Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
Department of Pediatrics at Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA.
Med Image Anal. 2021 Aug;72:102129. doi: 10.1016/j.media.2021.102129. Epub 2021 Jun 3.
Accurate modeling of diffusion-weighted magnetic resonance imaging measurements is necessary for accurate brain connectivity analysis. Existing methods for estimating the number and orientations of fascicles in an imaging voxel either depend on non-convex optimization techniques that are sensitive to initialization and measurement noise, or are prone to predicting spurious fascicles. In this paper, we propose a machine learning-based technique that can accurately estimate the number and orientations of fascicles in a voxel. Our method can be trained with either simulated or real diffusion-weighted imaging data. Our method estimates the angle to the closest fascicle for each direction in a set of discrete directions uniformly spread on the unit sphere. This information is then processed to extract the number and orientations of fascicles in a voxel. On realistic simulated phantom data with known ground truth, our method predicts the number and orientations of crossing fascicles more accurately than several classical and machine learning methods. It also leads to more accurate tractography. On real data, our method is better than or compares favorably with other methods in terms of robustness to measurement down-sampling and also in terms of expert quality assessment of tractography results.
准确建模扩散加权磁共振成像测量对于准确的脑连接分析是必要的。现有的用于估计成像体素中束的数量和方向的方法要么依赖于对初始化和测量噪声敏感的非凸优化技术，要么容易预测虚假束。在本文中，我们提出了一种基于机器学习的技术，可以准确估计体素中束的数量和方向。我们的方法可以用模拟或真实的扩散加权成像数据进行训练。我们的方法估计了在单位球上均匀分布的一组离散方向中每个方向到最近束的角度。然后，这些信息被处理以提取体素中束的数量和方向。在具有已知真实值的现实模拟幻像数据上，我们的方法比几种经典和机器学习方法更准确地预测了交叉束的数量和方向。它还导致更准确的轨迹追踪。在真实数据上，我们的方法在测量下采样的鲁棒性方面以及在轨迹追踪结果的专家质量评估方面都优于或优于其他方法。
