Zeng Qingrun, Xia Ze, Huang Jiahao, Xie Lei, Zhang Jiawei, Huang Shengwei, Xing Zhengqiu, Zhuge Qichuan, Feng Yuanjing
College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, China.
Advanced Technology Institute, Zhejiang University of Technology, Hangzhou, 310023, China.
Med Biol Eng Comput. 2025 May 6. doi: 10.1007/s11517-025-03357-3.
The corticospinal tract (CST) is the primary neural pathway responsible for voluntary motor functions, and preoperative CST reconstruction is crucial for preserving nerve functions during neurosurgery. Diffusion magnetic resonance imaging-based tractography is the only noninvasive method to preoperatively reconstruct CST in clinical practice. However, for the largesize bundle CST with complex fiber geometry (fanning fibers), reconstructing its full extent remains challenging with local-derived methods without incorporating global information. Especially in the presence of tumors, the mass effect and partial volume effect cause abnormal diffusion signals. In this work, a CST reconstruction tractography method based on a novel direction filter was proposed, designed to ensure robust CST reconstruction in the clinical dataset with tumors. A direction filter based on a fourth-order differential equation was introduced for global direction estimation. By considering the spatial consistency and leveraging anatomical prior knowledge, the direction filter was computed by minimizing the energy between the target directions and initial fiber directions. On the basis of the new directions corresponding to CST obtained by the direction filter, the fiber tracking method was implemented to reconstruct the fiber trajectory. Additionally, a deep learning-based method along with tractography template prior information was employed to generate the regions of interest (ROIs) and initial fiber directions. Experimental results showed that the proposed method yields higher valid connections and lower no connections and exhibits the fewest broken fibers and short-connected fibers. The proposed method offers an effective tool to enhance CST-related surgical outcomes by optimizing tumor resection and preserving CST.
皮质脊髓束（CST）是负责自主运动功能的主要神经通路，术前CST重建对于神经外科手术中保留神经功能至关重要。基于扩散磁共振成像的纤维束成像技术是临床实践中术前重建CST的唯一非侵入性方法。然而，对于具有复杂纤维几何结构（扇形纤维）的大尺寸束状CST，在不纳入全局信息的情况下，使用局部衍生方法重建其完整范围仍然具有挑战性。特别是在存在肿瘤的情况下，肿块效应和部分容积效应会导致异常扩散信号。在这项工作中，提出了一种基于新型方向滤波器的CST重建纤维束成像方法，旨在确保在有肿瘤的临床数据集中稳健地重建CST。引入了基于四阶微分方程的方向滤波器用于全局方向估计。通过考虑空间一致性并利用解剖学先验知识，通过最小化目标方向和初始纤维方向之间的能量来计算方向滤波器。基于方向滤波器获得的与CST对应的新方向，实施纤维追踪方法来重建纤维轨迹。此外，采用基于深度学习的方法以及纤维束成像模板先验信息来生成感兴趣区域（ROI）和初始纤维方向。实验结果表明，所提出的方法产生更高的有效连接和更低的无连接，并表现出最少的断纤维和短连接纤维。所提出的方法提供了一种有效的工具，通过优化肿瘤切除和保留CST来提高与CST相关的手术效果。
