MSR：用于带有标注数据集的颈椎脊柱CT-MRI刚-变形配准的混合场建模
MSR:Hybrid Field Modeling for CT-MRI Rigid-Deformable Registration of the Cervical Spine with an Annotated Dataset
AI总结：
本文提出MSR框架，通过混合刚-变形配准方法提升颈椎脊柱CT-MRI配准精度，解决复杂结构配准难题。
摘要：
准确的CT-MRI颈椎脊柱配准对术前规划至关重要，因其解剖复杂、变异大且易伤椎动脉和脊髓。然而，颈椎CT-MRI配准研究有限，尤其在刚-变形混合建模方面，缺乏高质量多模态标注数据进一步限制了进展。为应对这些挑战，我们构建并发布了全面标注的CT-MRI数据集R-D-Reg，并提出MSR，一种用于复杂关节结构的刚-变形混合配准框架。具体而言，MSR包括用于独立局部刚性对齐单个椎体的刚性配准模块，以及结合基于Mamba的全局建模和基于Swin Transformer的局部建模的变形配准模块，通过自适应门控结合MSL块。然后将刚性和变形性形变场融合，生成更好的局部解剖一致性保持的混合场。代码和数据集可在https://github.com/ssc1230609-spec/MSR-registration公开获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
