Image registration, a critical process in medical imaging, involves aligning
different sets of medical imaging data into a single unified coordinate system.
Deep learning networks, such as the Convolutional Neural Network (CNN)-based
VoxelMorph, Vision Transformer (ViT)-based TransMorph, and State Space Model
(SSM)-based MambaMorph, have demonstrated effective performance in this domain.
The recent Visual State Space Model (VMamba), which incorporates a cross-scan
module with SSM, has exhibited promising improvements in modeling global-range
dependencies with efficient computational cost in computer vision tasks. This
paper hereby introduces an exploration of VMamba with image registration, named
VMambaMorph. This novel hybrid VMamba-CNN network is designed specifically for
3D image registration. Utilizing a U-shaped network architecture, VMambaMorph
computes the deformation field based on target and source volumes. The
VMamba-based block with 2D cross-scan module is redesigned for 3D volumetric
feature processing, and a fine-grained feature extraction module is proposed
for high-dimensional feature learning. We validate VMambaMorph using a public
benchmark brain MR-CT registration dataset, comparing its performance against
current state-of-the-art methods. The results indicate that VMambaMorph
achieves competitive registration quality. The code for VMambaMorph is
available on GitHub.

VMambaMorph 是一种基于 VMamba 和 CNN 的网络，专门为 3D 图像配准设计，利用公共基准脑 MR-CT 配准数据集验证了其与当前最先进方法的竞争性配准质量。

VMambaMorph：一种基于 Visual Mamba 的带有跨扫描模块的可变形三维图像配准框架

VMambaMorph: a Visual Mamba-based Framework with Cross-Scan Module for  Deformable 3D Image Registration

Reliably and physically accurately transferring information between images
through deformable image registration with large anatomical differences is an
open challenge in medical image analysis. Most existing methods have two key
shortcomings: first, they require extensive up-front parameter tuning to each
specific registration problem, and second, they have difficulty capturing large
deformations and content mismatches between images. There have however been
developments that have laid the foundation for potential solutions to both
shortcomings. Towards the first shortcoming, a multi-objective optimization
approach using the Real-Valued Gene-pool Optimal Mixing Evolutionary Algorithm
(RV-GOMEA) has been shown to be capable of producing a diverse set of
registrations for 2D images in one run of the algorithm, representing different
trade-offs between conflicting objectives in the registration problem. This
allows the user to select a registration afterwards and removes the need for
up-front tuning. Towards the second shortcoming, a dual-dynamic grid
transformation model has proven effective at capturing large differences in 2D
images. These two developments have recently been accelerated through GPU
parallelization, delivering large speed-ups. Based on this accelerated version,
it is now possible to extend the approach to 3D images. Concordantly, this work
introduces the first method for multi-objective 3D deformable image
registration, using a 3D dual-dynamic grid transformation model based on
simplex meshes while still supporting the incorporation of annotated guidance
information and multi-resolution schemes. Our proof-of-concept prototype shows
promising results on synthetic and clinical 3D registration problems, forming
the foundation for a new, insightful method that can include bio-mechanical
properties in the registration.

通过使用多目标优化方法和双动态网格变换等技术，本文提出了一种解决医学图像中大幅度变形和内容不匹配问题的多目标三维变形图像配准方法，并实现了良好的效果。