用于颅神经纤维束成像的解剖引导纤维轨迹分布估计
Anatomy-guided fiber trajectory distribution estimation for cranial nerves tractography
AI总结：
针对颅神经纤维束成像易产生错误轨迹和遗漏连接的问题，提出结合解剖先验的解剖引导纤维轨迹分布框架，通过高阶流线微分方程表征纤维分布，在HCP和MDM数据集上有效减少了假阳性纤维并提升了重建颅神经的解剖一致性。
摘要：
扩散MRI纤维束成像是一种用于识别和分析颅神经颅内走行的重要工具。然而，颅底的复杂环境导致扩散方向与纤维几何形状之间的空间对应关系模糊，现有的扩散纤维束成像方法在识别颅神经时容易产生错误轨迹并遗漏真阳性连接。为克服上述挑战，我们提出了一种具有解剖引导纤维轨迹分布的新型颅神经识别框架，该框架在颅神经追踪过程中结合了解剖形状先验知识，以构建扩散张量向量场。我们引入了用于连续流场表示的高阶流线微分方程，从基于纤维束的层面直接表征颅神经的纤维轨迹分布。在活体HCP数据集和临床MDM数据集上的实验结果表明，与竞争方法相比，所提出的方法减少了假阳性纤维的产生，并且生成的重建颅神经（即CN II、CN III、CN V和CN VII/VIII）被判定为更符合已知的解剖结构。
英文摘要：
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.
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