Sherbrooke Connectivity Imaging Laboratory (SCIL), Université de Sherbrooke, Canada.
Sherbrooke Connectivity Imaging Laboratory (SCIL), Université de Sherbrooke, Canada.
Neuroimage. 2019 Feb 1;186:382-398. doi: 10.1016/j.neuroimage.2018.11.018. Epub 2018 Nov 17.
Anatomical white matter bundles vary in shape, size, length, and complexity, making diffusion MRI tractography reconstruction of some bundles more difficult than others. As a result, bundles reconstruction often suffers from a poor spatial extent recovery. To fill-up the white matter volume as much and as best as possible, millions of streamlines can be generated and filtering techniques applied to address this issue. However, well-known problems and biases are introduced such as the creation of a large number of false positives and over-representation of easy-to-track parts of bundles and under-representation of hard-to-track. To address these challenges, we developed a Bundle-Specific Tractography (BST) algorithm. It incorporates anatomical and orientational prior knowledge during the process of streamline tracing to increase reproducibility, sensitivity, specificity and efficiency when reconstructing certain bundles of interest. BST outperforms classical deterministic, probabilistic, and global tractography methods. The increase in anatomically plausible streamlines, with larger spatial coverage, helps to accurately represent the full shape of bundles, which could greatly enhance and robustify tract-based and connectivity-based neuroimaging studies.
解剖学上的白质束在形状、大小、长度和复杂性上存在差异，这使得一些白质束的弥散磁共振成像轨迹重建比其他白质束更困难。因此，束重建经常存在空间范围恢复不佳的问题。为了尽可能多地填充白质体积，可以生成数百万条流线，并应用滤波技术来解决这个问题。然而，这会引入众所周知的问题和偏差，例如产生大量的假阳性，以及容易跟踪的束部分的过度表示和难以跟踪的部分的表示不足。为了解决这些挑战，我们开发了一种束特定的轨迹重建（BST）算法。它在流线追踪过程中结合了解剖学和方向先验知识，以提高对特定感兴趣束进行重建的可重复性、敏感性、特异性和效率。BST 优于经典的确定性、概率性和全局轨迹重建方法。增加具有更大空间覆盖范围的解剖学上合理的流线有助于准确表示束的完整形状，这可以极大地增强和强化基于轨迹和连接的神经影像学研究。
