Fiber Direction Estimation, Smoothing and Tracking in Diffusion MRI

TL;DR

This paper introduces a new approach for estimating, smoothing, and tracking multiple fiber directions in diffusion MRI, improving accuracy especially in crossing fiber regions, with applications demonstrated on simulated and real Alzheimer's data.

Contribution

It presents a novel identifiable multi-tensor model for diffusion direction estimation, a new smoothing technique with strong theoretical and empirical support, and a fiber tracking algorithm for multiple directions within a voxel.

Findings

Enhanced accuracy in crossing fiber regions

Effective fiber tracking with multiple directions

Validated on simulated and Alzheimer's data

Abstract

Diffusion magnetic resonance imaging is an imaging technology designed to probe anatomical architectures of biological samples in an in vivo and non-invasive manner through measuring water diffusion. The contribution of this paper is threefold. First it proposes a new method to identify and estimate multiple diffusion directions within a voxel through a new and identifiable parametrization of the widely used multi-tensor model. Unlike many existing methods, this method focuses on the estimation of diffusion directions rather than the diffusion tensors. Second, this paper proposes a novel direction smoothing method which greatly improves direction estimation in regions with crossing fibers. This smoothing method is shown to have excellent theoretical and empirical properties. Lastly, this paper develops a fiber tracking algorithm that can handle multiple directions within a voxel. The overall methodology is illustrated with simulated data and a data set collected for the study of Alzheimer's disease by the Alzheimer's Disease Neuroimaging Initiative (ADNI).