Abstract #0393

Making SPICE Spicier with Sparse Sampling of (k, t)-Space and Learned Subspaces

Rong Guo^1,2, Yudu Li^1,2, Yibo Zhao^1,2, Yao Li^3,4, and Zhi-Pei Liang^1,2

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, ³School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China, ⁴Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, China

SPICE has recently provided a unique capability for simultaneous acquisition of metabolite and water spectroscopic signals. While the water signals are often removed as nuisance components in traditional MRSI experiments, SPICE utilizes the water signals for QSM, MWF mapping, etc. In this work, we further extend SPICE data acquisition to achieve much larger k-space coverage and improve its processing scheme for simultaneous MRSI/QSM/SWI/MWF mapping. In vivo experiments demonstrated that this new scheme improved the accuracy of water/lipid removal, reduced the effects of field inhomogeneity, and achieved higher resolution for QSM, SWI and MWF using the unsuppressed water signals.

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