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

TL;DR

This paper introduces extbackslash AnyTwoReg, a set-based, permutation-invariant groupwise registration framework for variable-length, variable-contrast cardiac MRI sequences, enhancing robustness and generalization across protocols.

Contribution

It proposes a novel set-based registration method that decouples network design from sequence length and input order, enabling zero-shot generalization across different MRI protocols.

Findings

extbackslash AnyTwoReg generalizes to unseen MRI datasets with different lengths and contrasts.

It improves the quality of downstream quantitative mapping.

The framework is applicable to Cine MRI for inter-cardiac-phase registration.

Abstract

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 \emph{\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, \emph{\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 $T_1$ mapping dataset (STONE, sequence length $L=11$), \AnyTwoReg generalizes to two unseen quantitative MRI datasets (MOLLI, ASL) with variable lengths ($L \in [11, 60]$) 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.