  Capturing voxel-wise spatial correspondence across distinct modalities is
crucial for medical image analysis. However, current registration approaches
are not practical enough in terms of registration accuracy and clinical
applicability. In this paper, we introduce MambaMorph, a novel multi-modality
deformable registration framework. Specifically, MambaMorph utilizes a
Mamba-based registration module and a fine-grained, yet simple, feature
extractor for efficient long-range correspondence modeling and high-dimensional
feature learning, respectively. Additionally, we develop a well-annotated brain
MR-CT registration dataset, SR-Reg, to address the scarcity of data in
multi-modality registration. To validate MambaMorph's multi-modality
registration capabilities, we conduct quantitative experiments on both our
SR-Reg dataset and a public T1-T2 dataset. The experimental results on both
datasets demonstrate that MambaMorph significantly outperforms the current
state-of-the-art learning-based registration methods in terms of registration
accuracy. Further study underscores the efficiency of the Mamba-based
registration module and the lightweight feature extractor, which achieve
notable registration quality while maintaining reasonable computational costs
and speeds. We believe that MambaMorph holds significant potential for
practical applications in medical image registration. The code for MambaMorph
is available at: https://github.com/Guo-Stone/MambaMorph.
