Groupwise Registration with Physics-Informed Test-Time Adaptation on Multi-Parametric Cardiac MRI
Xinqi Li (Biomedical Imaging Research Institute Los Angeles, Technische Universität München, Helmholtz Association, Berlin)
Yi Zhang (Helmholtz Association, Berlin)
Li Ting Huang (Biomedical Imaging Research Institute Los Angeles)
Hsiao Huang Chang (Taipei Veterans General Hospital, Taipei)
Thoralf Niendorf (Helmholtz Association, Berlin)
Min Chi Ku (Helmholtz Association, Berlin)
Qian Tao (TU Delft - Applied Sciences)
Hsin Jung Yang (Biomedical Imaging Research Institute Los Angeles)
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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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File under embargo until 01-11-2026