Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Jörg Polzehl, Karsten Tabelow
Title: Structural Adaptive Smoothing in Diffusion Tensor Imaging: The R Package dti
Abstract: Diffusion weighted imaging has become and will certainly continue to be an important tool in medical research and diagnostics. Data obtained with diffusion weighted imaging are characterized by a high noise level. Thus, estimation of quantities like anisotropy indices or the main diffusion direction may be significantly compromised by noise in clinical or neuroscience applications.

Here, we present a new package dti for R, which provides functions for the analysis of diffusion weighted data within the diffusion tensor model. This includes smoothing by a recently proposed structural adaptive smoothing procedure based on the propagation-separation approach in the context of the widely used diffusion tensor model. We extend the procedure and show, how a correction for Rician bias can be incorporated. We use a heteroscedastic nonlinear regression model to estimate the diffusion tensor. The smoothing procedure naturally adapts to different structures of different size and thus avoids oversmoothing edges and fine structures.

We illustrate the usage and capabilities of the package through some examples.

Page views:: 4261. Submitted: 2009-01-12. Published: 2009-09-15.
Paper: Structural Adaptive Smoothing in Diffusion Tensor Imaging: The R Package dti     Download PDF (Downloads: 4141)
dti_0.8-1.tar.gz: R source package Download (Downloads: 886; 129KB)
v31i09.R: R example code from the paper Download (Downloads: 1066; 8KB)
SCIRunData_4.1_20090731_data.tgz: SCIRunData data Download (Downloads: 959; 485MB)

DOI: 10.18637/jss.v031.i09

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.