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Automated MRI perfusion-diffusion mismatch estimation may be significantly different in individual patients when using different software packages
OBJECTIVE: To compare two established software applications in terms of apparent diffusion coefficient (ADC) lesion volumes, volume of critically hypoperfused brain tissue, and calculated volumes of perfusion-diffusion mismatch in brain MRI of patients with acute ischemic stroke. METHODS: Brain MRI...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7813720/ https://www.ncbi.nlm.nih.gov/pubmed/32822053 http://dx.doi.org/10.1007/s00330-020-07150-8 |
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author | Deutschmann, Hannes Hinteregger, Nicole Wießpeiner, Ulrike Kneihsl, Markus Fandler-Höfler, Simon Michenthaler, Manuela Enzinger, Christian Hassler, Eva Leber, Stefan Reishofer, Gernot |
author_facet | Deutschmann, Hannes Hinteregger, Nicole Wießpeiner, Ulrike Kneihsl, Markus Fandler-Höfler, Simon Michenthaler, Manuela Enzinger, Christian Hassler, Eva Leber, Stefan Reishofer, Gernot |
author_sort | Deutschmann, Hannes |
collection | PubMed |
description | OBJECTIVE: To compare two established software applications in terms of apparent diffusion coefficient (ADC) lesion volumes, volume of critically hypoperfused brain tissue, and calculated volumes of perfusion-diffusion mismatch in brain MRI of patients with acute ischemic stroke. METHODS: Brain MRI examinations of 81 patients with acute stroke due to large vessel occlusion of the anterior circulation were analyzed. The volume of hypoperfused brain tissue, ADC volume, and the volume of perfusion-diffusion mismatch were calculated automatically with two different software packages. The calculated parameters were compared quantitatively using formal statistics. RESULTS: Significant difference was found for the volume of hypoperfused tissue (median 91.0 ml vs. 102.2 ml; p < 0.05) and the ADC volume (median 30.0 ml vs. 23.9 ml; p < 0.05) between different software packages. The volume of the perfusion-diffusion mismatch differed significantly (median 47.0 ml vs. 67.2 ml; p < 0.05). Evaluation of the results on a single-subject basis revealed a mean absolute difference of 20.5 ml for hypoperfused tissue, 10.8 ml for ADC volumes, and 27.6 ml for mismatch volumes, respectively. Application of the DEFUSE 3 threshold of 70 ml infarction core would have resulted in dissenting treatment decisions in 6/81 (7.4%) patients. CONCLUSION: Volume segmentation in different software products may lead to significantly different results in the individual patient and may thus seriously influence the decision for or against mechanical thrombectomy. KEY POINTS: • Automated calculation of MRI perfusion-diffusion mismatch helps clinicians to apply inclusion and exclusion criteria derived from randomized trials. • Infarct volume segmentation plays a crucial role and lead to significantly different result for different computer programs. • Perfusion-diffusion mismatch estimation from different computer programs may influence the decision for or against mechanical thrombectomy. |
format | Online Article Text |
id | pubmed-7813720 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-78137202021-01-25 Automated MRI perfusion-diffusion mismatch estimation may be significantly different in individual patients when using different software packages Deutschmann, Hannes Hinteregger, Nicole Wießpeiner, Ulrike Kneihsl, Markus Fandler-Höfler, Simon Michenthaler, Manuela Enzinger, Christian Hassler, Eva Leber, Stefan Reishofer, Gernot Eur Radiol Neuro OBJECTIVE: To compare two established software applications in terms of apparent diffusion coefficient (ADC) lesion volumes, volume of critically hypoperfused brain tissue, and calculated volumes of perfusion-diffusion mismatch in brain MRI of patients with acute ischemic stroke. METHODS: Brain MRI examinations of 81 patients with acute stroke due to large vessel occlusion of the anterior circulation were analyzed. The volume of hypoperfused brain tissue, ADC volume, and the volume of perfusion-diffusion mismatch were calculated automatically with two different software packages. The calculated parameters were compared quantitatively using formal statistics. RESULTS: Significant difference was found for the volume of hypoperfused tissue (median 91.0 ml vs. 102.2 ml; p < 0.05) and the ADC volume (median 30.0 ml vs. 23.9 ml; p < 0.05) between different software packages. The volume of the perfusion-diffusion mismatch differed significantly (median 47.0 ml vs. 67.2 ml; p < 0.05). Evaluation of the results on a single-subject basis revealed a mean absolute difference of 20.5 ml for hypoperfused tissue, 10.8 ml for ADC volumes, and 27.6 ml for mismatch volumes, respectively. Application of the DEFUSE 3 threshold of 70 ml infarction core would have resulted in dissenting treatment decisions in 6/81 (7.4%) patients. CONCLUSION: Volume segmentation in different software products may lead to significantly different results in the individual patient and may thus seriously influence the decision for or against mechanical thrombectomy. KEY POINTS: • Automated calculation of MRI perfusion-diffusion mismatch helps clinicians to apply inclusion and exclusion criteria derived from randomized trials. • Infarct volume segmentation plays a crucial role and lead to significantly different result for different computer programs. • Perfusion-diffusion mismatch estimation from different computer programs may influence the decision for or against mechanical thrombectomy. Springer Berlin Heidelberg 2020-08-21 2021 /pmc/articles/PMC7813720/ /pubmed/32822053 http://dx.doi.org/10.1007/s00330-020-07150-8 Text en © The Author(s) 2020 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/. |
spellingShingle | Neuro Deutschmann, Hannes Hinteregger, Nicole Wießpeiner, Ulrike Kneihsl, Markus Fandler-Höfler, Simon Michenthaler, Manuela Enzinger, Christian Hassler, Eva Leber, Stefan Reishofer, Gernot Automated MRI perfusion-diffusion mismatch estimation may be significantly different in individual patients when using different software packages |
title | Automated MRI perfusion-diffusion mismatch estimation may be significantly different in individual patients when using different software packages |
title_full | Automated MRI perfusion-diffusion mismatch estimation may be significantly different in individual patients when using different software packages |
title_fullStr | Automated MRI perfusion-diffusion mismatch estimation may be significantly different in individual patients when using different software packages |
title_full_unstemmed | Automated MRI perfusion-diffusion mismatch estimation may be significantly different in individual patients when using different software packages |
title_short | Automated MRI perfusion-diffusion mismatch estimation may be significantly different in individual patients when using different software packages |
title_sort | automated mri perfusion-diffusion mismatch estimation may be significantly different in individual patients when using different software packages |
topic | Neuro |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7813720/ https://www.ncbi.nlm.nih.gov/pubmed/32822053 http://dx.doi.org/10.1007/s00330-020-07150-8 |
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