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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.27654 |
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| _version_ | 1866910180053090304 |
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| author | Zhang, Bohai Chen, Wenjie Li, Mu Long, Kaixing Shen, Xing Yao, Xinqiang Yang, Jincheng Chen, Jianting Yang, Wei Feng, Qianjin Cao, Lei |
| author_facet | Zhang, Bohai Chen, Wenjie Li, Mu Long, Kaixing Shen, Xing Yao, Xinqiang Yang, Jincheng Chen, Jianting Yang, Wei Feng, Qianjin Cao, Lei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_27654 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MSR:Hybrid Field Modeling for CT-MRI Rigid-Deformable Registration of the Cervical Spine with an Annotated Dataset Zhang, Bohai Chen, Wenjie Li, Mu Long, Kaixing Shen, Xing Yao, Xinqiang Yang, Jincheng Chen, Jianting Yang, Wei Feng, Qianjin Cao, Lei Computer Vision and Pattern Recognition 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. |
| title | MSR:Hybrid Field Modeling for CT-MRI Rigid-Deformable Registration of the Cervical Spine with an Annotated Dataset |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.27654 |