Structural Adaptive Smoothing in Diffusion Tensor Imaging: The R Package dti
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 directio...
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doaj-67549b4b5d0d4660ba82dff99c7841642020-11-24T23:22:31ZengFoundation for Open Access StatisticsJournal of Statistical Software1548-76602009-09-013109Structural Adaptive Smoothing in Diffusion Tensor Imaging: The R Package dtiJorg PolzehlKarsten TabelowDiffusion 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 <b>dti</b> for <b>R</b>, 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.http://www.jstatsoft.org/v31/i09/paperstructural adaptive smoothingdiffusion weighted imagingdiffusion tensor modelRician biasR |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jorg Polzehl Karsten Tabelow |
spellingShingle |
Jorg Polzehl Karsten Tabelow Structural Adaptive Smoothing in Diffusion Tensor Imaging: The R Package dti Journal of Statistical Software structural adaptive smoothing diffusion weighted imaging diffusion tensor model Rician bias R |
author_facet |
Jorg Polzehl Karsten Tabelow |
author_sort |
Jorg Polzehl |
title |
Structural Adaptive Smoothing in Diffusion Tensor Imaging: The R Package dti |
title_short |
Structural Adaptive Smoothing in Diffusion Tensor Imaging: The R Package dti |
title_full |
Structural Adaptive Smoothing in Diffusion Tensor Imaging: The R Package dti |
title_fullStr |
Structural Adaptive Smoothing in Diffusion Tensor Imaging: The R Package dti |
title_full_unstemmed |
Structural Adaptive Smoothing in Diffusion Tensor Imaging: The R Package dti |
title_sort |
structural adaptive smoothing in diffusion tensor imaging: the r package dti |
publisher |
Foundation for Open Access Statistics |
series |
Journal of Statistical Software |
issn |
1548-7660 |
publishDate |
2009-09-01 |
description |
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 <b>dti</b> for <b>R</b>, 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. |
topic |
structural adaptive smoothing diffusion weighted imaging diffusion tensor model Rician bias R |
url |
http://www.jstatsoft.org/v31/i09/paper |
work_keys_str_mv |
AT jorgpolzehl structuraladaptivesmoothingindiffusiontensorimagingtherpackagedti AT karstentabelow structuraladaptivesmoothingindiffusiontensorimagingtherpackagedti |
_version_ |
1725567750945898496 |