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Abstract #0390

High-Resolution Dynamic 31P-MRSI of Ischemia-Reperfusion in Rat Using Low-Rank Tensor Model with Deep Learning Priors

Yudu Li1,2, Kihwan Kim3,4, Bryan Clifford1,2, Rong Guo1,2, Yuning Gu3,4, Zhi-Pei Liang1,2, and Xin Yu3,4,5,6
1Department of Electrical 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, 3Department of Biomedical 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 a promising tool for in vivo quantification of mitochondrial oxidative capacity. However, its practical utility is limited by the inherently low SNR of the 31P signal. This work is built upon our recent progress in accelerating dynamic 31P-MRSI using low-rank tensor models. We extended this method by learning the temporal priors with deep generative models and then incorporating them into the reconstruction via an information theoretical framework. This approach enabled high-resolution dynamic 31P-MRSI with 1.5x1.5x2 mm3 nominal spatial resolution and 5.1-sec temporal resolution in capturing the kinetics of metabolite changes in rat hindlimb during a stimulation-recovery protocol.

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