI. Magn Reson Med 78:1330-1341, 2017.

• 出版日期2017-10