Abstract #0390

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

Yudu Li^1,2, Kihwan Kim^3,4, Bryan Clifford^1,2, Rong Guo^1,2, Yuning Gu^3,4, Zhi-Pei Liang^1,2, and Xin Yu^3,4,5,6

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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