Keywords: Spectroscopy, Sparse & Low-Rank Models
Motivation: In subspace-based MRSI, there is potential discrepancy between a physics-driven pre-learned subspace for reconstruction and experimental variations for individual experiments.
Goal(s): To develop a strategy to accurately adapt the pre-learned subspace to subject/experiment-dependent spectral variations while preserving weak metabolite signals and avoiding overfitting.
Approach: A CNN-based strategy was proposed, incorporating spatial constraints and a union-of-subspace-based signal separation/protection, to achieve effective subspace adaptation for individual experiments.
Results: Our method achieved lower adaptation errors, better preservation of weak metabolite signals and improved quantification.
Impact: The proposed method minimized the discrepancy between physics-driven subspace and in vivo data, improving subspace-based MRSI reconstruction and quantification.
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