Anatomy-guided fiber trajectory distribution estimation for cranial nerves tractography
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866929507237101568 |
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| author | Xie, Lei Zeng, Qingrun Zhou, Huajun Xie, Guoqiang Li, Mingchu Huang, Jiahao Cui, Jianan Chen, Hao Feng, Yuanjing |
| author_facet | Xie, Lei Zeng, Qingrun Zhou, Huajun Xie, Guoqiang Li, Mingchu Huang, Jiahao Cui, Jianan Chen, Hao Feng, Yuanjing |
| contents | Diffusion MRI tractography is an important tool for identifying and analyzing the intracranial course of cranial nerves (CNs). However, the complex environment of the skull base leads to ambiguous spatial correspondence between diffusion directions and fiber geometry, and existing diffusion tractography methods of CNs identification are prone to producing erroneous trajectories and missing true positive connections. To overcome the above challenge, we propose a novel CNs identification framework with anatomy-guided fiber trajectory distribution, which incorporates anatomical shape prior knowledge during the process of CNs tracing to build diffusion tensor vector fields. We introduce higher-order streamline differential equations for continuous flow field representations to directly characterize the fiber trajectory distribution of CNs from the tract-based level. The experimental results on the vivo HCP dataset and the clinical MDM dataset demonstrate that the proposed method reduces false-positive fiber production compared to competing methods and produces reconstructed CNs (i.e. CN II, CN III, CN V, and CN VII/VIII) that are judged to better correspond to the known anatomy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_18856 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Anatomy-guided fiber trajectory distribution estimation for cranial nerves tractography Xie, Lei Zeng, Qingrun Zhou, Huajun Xie, Guoqiang Li, Mingchu Huang, Jiahao Cui, Jianan Chen, Hao Feng, Yuanjing Image and Video Processing Computer Vision and Pattern Recognition Diffusion MRI tractography is an important tool for identifying and analyzing the intracranial course of cranial nerves (CNs). However, the complex environment of the skull base leads to ambiguous spatial correspondence between diffusion directions and fiber geometry, and existing diffusion tractography methods of CNs identification are prone to producing erroneous trajectories and missing true positive connections. To overcome the above challenge, we propose a novel CNs identification framework with anatomy-guided fiber trajectory distribution, which incorporates anatomical shape prior knowledge during the process of CNs tracing to build diffusion tensor vector fields. We introduce higher-order streamline differential equations for continuous flow field representations to directly characterize the fiber trajectory distribution of CNs from the tract-based level. The experimental results on the vivo HCP dataset and the clinical MDM dataset demonstrate that the proposed method reduces false-positive fiber production compared to competing methods and produces reconstructed CNs (i.e. CN II, CN III, CN V, and CN VII/VIII) that are judged to better correspond to the known anatomy. |
| title | Anatomy-guided fiber trajectory distribution estimation for cranial nerves tractography |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2402.18856 |