Abstract #0911

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

Bryan Clifford^1,2, Yuning Gu^3,4, Yudu Li^1,2, Yuchi Liu^3,4, Fan Lam², Zhi-Pei Liang^1,2, and Xin Yu^3,4,5,6

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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