Abstract #2932

Brain Atlas of Macromolecular Spectroscopic Signals from Ultrashort-TE MRSI

Yibo Zhao¹, Yudu Li^1,2,3, Wen Jin^1,4, Rong Guo^1,5, Yao Li⁶, Jie Luo⁶, and Zhi-Pei Liang^1,4

¹Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Champaign, IL, United States, ²Department of Bioengineering, University of Illinois at Urbana-Champaign, Champaign, IL, United States, ³National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Champaign, IL, United States, ⁴Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Champaign, IL, United States, ⁵Siemens Medical Solutions USA, Inc., St. Louis, MO, United States, ⁶Shanghai Jiao Tong University, Shanghai, China

Synopsis

Keywords: Spectroscopy, Spectroscopy, Macromolecules, Atlas

Motivation: Macromolecules generate broad baselines in short-TE MRSI data, complicating the quantification of metabolite signals.

Goal(s): To develop a brain atlas of macromolecular signals for modelling and effective removal of macromolecule signals in ultrashort-TE FID MRSI at 3T.

Approach: We learned the spectroscopic patterns of macromolecule signals in the human brain across different subjects in the standard MNI space. The learned macromolecule atlas is used as probabilistic distribution constraints for subspace-based macromolecule removal.

Results: Macromolecule atlas was built from a group of healthy subjects (n=45). It was tested using simulation data and experimental data acquired from healthy subjects and tumor patients, producing encouraging results.

Impact: The proposed method may lead to more accurate modelling of macromolecule signals, thus improving the quantification of metabolites in short-TE MRSI.

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