Yue Chen, Zipunnikov Vadim, Bazin Pierre-Louis, Pham Dzung, Reich Daniel, Crainiceanu Ciprian, Caffo Brian
Department of Biostatistics, Johns Hopkins University, Baltimore, MD, 21205.
Department of Neurophysics, Max Planck Institute, Leipzig, Germany, 04103.
J Am Stat Assoc. 2016;111(515):1050-1060. doi: 10.1080/01621459.2016.1164050. Epub 2016 Oct 18.
In this manuscript, we are concerned with data generated from a diffusion tensor imaging (DTI) experiment. The goal is to parameterize manifold-like white matter tracts, such as the corpus callosum, using principal surfaces. The problem is approached by finding a geometrically motivated surface-based representation of the corpus callosum and visualized fractional anisotropy (FA) values projected onto the surface. The method also applies to any other diffusion summary. An algorithm is proposed that 1) constructs the principal surface of a corpus callosum; 2) flattens the surface into a parametric 2D map; 3) projects associated FA values on the map. The algorithm is applied to a longitudinal study containing 466 diffusion tensor images of 176 multiple sclerosis (MS) patients observed at multiple visits. For each subject and visit the study contains a registered DTI scan of the corpus callosum at roughly 20,000 voxels. Extensive simulation studies demonstrate fast convergence and robust performance of the algorithm under a variety of challenging scenarios.
在本手稿中，我们关注的是由扩散张量成像（DTI）实验生成的数据。目标是使用主曲面来参数化类似流形的白质束，比如胼胝体。该问题通过找到一种基于几何的胼胝体表面表示方法，并可视化投影到该表面上的分数各向异性（FA）值来解决。该方法也适用于任何其他扩散总结。我们提出了一种算法，该算法：1）构建胼胝体的主曲面；2）将该曲面展平为参数化二维地图；3）将相关的FA值投影到该地图上。该算法应用于一项纵向研究，该研究包含对176名多发性硬化症（MS）患者在多次就诊时进行观察所获得的466张扩散张量图像。对于每个受试者和每次就诊，该研究包含一个大约20000体素的胼胝体配准DTI扫描。广泛的模拟研究表明，在各种具有挑战性的场景下，该算法具有快速收敛性和稳健的性能。
