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An atlas of white matter anatomy, its variability, and reproducibility based on constrained spherical deconvolution of diffusion MRI
Virtual dissection of white matter (WM) using diffusion MRI tractography is confounded by its poor reproducibility. Despite the increased adoption of advanced reconstruction models, early region-of-interest driven protocols based on diffusion tensor imaging (DTI) remain the dominant reference for vi...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10265547/ https://www.ncbi.nlm.nih.gov/pubmed/35231632 http://dx.doi.org/10.1016/j.neuroimage.2022.119029 |
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author | Radwan, Ahmed M. Sunaert, Stefan Schilling, Kurt Descoteaux, Maxime Landman, Bennett A. Vandenbulcke, Mathieu Theys, Tom Dupont, Patrick Emsell, Louise |
author_facet | Radwan, Ahmed M. Sunaert, Stefan Schilling, Kurt Descoteaux, Maxime Landman, Bennett A. Vandenbulcke, Mathieu Theys, Tom Dupont, Patrick Emsell, Louise |
author_sort | Radwan, Ahmed M. |
collection | PubMed |
description | Virtual dissection of white matter (WM) using diffusion MRI tractography is confounded by its poor reproducibility. Despite the increased adoption of advanced reconstruction models, early region-of-interest driven protocols based on diffusion tensor imaging (DTI) remain the dominant reference for virtual dissection protocols. Here we bridge this gap by providing a comprehensive description of typical WM anatomy reconstructed using a reproducible automated subject-specific parcellation-based approach based on probabilistic constrained-spherical deconvolution (CSD) tractography. We complement this with a WM template in MNI space comprising 68 bundles, including all associated anatomical tract selection labels and associated automated workflows. Additionally, we demonstrate bundle inter- and intra-subject variability using 40 (20 test-retest) datasets from the human connectome project (HCP) and 5 sessions with varying b-values and number of b-shells from the single-subject Multiple Acquisitions for Standardization of Structural Imaging Validation and Evaluation (MASSIVE) dataset. The most reliably reconstructed bundles were the whole pyramidal tracts, primary corticospinal tracts, whole superior longitudinal fasciculi, frontal, parietal and occipital segments of the corpus callosum and middle cerebellar peduncles. More variability was found in less dense bundles, e.g., the fornix, dentato-rubro-thalamic tract (DRTT), and premotor pyramidal tract. Using the DRTT as an example, we show that this variability can be reduced by using a higher number of seeding attempts. Overall inter-session similarity was high for HCP test-retest data (median weighted-dice = 0.963, stdev = 0.201 and IQR = 0.099). Compared to the HCP-template bundles there was a high level of agreement for the HCP test-retest data (median weighted-dice = 0.747, stdev = 0.220 and IQR = 0.277) and for the MASSIVE data (median weighted-dice = 0.767, stdev = 0.255 and IQR = 0.338). In summary, this WM atlas provides an overview of the capabilities and limitations of automated subject-specific probabilistic CSD tractography for mapping white matter fasciculi in healthy adults. It will be most useful in applications requiring a reproducible parcellation-based dissection protocol, and as an educational resource for applied neuroimaging and clinical professionals. |
format | Online Article Text |
id | pubmed-10265547 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
record_format | MEDLINE/PubMed |
spelling | pubmed-102655472023-06-14 An atlas of white matter anatomy, its variability, and reproducibility based on constrained spherical deconvolution of diffusion MRI Radwan, Ahmed M. Sunaert, Stefan Schilling, Kurt Descoteaux, Maxime Landman, Bennett A. Vandenbulcke, Mathieu Theys, Tom Dupont, Patrick Emsell, Louise Neuroimage Article Virtual dissection of white matter (WM) using diffusion MRI tractography is confounded by its poor reproducibility. Despite the increased adoption of advanced reconstruction models, early region-of-interest driven protocols based on diffusion tensor imaging (DTI) remain the dominant reference for virtual dissection protocols. Here we bridge this gap by providing a comprehensive description of typical WM anatomy reconstructed using a reproducible automated subject-specific parcellation-based approach based on probabilistic constrained-spherical deconvolution (CSD) tractography. We complement this with a WM template in MNI space comprising 68 bundles, including all associated anatomical tract selection labels and associated automated workflows. Additionally, we demonstrate bundle inter- and intra-subject variability using 40 (20 test-retest) datasets from the human connectome project (HCP) and 5 sessions with varying b-values and number of b-shells from the single-subject Multiple Acquisitions for Standardization of Structural Imaging Validation and Evaluation (MASSIVE) dataset. The most reliably reconstructed bundles were the whole pyramidal tracts, primary corticospinal tracts, whole superior longitudinal fasciculi, frontal, parietal and occipital segments of the corpus callosum and middle cerebellar peduncles. More variability was found in less dense bundles, e.g., the fornix, dentato-rubro-thalamic tract (DRTT), and premotor pyramidal tract. Using the DRTT as an example, we show that this variability can be reduced by using a higher number of seeding attempts. Overall inter-session similarity was high for HCP test-retest data (median weighted-dice = 0.963, stdev = 0.201 and IQR = 0.099). Compared to the HCP-template bundles there was a high level of agreement for the HCP test-retest data (median weighted-dice = 0.747, stdev = 0.220 and IQR = 0.277) and for the MASSIVE data (median weighted-dice = 0.767, stdev = 0.255 and IQR = 0.338). In summary, this WM atlas provides an overview of the capabilities and limitations of automated subject-specific probabilistic CSD tractography for mapping white matter fasciculi in healthy adults. It will be most useful in applications requiring a reproducible parcellation-based dissection protocol, and as an educational resource for applied neuroimaging and clinical professionals. 2022-07-01 2022-02-26 /pmc/articles/PMC10265547/ /pubmed/35231632 http://dx.doi.org/10.1016/j.neuroimage.2022.119029 Text en https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ) |
spellingShingle | Article Radwan, Ahmed M. Sunaert, Stefan Schilling, Kurt Descoteaux, Maxime Landman, Bennett A. Vandenbulcke, Mathieu Theys, Tom Dupont, Patrick Emsell, Louise An atlas of white matter anatomy, its variability, and reproducibility based on constrained spherical deconvolution of diffusion MRI |
title | An atlas of white matter anatomy, its variability, and reproducibility based on constrained spherical deconvolution of diffusion MRI |
title_full | An atlas of white matter anatomy, its variability, and reproducibility based on constrained spherical deconvolution of diffusion MRI |
title_fullStr | An atlas of white matter anatomy, its variability, and reproducibility based on constrained spherical deconvolution of diffusion MRI |
title_full_unstemmed | An atlas of white matter anatomy, its variability, and reproducibility based on constrained spherical deconvolution of diffusion MRI |
title_short | An atlas of white matter anatomy, its variability, and reproducibility based on constrained spherical deconvolution of diffusion MRI |
title_sort | atlas of white matter anatomy, its variability, and reproducibility based on constrained spherical deconvolution of diffusion mri |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10265547/ https://www.ncbi.nlm.nih.gov/pubmed/35231632 http://dx.doi.org/10.1016/j.neuroimage.2022.119029 |
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