Spatial shrinkage estimation of diffusion tensors on diffusion-weighted imaging data

Abstract

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 L1-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. © 2013 American Statistical Association.