Deformable image registration is an essential approach for medical image analysis.This paper introduces MambaMorph, an innovative multi-modality deformable registration network, specifically designed for Magnetic Resonance (MR) and Computed Tomography (CT) image alignment. MambaMorph stands out with its Mamba-based registration module and a contrastive feature learning approach, addressing the prevalent challenges in multi-modality registration. The network leverages Mamba blocks for efficient long-range modeling and high-dimensional data processing, coupled with a feature extractor that learns fine-grained features for enhanced registration accuracy. Experimental results showcase MambaMorph's superior performance over existing methods in MR-CT registration, underlining its potential in clinical applications. This work underscores the significance of feature learning in multi-modality registration and positions MambaMorph as a trailblazing solution in this field. The code for MambaMorph is available at: https://github.com/Guo-Stone/MambaMorph.

翻译：可变形图像配准是医学图像分析中的关键技术。本文提出MambaMorph——一种创新的多模态可变形配准网络，专为磁共振（MR）与计算机断层扫描（CT）图像对齐设计。MambaMorph以基于Mamba的配准模块和对比特征学习方法脱颖而出，有效应对多模态配准中的常见挑战。该网络利用Mamba模块实现高效的长程建模与高维数据处理，并结合特征提取器学习细粒度特征以提升配准精度。实验结果表明，MambaMorph在MR-CT配准任务中展现出优于现有方法的性能，凸显其在临床应用中的潜力。本研究强调了特征学习在多模态配准中的关键作用，并将MambaMorph定位为该领域的开创性解决方案。MambaMorph的代码已开源于：https://github.com/Guo-Stone/MambaMorph。