GMmorph: dynamic spatial matching registration model for 3D medical image based on gated Mamba

Objective.Deformable registration aims to achieve nonlinear alignment of image space by estimating a dense displacement field. It is commonly used as a preprocessing step in clinical and image analysis applications, such as surgical planning, diagnostic assistance, and surgical navigation. We aim to overcome these challenges: Deep learning-based registration methods often struggle with complex displacements and lack effective interaction between global and local feature information. They also neglect the spatial position matching process, leading to insufficient registration accuracy and reduced robustness when handling abnormal tissues.Approach.We propose a dual-branch interactive registration model architecture from the perspective of spatial matching. Implicit regularization is achieved through a consistency loss, enabling the network to balance high accuracy with a low folding rate. We introduced the dynamic matching module between the two branches of the registration, which generates learnable offsets based on all the tokens across the entire resolution range of the base branch features. Using trilinear interpolation, the model adjusts its feature expression range according to the learned offsets, capturing highly flexible positional differences. To facilitate the spatial matching process, we designed the gated mamba layer to globally model pixel-level features by associating all voxel information, while the detail enhancement module, which is based on channel and spatial attention, enhances the richness of local feature details.Main results.Our study explores the model's performance in single-modal and multi-modal image registration, including normal brain, brain tumor, and lung images. We propose unsupervised and semi-supervised registration modes and conduct extensive validation experiments. The results demonstrate that the model achieves state-of-the-art performance across multiple datasets.Significance.By introducing a novel perspective of position matching, the model achieves precise registration of various types of medical data, offering significant clinical value in medical applications.