# Set-Based Groupwise Registration for Variable-Length, Variable-Contrast Cardiac MRI

arXiv: https://arxiv.org/abs/2605.10571 · HTML: https://mlsanity.com/paper/2605.10571/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 29 references that are in the map.
- First posted on arXiv 11 May 2026.

## Builds on (most cited first, 11 of 11)

- [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)
- [Correlation-aware Coarse-to-fine MLPs for Deformable Medical Image Registration](https://mlsanity.com/paper/2406.00123/) (2024)
- [GroupRegNet: A Groupwise One-shot Deep Learning-based 4D Image Registration Method](https://mlsanity.com/paper/2009.02613/) (2020)
- [MultiMorph: On-demand Atlas Construction](https://mlsanity.com/paper/2504.00247/) (2025)
- [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)
- [InstantGroup: Instant Template Generation for Scalable Group of Brain MRI Registration](https://mlsanity.com/paper/2211.05622/) (2022)
- [MBSS-T1: Model-Based Subject-Specific Self-Supervised Motion Correction for Robust Cardiac T1 Mapping](https://mlsanity.com/paper/2408.11992/) (2024)
- [Reverse Imaging for Wide-spectrum Generalization of Cardiac MRI Segmentation](https://mlsanity.com/paper/2508.21254/) (2025)

## Abstract (arXiv)

Quantitative cardiac magnetic resonance imaging (MRI) enables non-invasive myocardial tissue characterization but relies on robust motion correction within these variable-length, variable-contrast image sequences. Groupwise registration, which simultaneously aligns all images, has shown greater robustness than pairwise registration for motion correction. However, current deep-learning-based groupwise registration methods cannot generalize across MRI sequences: the architecture typically encodes input data as a fixed-length channel stack, which rigidly couples network design to protocol-specific sequence length, input ordering, and contrast dynamics. At inference time, any change in imaging protocols will render the network unusable. In this work, we introduce AnyTwoReg, a new set-based groupwise registration framework that takes a quantitative MRI sequence as an unordered set. This set formulation fundamentally decouples network design from sequence length and input ordering. By utilizing a shared encoder and correlation-guided feature aggregation, AnyTwoReg constructs a permutation-invariant canonical reference for registration, and learns a permutation-equivariant mapping from images to deformation fields. Additionally, we extract contrast-insensitive image features from an existing foundation model to handle extreme contrast variations. Trained exclusively on a single public T1 mapping dataset (STONE, sequence length L=11), AnyTwoReg generalizes to two unseen quantitative MRI datasets (MOLLI, ASL) with variable lengths (L ∈ [11, 60]) and different contrast dynamics. It achieves strong cross-protocol generalization in a zero-shot manner, and consistently improves downstream quantitative mapping quality. Notably, while designed for quantitative MRI sequences, our framework is directly applicable to Cine MRI sequences for inter-cardiac-phase registration.