提出混合刚性-非刚性配准框架，提升颈椎CT-MRI对齐精度。
MSR:Hybrid Field Modeling for CT-MRI Rigid-Deformable Registration of the Cervical Spine with an Annotated Dataset
- 分步处理：先刚性对齐每节椎骨，再用混合建模进行非刚性配准
- 新结构融合Mamba与Swin Transformer，自适应融合全局与局部特征
- 发布首个标注完整的颈椎多模态数据集R-D-Reg，开源代码可复现
颈椎的精准CT-MRI配准对术前规划至关重要，因其解剖结构复杂、个体差异大且易损伤椎动脉和脊髓。然而，针对刚性-非刚性混合建模的颈椎配准研究仍不充分，且高质量多模态标注数据匮乏制约进展。为此，我们构建并发布了全面标注的CT-MRI数据集R-D-Reg，提出MSR框架用于复杂关节结构的刚性-非刚性混合配准。MSR包含独立的刚性配准模块，实现各椎骨的局部刚性对齐；以及基于Mamba全局建模与Swin Transformer局部建模的自适应门控融合的变形模块。最终融合刚性与非刚性形变场，生成更保真局部解剖一致性的混合场。代码与数据集已公开于https://github.com/ssc1230609-spec/MSR-registration。
原文摘要 · Abstract (English)
Accurate CT-MRI registration of the cervical spine is essential for preoperative planning because this region is anatomically complex,highly variable,and vulnerable to injury of the vertebral arteries and spinal cord. However,cervical CT-MRI registration remains underexplored,particularly for rigid-deformable hybrid modeling,and the lack of high-quality annotated multimodal data further limits progress. To address these challenges, we construct and release a comprehensively annotated CT-MRI dataset, R-D-Reg, and propose MSR, a rigid-deformable hybrid registration framework for complex joint structures. Specifically, MSR includes a rigid registration module for independent local rigid alignment of individual vertebrae and a deformable registration module with an MSL block that combines Mamba-based global modeling and Swin Transformer-based local modeling through adaptive gating. The rigid and deformable deformation fields are then fused to generate a hybrid field that better preserves local anatomical consistency. The code and dataset are publicly available at https://github.com/ssc1230609-spec/MSR-registration.
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