Spatial Shrinkage Estimation of Diffusion Tensors on Diffusion-Weighted Imaging Data

• 成果类型: Article
• 署名作者: Yu, Tao; Li, Pengfei
• 署名单位: National University of Singapore; University of Waterloo
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2013.804408
• 发表日期: 2013
• 页码: 864-875
• 关键词: nonconcave penalized likelihood; varying-coefficient models; oracle properties; human brain; mri; selection; noise; Lasso; dti; error

摘要

Diffusion tensor imaging (DTI), based on the diffusion-weighted imaging (DWI) data acquired from magnetic resonance experiments, has been widely used to analyze the physical structure of white-matter fibers in the human brain in vivo. The raw DWI data, however, carry noise; this contaminates the diffusion tensor (DT) estimates and introduces systematic bias into the induced eigenvalues. These bias components affect the effectiveness of fiber-tracking algorithms. In this article, we propose a two-stage spatial shrinkage estimation (SpSkE) procedure to accommodate the spatial information carried in DWI data in DT estimation and to reduce the bias components in the corresponding derived eigenvalues. To this end, in the framework of the heteroscedastic linear model, SpSkE incorporates L-1-type penalization and the locally weighted least-square function. The theoretical properties of SpSkE are explored. The effectiveness of SpSkE is further illustrated by simulation and real-data examples. Supplementary materials for this article are available online.