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Tools
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| Item Type: | Conference or Workshop Item |
|---|---|
| Title: | Groupwise registration with physics-informed test-time adaptation on multi-parametric cardiac MRI |
| Creators: |
Li, Xinqi  ORCID: https://orcid.org/0009-0002-4799-8333, Zhang, Yi, Huang, Li-Ting, Chang, Hsiao-Huang, Niendorf, Thoralf ORCID: https://orcid.org/0000-0001-7584-6527, Ku, Min-Chi ORCID: https://orcid.org/0000-0003-0963-2461, 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: | Lecture Notes in Computer Science |
| Title of Book: | Statistical Atlases and Computational Models of the Heart. Regular and CMRxRecon Challenge Papers. 16th International Workshop, STACOM 2025, Held in Conjunction with MICCAI 2025, Daejeon, South Korea, September 27, 2025, Revised Selected Papers |
| ISSN: | 0302-9743 |
| ISBN: | 978-3-032-17733-9 |
| Publisher: | Springer |
| Volume: | 16459 |
| Page Range: | 181-190 |
| Number of Pages: | 10 |
| Date: | 19 May 2026 |
| Official Publication: | https://doi.org/10.1007/978-3-032-17734-6_18 |
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