Center for Advanced Imaging Innovation and Research (CAI(2)R), NYU School of Medicine, New York, NY, USA; Center for Biomedical Imaging, Dept. of Radiology, NYU School of Medicine, New York, NY, USA.
Center for Advanced Imaging Innovation and Research (CAI(2)R), NYU School of Medicine, New York, NY, USA; Center for Biomedical Imaging, Dept. of Radiology, NYU School of Medicine, New York, NY, USA; The Sackler Institute of Graduate Biomedical Sciences, NYU School of Medicine, New York, NY, USA.
Neuroimage. 2019 Sep;198:231-241. doi: 10.1016/j.neuroimage.2019.05.024. Epub 2019 May 16.
Diffusion tractography is routinely used to study white matter architecture and brain connectivity in vivo. A key step for successful tractography of neuronal tracts is the correct identification of tract directions in each voxel. Here we propose a fingerprinting-based methodology to identify these fiber directions in Orientation Distribution Functions, dubbed ODF-Fingerprinting (ODF-FP). In ODF-FP, fiber configurations are selected based on the similarity between measured ODFs and elements in a pre-computed library. In noisy ODFs, the library matching algorithm penalizes the more complex fiber configurations. ODF simulations and analysis of bootstrapped partial and whole-brain in vivo datasets show that the ODF-FP approach improves the detection of fiber pairs with small crossing angles while maintaining fiber direction precision, which leads to better tractography results. Rather than focusing on the ODF maxima, the ODF-FP approach uses the whole ODF shape to infer fiber directions to improve the detection of fiber bundles with small crossing angle. The resulting fiber directions aid tractography algorithms in accurately displaying neuronal tracts and calculating brain connectivity.
弥散张量成像技术常用于活体研究白质结构和大脑连接。成功追踪神经元束的关键步骤是正确识别每个体素中的束方向。在这里,我们提出了一种基于指纹的方法来识别取向分布函数中的这些纤维方向,称为 ODF 指纹(ODF-FP)。在 ODF-FP 中,根据测量的 ODF 与预计算库中元素之间的相似性选择纤维构型。在噪声 ODF 中,库匹配算法会惩罚更复杂的纤维构型。ODF 模拟和对 bootstrap 部分和全脑活体数据集的分析表明,ODF-FP 方法提高了对具有小交叉角的纤维对的检测能力,同时保持纤维方向精度,从而获得更好的追踪结果。ODF-FP 方法不是专注于 ODF 最大值,而是使用整个 ODF 形状来推断纤维方向,以提高对具有小交叉角的纤维束的检测能力。得到的纤维方向有助于追踪算法准确显示神经元束并计算大脑连接。