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Abstract #5237

Convolutional Network Based Lineshape Adaptation Improved Subject-Specific Subspace Estimation for MRSI Reconstruction

Ruiyang Zhao1,2, Zepeng Wang2,3, and Fan Lam1,2,3
1Department of Electrical and Computer Engineering, University of illinois, Urbana Chamapign, Champaign, IL, United States, 2Beckman Institute for Advanced Science and Technology, University of illinois Urbana-Champaign, Champaign, IL, United States, 3Department of Bioengineering, University of illinois Urbana-Champaign, Champaign, IL, United States

Synopsis

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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Keywords