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Semi-automated segmentation of the lateral periventricular regions using diffusion magnetic resonance imaging
The lateral ventricular perimeter (LVP) of the brain is a critical region because in addition to housing neural stem cells required for brain development, it facilitates cerebrospinal fluid (CSF) bulk flow and functions as a blood-CSF barrier to protect periventricular white matter (PVWM) and other...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7492999/ https://www.ncbi.nlm.nih.gov/pubmed/32983918 http://dx.doi.org/10.1016/j.mex.2020.101023 |
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author | Isaacs, Albert M. Han, Rowland H. Smyser, Christopher D. Limbrick, David D. Shimony, Joshua S. |
author_facet | Isaacs, Albert M. Han, Rowland H. Smyser, Christopher D. Limbrick, David D. Shimony, Joshua S. |
author_sort | Isaacs, Albert M. |
collection | PubMed |
description | The lateral ventricular perimeter (LVP) of the brain is a critical region because in addition to housing neural stem cells required for brain development, it facilitates cerebrospinal fluid (CSF) bulk flow and functions as a blood-CSF barrier to protect periventricular white matter (PVWM) and other adjacent regions from injurious toxins. LVP injury is common, particularly among preterm infants who sustain intraventricular hemorrhage or post hemorrhagic hydrocephalus and has been associated with poor neurological outcomes. Assessment of the LVP with diffusion MRI has been challenging, primarily due to issues with partial volume artifacts since the LVP region is in close proximity to CSF and other structures of varying signal intensities that may be inadvertently included in LVP segmentation. This research method presents: • A novel MATLAB-based method to segment a homogenous LVP layer using high spatial resolution parameters (voxel size 1.2 × 1.2 × 1.2 mm(3)) to only capture the innermost layer of the LVP. • The segmented LVP is averaged from three contiguous axial slices to increase signal to noise ratio and reduce the effect of any residual volume averaging effect and eliminates manual and inter/intrarater-related errors. |
format | Online Article Text |
id | pubmed-7492999 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-74929992020-09-24 Semi-automated segmentation of the lateral periventricular regions using diffusion magnetic resonance imaging Isaacs, Albert M. Han, Rowland H. Smyser, Christopher D. Limbrick, David D. Shimony, Joshua S. MethodsX Method Article The lateral ventricular perimeter (LVP) of the brain is a critical region because in addition to housing neural stem cells required for brain development, it facilitates cerebrospinal fluid (CSF) bulk flow and functions as a blood-CSF barrier to protect periventricular white matter (PVWM) and other adjacent regions from injurious toxins. LVP injury is common, particularly among preterm infants who sustain intraventricular hemorrhage or post hemorrhagic hydrocephalus and has been associated with poor neurological outcomes. Assessment of the LVP with diffusion MRI has been challenging, primarily due to issues with partial volume artifacts since the LVP region is in close proximity to CSF and other structures of varying signal intensities that may be inadvertently included in LVP segmentation. This research method presents: • A novel MATLAB-based method to segment a homogenous LVP layer using high spatial resolution parameters (voxel size 1.2 × 1.2 × 1.2 mm(3)) to only capture the innermost layer of the LVP. • The segmented LVP is averaged from three contiguous axial slices to increase signal to noise ratio and reduce the effect of any residual volume averaging effect and eliminates manual and inter/intrarater-related errors. Elsevier 2020-08-20 /pmc/articles/PMC7492999/ /pubmed/32983918 http://dx.doi.org/10.1016/j.mex.2020.101023 Text en © 2020 The Author(s) http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Method Article Isaacs, Albert M. Han, Rowland H. Smyser, Christopher D. Limbrick, David D. Shimony, Joshua S. Semi-automated segmentation of the lateral periventricular regions using diffusion magnetic resonance imaging |
title | Semi-automated segmentation of the lateral periventricular regions using diffusion magnetic resonance imaging |
title_full | Semi-automated segmentation of the lateral periventricular regions using diffusion magnetic resonance imaging |
title_fullStr | Semi-automated segmentation of the lateral periventricular regions using diffusion magnetic resonance imaging |
title_full_unstemmed | Semi-automated segmentation of the lateral periventricular regions using diffusion magnetic resonance imaging |
title_short | Semi-automated segmentation of the lateral periventricular regions using diffusion magnetic resonance imaging |
title_sort | semi-automated segmentation of the lateral periventricular regions using diffusion magnetic resonance imaging |
topic | Method Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7492999/ https://www.ncbi.nlm.nih.gov/pubmed/32983918 http://dx.doi.org/10.1016/j.mex.2020.101023 |
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