Abstract #2984

Learning-based Separation of Macromolecules and Metabolites in Ultrashort-TE FID MRSI with Auxiliary SE MRSI Data

Yibo Zhao^1,2, Yudu Li^1,3, Wen Jin^1,2, Rong Guo^1,4, 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 Processing, Data Analysis

Motivation: Macromolecules have significant spectral overlap with metabolites, confounding accurate quantification of metabolites in ultrashort-TE MRSI.

Goal(s): To develop a novel method for effective and reliable separation of metabolites and macromolecules from ultrashort-TE FID MRSI data.

Approach: We translated auxiliary macromolecule-free SE metabolite signals to FID signals using a learning-based approach. The translated metabolite reference was incorporated in the spectral model of FID MRSI data through generalized series modelling. Macromolecules signals were modelled with probabilistic subspaces.

Results: The proposed method has been validated using numerical simulation and experimental data from healthy subjects and a tumor patient, producing encouraging results.

Impact: This work provides a novel approach to exploiting the characteristic spectral features in FID and SE MRSI experiments for effective separation of metabolites and macromolecules.

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