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

arXiv: https://arxiv.org/abs/2510.26022 · HTML: https://mlsanity.com/paper/2510.26022/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 26 references that are in the map.
- First posted on arXiv 29 Oct 2025.

## Builds on (most cited first, 9 of 9)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [VoxelMorph: A Learning Framework for Deformable Medical Image Registration](https://mlsanity.com/paper/1809.05231/) (2018)
- [Contrastive Test-Time Adaptation](https://mlsanity.com/paper/2204.10377/) (2022)
- [Deep Learning in Medical Image Registration: A Review](https://mlsanity.com/paper/1912.12318/) (2019)
- [Test-Time Training on Nearest Neighbors for Large Language Models](https://mlsanity.com/paper/2305.18466/) (2023)
- [PCMC-T1: Free-breathing myocardial T1 mapping with Physically-Constrained Motion Correction](https://mlsanity.com/paper/2308.11281/) (2023)
- [Deep-Learning based Motion Correction for Myocardial T1 Mapping](https://mlsanity.com/paper/2109.09146/) (2021)
- [Contrast-Agnostic Groupwise Registration by Robust PCA for Quantitative Cardiac MRI](https://mlsanity.com/paper/2311.01916/) (2023)
- [Deep-learning-based groupwise registration for motion correction of cardiac T1 mapping](https://mlsanity.com/paper/2406.12456/) (2024)

## Abstract (arXiv)

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.