The Annals of Applied Statistics

Fiber direction estimation, smoothing and tracking in diffusion MRI

Raymond K. W. Wong, Thomas C. M. Lee, Debashis Paul, Jie Peng, and Alzheimer’s Disease Neuroimaging Initiative

Full-text: Open access

Abstract

Diffusion magnetic resonance imaging is an imaging technology designed to probe anatomical architectures of biological samples in an in vivo and noninvasive 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. Last, 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).

Article information

Source
Ann. Appl. Stat., Volume 10, Number 3 (2016), 1137-1156.

Dates
Received: January 2015
Revised: September 2015
First available in Project Euclid: 28 September 2016

Permanent link to this document
https://projecteuclid.org/euclid.aoas/1475069599

Digital Object Identifier
doi:10.1214/15-AOAS880

Mathematical Reviews number (MathSciNet)
MR3553220

Zentralblatt MATH identifier
06775262

Keywords
Diffusion tensor imaging direction smoothing multi-tensor model fiber tracking tractography

Citation

Wong, Raymond K. W.; Lee, Thomas C. M.; Paul, Debashis; Peng, Jie; Disease Neuroimaging Initiative, Alzheimer’s. Fiber direction estimation, smoothing and tracking in diffusion MRI. Ann. Appl. Stat. 10 (2016), no. 3, 1137--1156. doi:10.1214/15-AOAS880. https://projecteuclid.org/euclid.aoas/1475069599


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