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Abstract #3805

B1 Correction for MRSI using Unified Subspace Model for Multi-Center Study

Ziyu Meng1,2, Yibo Zhao3, Chang Xu1, Yudu Li3,4,5, Wen Jin3,6, Bin Bo1, Yingying Tang7, Weijun Tang8, Tianyao Wang9, Zhi-Pei Liang3,6, and Yao Li1,2
1National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy (NERC-AMRT), School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China, 2Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China, 3Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States, 4Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States, 5The National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL, United States, 6Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States, 7Shanghai Mental Health Center, Shanghai Jiao Tong University of Medicine, Shanghai, China, 8Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China, 9Department of Radiology, Renji Hospital, Shanghai Jiao Tong University of Medicine, Shanghai, China

Synopsis

Keywords: System Imperfections, System Imperfections: Measurement & Correction

Motivation: MRSI provides label-free mapping of brain metabolites, but its accuracy is compromised by $$$B_{1}$$$ inhomogeneity.

Goal(s): To develop a learning-based $$$B_{1}$$$ correction method leveraging a unified subspace model derived from multiple centers for MRSI across scanners.

Approach: Non-water-suppressed MRSI and VFA water data were acquired from 129 healthy subjects across three centers. A unified subspace model was built and learned from pre-scanned multi-center training data. The $$$B_{1}$$$ fields for new data were estimated using a maximum-a-posterior Bayesian approach.

Results: In both healthy subjects and stroke patients, the proposed method achieved robust high-quality $$$B_{1}$$$ maps and produced significantly improved neurometabolite maps.

Impact: The proposed $$$B_{1}$$$ correction method will enhance the quantitative accuracy of metabolite measurements and thus enhance the robustness and practical usefulness of MRSI in clinical applications.

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Abstract

Keywords