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Reliability of brain atrophy measurements in multiple sclerosis using MRI: an assessment of six freely available software packages for cross-sectional analyses

PURPOSE: Volume measurement using MRI is important to assess brain atrophy in multiple sclerosis (MS). However, differences between scanners, acquisition protocols, and analysis software introduce unwanted variability of volumes. To quantify theses effects, we compared within-scanner repeatability a...

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Autores principales: van Nederpelt, David R., Amiri, Houshang, Brouwer, Iman, Noteboom, Samantha, Mokkink, Lidwine B., Barkhof, Frederik, Vrenken, Hugo, Kuijer, Joost P. A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10497452/
https://www.ncbi.nlm.nih.gov/pubmed/37526657
http://dx.doi.org/10.1007/s00234-023-03189-8
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author van Nederpelt, David R.
Amiri, Houshang
Brouwer, Iman
Noteboom, Samantha
Mokkink, Lidwine B.
Barkhof, Frederik
Vrenken, Hugo
Kuijer, Joost P. A.
author_facet van Nederpelt, David R.
Amiri, Houshang
Brouwer, Iman
Noteboom, Samantha
Mokkink, Lidwine B.
Barkhof, Frederik
Vrenken, Hugo
Kuijer, Joost P. A.
author_sort van Nederpelt, David R.
collection PubMed
description PURPOSE: Volume measurement using MRI is important to assess brain atrophy in multiple sclerosis (MS). However, differences between scanners, acquisition protocols, and analysis software introduce unwanted variability of volumes. To quantify theses effects, we compared within-scanner repeatability and between-scanner reproducibility of three different MR scanners for six brain segmentation methods. METHODS: Twenty-one people with MS underwent scanning and rescanning on three 3 T MR scanners (GE MR750, Philips Ingenuity, Toshiba Vantage Titan) to obtain 3D T1-weighted images. FreeSurfer, FSL, SAMSEG, FastSurfer, CAT-12, and SynthSeg were used to quantify brain, white matter and (deep) gray matter volumes both from lesion-filled and non-lesion-filled 3D T1-weighted images. We used intra-class correlation coefficient (ICC) to quantify agreement; repeated-measures ANOVA to analyze systematic differences; and variance component analysis to quantify the standard error of measurement (SEM) and smallest detectable change (SDC). RESULTS: For all six software, both between-scanner agreement (ICCs ranging 0.4–1) and within-scanner agreement (ICC range: 0.6–1) were typically good, and good to excellent (ICC > 0.7) for large structures. No clear differences were found between filled and non-filled images. However, gray and white matter volumes did differ systematically between scanners for all software (p < 0.05). Variance component analysis yielded within-scanner SDC ranging from 1.02% (SAMSEG, whole-brain) to 14.55% (FreeSurfer, CSF); and between-scanner SDC ranging from 4.83% (SynthSeg, thalamus) to 29.25% (CAT12, thalamus). CONCLUSION: Volume measurements of brain, GM and WM showed high repeatability, and high reproducibility despite substantial differences between scanners. Smallest detectable change was high, especially between different scanners, which hampers the clinical implementation of atrophy measurements. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00234-023-03189-8.
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spelling pubmed-104974522023-09-14 Reliability of brain atrophy measurements in multiple sclerosis using MRI: an assessment of six freely available software packages for cross-sectional analyses van Nederpelt, David R. Amiri, Houshang Brouwer, Iman Noteboom, Samantha Mokkink, Lidwine B. Barkhof, Frederik Vrenken, Hugo Kuijer, Joost P. A. Neuroradiology Diagnostic Neuroradiology PURPOSE: Volume measurement using MRI is important to assess brain atrophy in multiple sclerosis (MS). However, differences between scanners, acquisition protocols, and analysis software introduce unwanted variability of volumes. To quantify theses effects, we compared within-scanner repeatability and between-scanner reproducibility of three different MR scanners for six brain segmentation methods. METHODS: Twenty-one people with MS underwent scanning and rescanning on three 3 T MR scanners (GE MR750, Philips Ingenuity, Toshiba Vantage Titan) to obtain 3D T1-weighted images. FreeSurfer, FSL, SAMSEG, FastSurfer, CAT-12, and SynthSeg were used to quantify brain, white matter and (deep) gray matter volumes both from lesion-filled and non-lesion-filled 3D T1-weighted images. We used intra-class correlation coefficient (ICC) to quantify agreement; repeated-measures ANOVA to analyze systematic differences; and variance component analysis to quantify the standard error of measurement (SEM) and smallest detectable change (SDC). RESULTS: For all six software, both between-scanner agreement (ICCs ranging 0.4–1) and within-scanner agreement (ICC range: 0.6–1) were typically good, and good to excellent (ICC > 0.7) for large structures. No clear differences were found between filled and non-filled images. However, gray and white matter volumes did differ systematically between scanners for all software (p < 0.05). Variance component analysis yielded within-scanner SDC ranging from 1.02% (SAMSEG, whole-brain) to 14.55% (FreeSurfer, CSF); and between-scanner SDC ranging from 4.83% (SynthSeg, thalamus) to 29.25% (CAT12, thalamus). CONCLUSION: Volume measurements of brain, GM and WM showed high repeatability, and high reproducibility despite substantial differences between scanners. Smallest detectable change was high, especially between different scanners, which hampers the clinical implementation of atrophy measurements. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00234-023-03189-8. Springer Berlin Heidelberg 2023-08-01 2023 /pmc/articles/PMC10497452/ /pubmed/37526657 http://dx.doi.org/10.1007/s00234-023-03189-8 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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 Diagnostic Neuroradiology
van Nederpelt, David R.
Amiri, Houshang
Brouwer, Iman
Noteboom, Samantha
Mokkink, Lidwine B.
Barkhof, Frederik
Vrenken, Hugo
Kuijer, Joost P. A.
Reliability of brain atrophy measurements in multiple sclerosis using MRI: an assessment of six freely available software packages for cross-sectional analyses
title Reliability of brain atrophy measurements in multiple sclerosis using MRI: an assessment of six freely available software packages for cross-sectional analyses
title_full Reliability of brain atrophy measurements in multiple sclerosis using MRI: an assessment of six freely available software packages for cross-sectional analyses
title_fullStr Reliability of brain atrophy measurements in multiple sclerosis using MRI: an assessment of six freely available software packages for cross-sectional analyses
title_full_unstemmed Reliability of brain atrophy measurements in multiple sclerosis using MRI: an assessment of six freely available software packages for cross-sectional analyses
title_short Reliability of brain atrophy measurements in multiple sclerosis using MRI: an assessment of six freely available software packages for cross-sectional analyses
title_sort reliability of brain atrophy measurements in multiple sclerosis using mri: an assessment of six freely available software packages for cross-sectional analyses
topic Diagnostic Neuroradiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10497452/
https://www.ncbi.nlm.nih.gov/pubmed/37526657
http://dx.doi.org/10.1007/s00234-023-03189-8
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