Groupwise Registration with Physics-Informed Test-Time Adaptation on Multi-parametric Cardiac MRI

• URL: http://arxiv.org/abs/2510.26022v1
• Date: Wed, 29 Oct 2025 23:30:09 GMT
• Title: Groupwise Registration with Physics-Informed Test-Time Adaptation on Multi-parametric Cardiac MRI
• Authors: Xinqi Li, Yi Zhang, Li-Ting Huang, Hsiao-Huang Chang, Thoralf Niendorf, Min-Chi Ku, Qian Tao, Hsin-Jung Yang,
• Abstract summary: Multiparametric mapping MRI has become a viable tool for myocardial tissue characterization.<n> misalignment between multiparametric maps makes pixel-wise analysis challenging.<n>We developed a generalizable physics-informed deep-learning model using test-time adaptation.
• Score: 6.226653778992303
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multiparametric mapping MRI has become a viable tool for myocardial tissue characterization. However, misalignment between multiparametric maps makes pixel-wise analysis challenging. To address this challenge, we developed a generalizable physics-informed deep-learning model using test-time adaptation to enable group image registration across contrast weighted images acquired from multiple physical models (e.g., a T1 mapping model and T2 mapping model). The physics-informed adaptation utilized the synthetic images from specific physics model as registration reference, allows for transductive learning for various tissue contrast. We validated the model in healthy volunteers with various MRI sequences, demonstrating its improvement for multi-modal registration with a wide range of image contrast variability.

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