Parameterization of White Matter Manifold-Like Structures Using Principal Surfaces

• 成果类型: Article
• 署名作者: Yue, Chen; Zipunnikov, Vadim; Bazin, Pierre-Louis; Dzung Pham; Reich, Daniel; Crainiceanu, Ciprian; Caffo, Brian
• 署名单位: Johns Hopkins University; Max Planck Society; United States Department of Defense; Center for Neuroscience & Regenerative Medicine (CNRM); National Institutes of Health (NIH) - USA; National Institutes of Health (NIH) - USA; NIH National Institute of Neurological Disorders & Stroke (NINDS)
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2016.1164050
• 发表日期: 2016
• 页码: 1050-1060
• 关键词: component analysis; corpus-callosum; CURVES; regression; statistics; mri

摘要

In this article, 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 (a) constructs the principal surface of a corpus callosum; (b) flattens the surface into,a parametric two-dimensional (2D) map; and (c) 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.