Groupwise registration with physics-informed test-time adaptation on multi-parametric cardiac MRI

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Item Type:	Preprint
Title:	Groupwise registration with physics-informed test-time adaptation on multi-parametric cardiac MRI
Creators Name:	Li, Xinqi, Zhang, Yi, Huang, Li-Ting, Chang, Hsiao-Huang, Niendorf, Thoralf, Ku, Min-Chi, Tao, Qian and Yang, Hsin-Jung
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.
Keywords:	Registration, Cardiac MRI, Test-Time Adaptation
Source:	arXiv
Publisher:	Cornell University
Article Number:	2510.26022
Date:	29 October 2025
Official Publication:	https://doi.org/10.48550/arxiv.2510.26022

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