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Abstract #0911

Accelerating Low-Rank Tensor Model Based Dynamic 31P-MRSI of Ischemia-Reperfusion in Rat at 9.4T

Bryan Clifford1,2, Yuning Gu3,4, Yudu Li1,2, Yuchi Liu3,4, Fan Lam2, Zhi-Pei Liang1,2, and Xin Yu3,4,5,6

1Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States, 2Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States, 3Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States, 4Case Center for Imaging Research, Case Western Reserve University, Cleveland, OH, United States, 5Department of Radiology, Case Western Reserve University, Cleveland, OH, United States, 6Department of Physiology and Biophysics, Case Western Reserve University, Cleveland, OH, United States

Dynamic 31P-MRS/MRSI is often used to assess mitochondrial oxidative capacity in skeletal muscle by monitoring the depletion and recovery of the phosphocreatine concentration during ischemia-reperfusion experiments. In animal models, standard methods are unable to provide the spatiotemporal resolution needed to discern spatial heterogeneity of the recovery process ($$$<$$$10 s/frame, $$$\approx$$$1 mm3 per voxel). To address this problem, we have improved a recently proposed low-rank tensor based method for accelerated high-resolution dynamic 31P-MRSI to provide in vivo results with 1.5x1.5x2 mm3 nominal spatial resolution, 36 ppm spectral bandwidth, 0.14 ppm spectral resolution, and 5.1 s temporal resolution.

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