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Abstract #1058

Further Accelerating SPICE for Ultrafast MRSI Using Learned Spectral Features

Fan Lam1, Yudu Li1,2, Rong Guo1,2, Bryan Clifford1,2, Xi Peng1, and Zhi-Pei Liang1,2

1Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States, 2Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States

This work presents a new method to incorporate machine learning into SPICE (SPectroscopic Imaging by exploiting spatiospectral CorrElation) to further enhance its data acquisition speeds. The proposed method exploits the significant amount of prior knowledge about the spectral variations of biological tissues, e.g., molecular composition and resonance structures, by devising a novel strategy to learn the molecule-specific spectral features from training data, and incorporating the learned features into a subspace representation of the desired spatiospectral distribution for a general MRSI study. Impressive results have been produced by the proposed method from 1H-MRSI of the brain without any suppression pulses.

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