Abstract #0474

Separation of Macromolecules and Metabolites in Ultrashort-TE MRSI Data with Learned Probabilistic Subspaces

Yibo Zhao^1,2, Yudu Li^1,3, Wen Jin^1,2, Rong Guo^1,4, Wenli Li⁵, Yao Li⁵, Jie Luo⁵, and Zhi-Pei Liang^1,2

¹Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States, ²Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States, ³National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL, United States, ⁴Siemens Medical Solutions USA, Inc., Urbana, IL, United States, ⁵School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China

Synopsis

Keywords: Data Analysis, Spectroscopy, Macromolecule

Separation of macromolecules and metabolites in ultrashort-TE MRSI data has been very difficult due to limited SNR and strong spectral overlap. In this work, we proposed a new solution to the problem using a subspace-based approach aided with long-TE navigator signals. Physics-based prior information was incorporated through pre-learned spectral bases and probability distributions of spatial coefficients. The proposed method has been validated using experimental data from healthy and brain tumor subjects, producing impressive results.

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