Pinto Daniela, Roman Claudio, Guevara Miguel, Poupon Cyril, Mangin Jean-Francaois, Guevara Pamela
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:1-4. doi: 10.1109/EMBC.2018.8512333.
Most analysis and segmentation methods for diffusion MRI tractography datasets require a fiber distance measure able to determine the similarity between a pair of fibers. We present a stringent fiber distance measure able to perform a good discrimination between fiber shapes and lengths. It uses three terms: (i) a fiber maximum Euclidean distance, (ii) a fiber shape distance, and (iii) a fiber length distance. The distance was evaluated applying a hierarchical clustering of fibers connecting the pre-and post-central gyri of a subject. Results where compared with other known fiber distance measures. A better sensitivity to differences in fiber shape and length was found for the proposed distance. This will be very useful for the detailed study and description of white matter bundles. Known bundles will be better decomposed into sub-bundles, with more precision on the bundle shape and on the regions connected by the fibers. For short association bundles, this distance will be a real improvement, as even the most stringent distance used until now shows some limitations when evaluating the similarity of these fibers.
大多数用于扩散磁共振成像纤维束成像数据集的分析和分割方法都需要一种能够确定一对纤维之间相似性的纤维距离度量。我们提出了一种严格的纤维距离度量，能够对纤维形状和长度进行良好的区分。它使用三个项：（i）纤维最大欧几里得距离，（ii）纤维形状距离，以及（iii）纤维长度距离。通过对连接受试者中央前回和中央后回的纤维进行层次聚类来评估该距离。将结果与其他已知的纤维距离度量进行比较。发现所提出的距离对纤维形状和长度的差异具有更好的敏感性。这对于白质束的详细研究和描述将非常有用。已知的束将能更好地分解为子束，在束形状和纤维连接的区域上具有更高的精度。对于短联合束，这种距离将是一个真正的改进，因为即使是迄今为止使用的最严格的距离在评估这些纤维的相似性时也显示出一些局限性。
