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Wong RKW, Lee TCM, Paul D, et al. FIBER DIRECTION ESTIMATION, SMOOTHING AND TRACKING IN DIFFUSION MRI. Ann Appl Stat. 2016;10(3):1137-1156doi: 10.1214/15-AOAS880.
Wong, R. K. W., Lee, T. C. M., Paul, D., & Peng, J. (2016). FIBER DIRECTION ESTIMATION, SMOOTHING AND TRACKING IN DIFFUSION MRI. The annals of applied statistics, 10(3), 1137-1156. https://doi.org/10.1214/15-AOAS880
Wong, Raymond K W, et al. "FIBER DIRECTION ESTIMATION, SMOOTHING AND TRACKING IN DIFFUSION MRI." The annals of applied statistics vol. 10,3 (2016): 1137-1156. doi: https://doi.org/10.1214/15-AOAS880
Wong RKW, Lee TCM, Paul D, Peng J. FIBER DIRECTION ESTIMATION, SMOOTHING AND TRACKING IN DIFFUSION MRI. Ann Appl Stat. 2016 Sep;10(3):1137-1156. doi: 10.1214/15-AOAS880. Epub 2016 Sep 28. PMID: 28638497; PMCID: PMC5476320.
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Ann Appl Stat. 2016 Sep;10(3):1137-1156. doi: 10.1214/15-AOAS880. Epub 2016 Sep 28.

FIBER DIRECTION ESTIMATION, SMOOTHING AND TRACKING IN DIFFUSION MRI.

The annals of applied statistics

Raymond K W Wong, Thomas C M Lee, Debashis Paul, Jie Peng

Affiliations

  1. Department Of Statistics, Iowa State University, 2218 Snedecor Hall, Ames, Iowa 50011, USA.
  2. Department Of Statistics, University Of California, Davis, 4118 Mathematical Sciences Building, One Shields Avenue, Davis, California 95616, USA.

PMID: 28638497 PMCID: PMC5476320 DOI: 10.1214/15-AOAS880

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).

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

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