Institute for Computer Science, University of Bonn, Friedrich-Hirzebruch-Allee 8, Bonn, 53115, Germany; Bonn-Aachen International Center for Information Technology, University of Bonn, Friedrich-Hirzebruch-Allee 6, Bonn, 53115, Germany.
Experimental Pediatric Neuroimaging and Department of Pediatric Neurology & Developmental Medicine, University Children's Hospital, Hoppe-Seyler-Straße 1, Tuebingen, 72076, Germany.
Neuroimage. 2023 May 1;271:120004. doi: 10.1016/j.neuroimage.2023.120004. Epub 2023 Mar 9.
Tractography based on diffusion Magnetic Resonance Imaging (dMRI) is the prevalent approach to the in vivo delineation of white matter tracts in the human brain. Many tractography methods rely on models of multiple fiber compartments, but the local dMRI information is not always sufficient to reliably estimate the directions of secondary fibers. Therefore, we introduce two novel approaches that use spatial regularization to make multi-fiber tractography more stable. Both represent the fiber Orientation Distribution Function (fODF) as a symmetric fourth-order tensor, and recover multiple fiber orientations via low-rank approximation. Our first approach computes a joint approximation over suitably weighted local neighborhoods with an efficient alternating optimization. The second approach integrates the low-rank approximation into a current state-of-the-art tractography algorithm based on the unscented Kalman filter (UKF). These methods were applied in three different scenarios. First, we demonstrate that they improve tractography even in high-quality data from the Human Connectome Project, and that they maintain useful results with a small fraction of the measurements. Second, on the 2015 ISMRM tractography challenge, they increase overlap, while reducing overreach, compared to low-rank approximation without joint optimization or the traditional UKF, respectively. Finally, our methods permit a more comprehensive reconstruction of tracts surrounding a tumor in a clinical dataset. Overall, both approaches improve reconstruction quality. At the same time, our modified UKF significantly reduces the computational effort compared to its traditional counterpart, and to our joint approximation. However, when used with ROI-based seeding, joint approximation more fully recovers fiber spread.
基于扩散磁共振成像（dMRI）的束流追踪是在体描绘人类大脑白质束的流行方法。许多束流追踪方法依赖于多个纤维隔室的模型，但局部 dMRI 信息并不总是足以可靠地估计次纤维的方向。因此，我们引入了两种新颖的方法，利用空间正则化使多纤维束流追踪更稳定。这两种方法都将纤维方向分布函数（fODF）表示为对称的四阶张量，并通过低秩逼近恢复多个纤维方向。我们的第一种方法通过高效的交替优化，对具有适当加权的局部邻域进行联合逼近。第二种方法将低秩逼近集成到基于无迹卡尔曼滤波（UKF）的当前最先进的束流追踪算法中。这些方法在三个不同的场景中进行了应用。首先，我们证明它们即使在高质量的人类连接组计划数据中也能改善束流追踪，并且仅使用一小部分测量值就可以获得有用的结果。其次，在 2015 年 ISMRM 束流追踪挑战赛上，与没有联合优化或传统 UKF 的低秩逼近相比，它们分别提高了重叠度，同时减少了过度延伸。最后，我们的方法允许在临床数据集周围肿瘤的更全面的束流重建。总的来说，这两种方法都提高了重建质量。同时，与传统的 UKF 相比，我们修改后的 UKF 大大减少了计算工作量，与我们的联合逼近相比也是如此。然而，当与基于 ROI 的播种一起使用时，联合逼近更全面地恢复纤维扩散。
