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Accurate image-based CSF volume calculation of the lateral ventricles
The size/volume of the brain’s ventricles is essential in diagnosing and treating many neurological disorders, with various forms of hydrocephalus being some of the most common. Initial ventricular size and changes, if any, in response to disease progression or therapeutic intervention are monitored...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9287564/ https://www.ncbi.nlm.nih.gov/pubmed/35840587 http://dx.doi.org/10.1038/s41598-022-15995-w |
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author | Yepes-Calderon, Fernando McComb, J. Gordon |
author_facet | Yepes-Calderon, Fernando McComb, J. Gordon |
author_sort | Yepes-Calderon, Fernando |
collection | PubMed |
description | The size/volume of the brain’s ventricles is essential in diagnosing and treating many neurological disorders, with various forms of hydrocephalus being some of the most common. Initial ventricular size and changes, if any, in response to disease progression or therapeutic intervention are monitored by serial imaging methods. Significant variance in ventricular size is readily noted, but small incremental changes can be challenging to appreciate. We have previously reported using artificial intelligence to determine ventricular volume. The values obtained were compared with those calculated using the inaccurate manual segmentation as the “gold standard”. This document introduces a strategy to measure ventricular volumes where manual segmentation is not employed to validate the estimations. Instead, we created 3D printed models that mimic the lateral ventricles and measured those 3D models’ volume with a tuned water displacement device. The 3D models are placed in a gel and taken to the magnetic resonance scanner. Images extracted from the phantoms are fed to an artificial intelligence-based algorithm. The volumes yielded by the automation must equal those yielded by water displacement to assert validation. Then, we provide certified volumes for subjects in the age range (1–114) months old and two hydrocephalus patients. |
format | Online Article Text |
id | pubmed-9287564 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-92875642022-07-17 Accurate image-based CSF volume calculation of the lateral ventricles Yepes-Calderon, Fernando McComb, J. Gordon Sci Rep Article The size/volume of the brain’s ventricles is essential in diagnosing and treating many neurological disorders, with various forms of hydrocephalus being some of the most common. Initial ventricular size and changes, if any, in response to disease progression or therapeutic intervention are monitored by serial imaging methods. Significant variance in ventricular size is readily noted, but small incremental changes can be challenging to appreciate. We have previously reported using artificial intelligence to determine ventricular volume. The values obtained were compared with those calculated using the inaccurate manual segmentation as the “gold standard”. This document introduces a strategy to measure ventricular volumes where manual segmentation is not employed to validate the estimations. Instead, we created 3D printed models that mimic the lateral ventricles and measured those 3D models’ volume with a tuned water displacement device. The 3D models are placed in a gel and taken to the magnetic resonance scanner. Images extracted from the phantoms are fed to an artificial intelligence-based algorithm. The volumes yielded by the automation must equal those yielded by water displacement to assert validation. Then, we provide certified volumes for subjects in the age range (1–114) months old and two hydrocephalus patients. Nature Publishing Group UK 2022-07-15 /pmc/articles/PMC9287564/ /pubmed/35840587 http://dx.doi.org/10.1038/s41598-022-15995-w Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Yepes-Calderon, Fernando McComb, J. Gordon Accurate image-based CSF volume calculation of the lateral ventricles |
title | Accurate image-based CSF volume calculation of the lateral ventricles |
title_full | Accurate image-based CSF volume calculation of the lateral ventricles |
title_fullStr | Accurate image-based CSF volume calculation of the lateral ventricles |
title_full_unstemmed | Accurate image-based CSF volume calculation of the lateral ventricles |
title_short | Accurate image-based CSF volume calculation of the lateral ventricles |
title_sort | accurate image-based csf volume calculation of the lateral ventricles |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9287564/ https://www.ncbi.nlm.nih.gov/pubmed/35840587 http://dx.doi.org/10.1038/s41598-022-15995-w |
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