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: this https URL

MambaMorph 是一种创新的多模态可形变注册网络，特别适用于磁共振（MR）和计算机断层扫描（CT）图像对齐。它通过使用 Mamba 块进行高效的远程建模和高维数据处理，以及学习细粒度特征的特征提取器，提高了注册准确性。实验结果表明 MambaMorph 在 MR-CT 注册中具有优秀的性能，凸显了其在临床应用中的潜力。