Institution of Information Processing and Automation, Zhejiang University of Technology, Hangzhou, China.
Center for Pituitary Tumor Surgery, Department of Neurosurgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
NMR Biomed. 2023 Jul;36(7):e4904. doi: 10.1002/nbm.4904. Epub 2023 Feb 14.
The human visual pathway starts from the retina, passes through the retinogeniculate visual pathway, the optic radiation, and finally connects to the primary visual cortex. Diffusion MRI tractography is the only technology that can noninvasively reconstruct the visual pathway. However, complete and accurate visual pathway reconstruction is challenging because of the skull base environment and complex fiber geometries. Specifically, the optic nerve within the complex skull base environment can cause abnormal diffusion signals. The crossing and fanning fibers at the optic chiasm, and a sharp turn of Meyer's loop at the optic radiation, contribute to complex fiber geometries of the visual pathway. A fiber trajectory distribution (FTD) function-based tractography method of our previous work and several high sensitivity tractography methods can reveal these complex fiber geometries, but are accompanied by false-positive fibers. Thus, the related studies of the visual pathway mostly applied the expert region of interest selection strategy. However, interobserver variability is an issue in reconstructing an accurate visual pathway. In this paper, we propose a unified global tractography framework to automatically reconstruct the visual pathway. We first extend the FTD function to a high-order streamline differential equation for global trajectory estimation. At the global level, the tractography process is simplified as the estimation of global trajectory distribution coefficients by minimizing the cost between trajectory distribution and the selected directions under the prior guidance by introducing the tractography template as anatomic priors. Furthermore, we use a deep learning-based method and tractography template prior information to automatically generate the mask for tractography. The experimental results demonstrate that our proposed method can successfully reconstruct the visual pathway with high accuracy.
人类视觉通路始于视网膜，经过视放射，最终连接至初级视觉皮层。弥散磁共振成像轨迹追踪是唯一能够无创重建视觉通路的技术。然而，由于颅底环境和复杂的纤维几何形状，完整且准确的视觉通路重建具有挑战性。具体来说，复杂颅底环境中的视神经可能会导致异常的扩散信号。视交叉处的交叉和扇形纤维，以及视放射处 Meyer 环的急转弯，导致了视觉通路的复杂纤维几何形状。我们之前工作中的纤维轨迹分布（FTD）功能追踪方法和几种高灵敏度追踪方法可以揭示这些复杂的纤维几何形状，但伴随着假阳性纤维。因此，视觉通路的相关研究大多应用了专家感兴趣区选择策略。然而，在准确重建视觉通路时，观察者间的变异性是一个问题。在本文中，我们提出了一种统一的全局追踪框架来自动重建视觉通路。我们首先将 FTD 函数扩展到高阶流线微分方程，以进行全局轨迹估计。在全局水平上，通过引入追踪模板作为解剖先验，追踪过程简化为通过最小化轨迹分布和所选方向之间的代价来估计全局轨迹分布系数。此外，我们使用基于深度学习的方法和追踪模板先验信息来自动生成追踪的掩模。实验结果表明，我们提出的方法可以成功地以高精度重建视觉通路。
