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

Motivation: MRSI provides label-free mapping of brain metabolites, but its accuracy is compromised by $B₁$ inhomogeneity. Goal(s): To develop a learning-based $$B₁$ correction method leveraging a unified subspace model derived from multiple centers for MRSI across scanners. Approach: Non-water-suppressed MRSI and VFA water data were acquired from 129 healthy subjects across three centers. A unified subspace model was built and learned from pre-scanned multi-center training data. The $B₁$ fields for new data were estimated using a maximum-a-posterior Bayesian approach. Results: In both healthy subjects and stroke patients, the proposed method achieved robust high-quality $$B₁$ maps and produced significantly improved neurometabolite maps. Impact: The proposed $$B₁$ correction method will enhance the quantitative accuracy of metabolite measurements and thus enhance the robustness and practical usefulness of MRSI in clinical applications.