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Evaluations of diffusion tensor image registration based on fiber tractography
BACKGROUND: Diffusion Tensor Magnetic Resonance Imaging (DT-MRI, also known as DTI) measures the diffusion properties of water molecules in tissues and to date is one of the main techniques that can effectively study the microstructures of the brain in vivo. Presently, evaluation of DTI registration...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5234117/ https://www.ncbi.nlm.nih.gov/pubmed/28086899 http://dx.doi.org/10.1186/s12938-016-0299-2 |
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author | Wang, Yi Shen, Yu Liu, Dongyang Li, Guoqin Guo, Zhe Fan, Yangyu Niu, Yilong |
author_facet | Wang, Yi Shen, Yu Liu, Dongyang Li, Guoqin Guo, Zhe Fan, Yangyu Niu, Yilong |
author_sort | Wang, Yi |
collection | PubMed |
description | BACKGROUND: Diffusion Tensor Magnetic Resonance Imaging (DT-MRI, also known as DTI) measures the diffusion properties of water molecules in tissues and to date is one of the main techniques that can effectively study the microstructures of the brain in vivo. Presently, evaluation of DTI registration techniques is still in an initial stage of development. METHODS AND RESULTS: In this paper, six well-known open source DTI registration algorithms: Elastic, Rigid, Affine, DTI-TK, FSL and SyN were applied on 11 subjects from an open-access dataset, among which one was randomly chosen as the template. Eight different fiber bundles of 10 subjects and the template were obtained by drawing regions of interest (ROIs) around various structures using deterministic streamline tractography. The performances of the registration algorithms were evaluated by computing the distances and intersection angles between fiber tracts, as well as the fractional anisotropy (FA) profiles along the fiber tracts. Also, the mean squared error (MSE) and the residual MSE (RMSE) of fibers originating from the registered subjects and the template were calculated to assess the registration algorithm. Twenty-seven different fiber bundles of the 10 subjects and template were obtained by drawing ROIs around various structures using probabilistic tractography. The performances of registration algorithms on this second tractography method were evaluated by computing the spatial correlation similarity of the fibers between subjects as well as between each subject and the template. CONCLUSION: All experimental results indicated that DTI-TK performed the best under the study conditions, and SyN ranked just behind it. |
format | Online Article Text |
id | pubmed-5234117 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-52341172017-01-17 Evaluations of diffusion tensor image registration based on fiber tractography Wang, Yi Shen, Yu Liu, Dongyang Li, Guoqin Guo, Zhe Fan, Yangyu Niu, Yilong Biomed Eng Online Research BACKGROUND: Diffusion Tensor Magnetic Resonance Imaging (DT-MRI, also known as DTI) measures the diffusion properties of water molecules in tissues and to date is one of the main techniques that can effectively study the microstructures of the brain in vivo. Presently, evaluation of DTI registration techniques is still in an initial stage of development. METHODS AND RESULTS: In this paper, six well-known open source DTI registration algorithms: Elastic, Rigid, Affine, DTI-TK, FSL and SyN were applied on 11 subjects from an open-access dataset, among which one was randomly chosen as the template. Eight different fiber bundles of 10 subjects and the template were obtained by drawing regions of interest (ROIs) around various structures using deterministic streamline tractography. The performances of the registration algorithms were evaluated by computing the distances and intersection angles between fiber tracts, as well as the fractional anisotropy (FA) profiles along the fiber tracts. Also, the mean squared error (MSE) and the residual MSE (RMSE) of fibers originating from the registered subjects and the template were calculated to assess the registration algorithm. Twenty-seven different fiber bundles of the 10 subjects and template were obtained by drawing ROIs around various structures using probabilistic tractography. The performances of registration algorithms on this second tractography method were evaluated by computing the spatial correlation similarity of the fibers between subjects as well as between each subject and the template. CONCLUSION: All experimental results indicated that DTI-TK performed the best under the study conditions, and SyN ranked just behind it. BioMed Central 2017-01-10 /pmc/articles/PMC5234117/ /pubmed/28086899 http://dx.doi.org/10.1186/s12938-016-0299-2 Text en © The Author(s) 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Wang, Yi Shen, Yu Liu, Dongyang Li, Guoqin Guo, Zhe Fan, Yangyu Niu, Yilong Evaluations of diffusion tensor image registration based on fiber tractography |
title | Evaluations of diffusion tensor image registration based on fiber tractography |
title_full | Evaluations of diffusion tensor image registration based on fiber tractography |
title_fullStr | Evaluations of diffusion tensor image registration based on fiber tractography |
title_full_unstemmed | Evaluations of diffusion tensor image registration based on fiber tractography |
title_short | Evaluations of diffusion tensor image registration based on fiber tractography |
title_sort | evaluations of diffusion tensor image registration based on fiber tractography |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5234117/ https://www.ncbi.nlm.nih.gov/pubmed/28086899 http://dx.doi.org/10.1186/s12938-016-0299-2 |
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