Cargando…
Multivendor comparison of global and regional 2D cardiovascular magnetic resonance feature tracking strains vs tissue tagging at 3T
BACKGROUND: Cardiovascular magnetic resonance (CMR) 2D feature tracking (FT) left ventricular (LV) myocardial strain has seen widespread use to characterize myocardial deformation. Yet, validation of CMR FT measurements remains scarce, particularly for regional strain. Therefore, we aimed to perform...
Autores principales: | , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8117295/ https://www.ncbi.nlm.nih.gov/pubmed/33980259 http://dx.doi.org/10.1186/s12968-021-00742-3 |
_version_ | 1783691565079724032 |
---|---|
author | Militaru, Sebastian Panovsky, Roman Hanet, Vincent Amzulescu, Mihaela Silvia Langet, Hélène Pisciotti, Mary Mojica Pouleur, Anne-Catherine Vanoverschelde, Jean-Louis J. Gerber, Bernhard L. |
author_facet | Militaru, Sebastian Panovsky, Roman Hanet, Vincent Amzulescu, Mihaela Silvia Langet, Hélène Pisciotti, Mary Mojica Pouleur, Anne-Catherine Vanoverschelde, Jean-Louis J. Gerber, Bernhard L. |
author_sort | Militaru, Sebastian |
collection | PubMed |
description | BACKGROUND: Cardiovascular magnetic resonance (CMR) 2D feature tracking (FT) left ventricular (LV) myocardial strain has seen widespread use to characterize myocardial deformation. Yet, validation of CMR FT measurements remains scarce, particularly for regional strain. Therefore, we aimed to perform intervendor comparison of 3 different FT software against tagging. METHODS: In 61 subjects (18 healthy subjects, 18 patients with chronic myocardial infarction, 15 with dilated cardiomyopathy, and 10 with LV hypertrophy due to hypertrophic cardiomyopathy or aortic stenosis) were prospectively compared global (G) and regional transmural peak-systolic Lagrangian longitudinal (LS), circumferential (CS) and radial strains (RS) by 3 FT software (cvi42, Segment, and Tomtec) among each other and with tagging at 3T. We also evaluated the ability of regional LS, CS, and RS by different FT software vs tagging to identify late gadolinium enhancement (LGE) in the 18 infarct patients. RESULTS: GLS and GCS by all 3 software had an excellent agreement among each other (ICC = 0.94–0.98 for GLS and ICC = 0.96–0.98 for GCS respectively) and against tagging (ICC = 0.92–0.94 for GLS and ICC = 0.88–0.91 for GCS respectively), while GRS showed inconsistent agreement between vendors (ICC 0.10–0.81). For regional LS, the agreement was good (ICC = 0.68) between 2 vendors but less vs the 3(rd) (ICC 0.50–0.59) and moderate to poor (ICC 0.44–0.47) between all three FT software and tagging. Also, for regional CS agreement between 2 software was higher (ICC = 0.80) than against the 3rd (ICC = 0.58–0.60), and both better agreed with tagging (ICC = 0.70–0.72) than the 3rd (ICC = 0.57). Regional RS had more variation in the agreement between methods ranging from good (ICC = 0.75) to poor (ICC = 0.05). Finally, the accuracy of scar detection by regional strains differed among the 3 FT software. While the accuracy of regional LS was similar, CS by one software was less accurate (AUC 0.68) than tagging (AUC 0.80, p < 0.006) and RS less accurate (AUC 0.578) than the other two (AUC 0.76 and 0.73, p < 0.02) to discriminate segments with LGE. CONCLUSIONS: We confirm good agreement of CMR FT and little intervendor difference for GLS and GCS evaluation, with variable agreement for GRS. For regional strain evaluation, intervendor difference was larger, especially for RS, and the diagnostic performance varied more substantially among different vendors for regional strain analysis. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12968-021-00742-3. |
format | Online Article Text |
id | pubmed-8117295 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-81172952021-05-13 Multivendor comparison of global and regional 2D cardiovascular magnetic resonance feature tracking strains vs tissue tagging at 3T Militaru, Sebastian Panovsky, Roman Hanet, Vincent Amzulescu, Mihaela Silvia Langet, Hélène Pisciotti, Mary Mojica Pouleur, Anne-Catherine Vanoverschelde, Jean-Louis J. Gerber, Bernhard L. J Cardiovasc Magn Reson Research BACKGROUND: Cardiovascular magnetic resonance (CMR) 2D feature tracking (FT) left ventricular (LV) myocardial strain has seen widespread use to characterize myocardial deformation. Yet, validation of CMR FT measurements remains scarce, particularly for regional strain. Therefore, we aimed to perform intervendor comparison of 3 different FT software against tagging. METHODS: In 61 subjects (18 healthy subjects, 18 patients with chronic myocardial infarction, 15 with dilated cardiomyopathy, and 10 with LV hypertrophy due to hypertrophic cardiomyopathy or aortic stenosis) were prospectively compared global (G) and regional transmural peak-systolic Lagrangian longitudinal (LS), circumferential (CS) and radial strains (RS) by 3 FT software (cvi42, Segment, and Tomtec) among each other and with tagging at 3T. We also evaluated the ability of regional LS, CS, and RS by different FT software vs tagging to identify late gadolinium enhancement (LGE) in the 18 infarct patients. RESULTS: GLS and GCS by all 3 software had an excellent agreement among each other (ICC = 0.94–0.98 for GLS and ICC = 0.96–0.98 for GCS respectively) and against tagging (ICC = 0.92–0.94 for GLS and ICC = 0.88–0.91 for GCS respectively), while GRS showed inconsistent agreement between vendors (ICC 0.10–0.81). For regional LS, the agreement was good (ICC = 0.68) between 2 vendors but less vs the 3(rd) (ICC 0.50–0.59) and moderate to poor (ICC 0.44–0.47) between all three FT software and tagging. Also, for regional CS agreement between 2 software was higher (ICC = 0.80) than against the 3rd (ICC = 0.58–0.60), and both better agreed with tagging (ICC = 0.70–0.72) than the 3rd (ICC = 0.57). Regional RS had more variation in the agreement between methods ranging from good (ICC = 0.75) to poor (ICC = 0.05). Finally, the accuracy of scar detection by regional strains differed among the 3 FT software. While the accuracy of regional LS was similar, CS by one software was less accurate (AUC 0.68) than tagging (AUC 0.80, p < 0.006) and RS less accurate (AUC 0.578) than the other two (AUC 0.76 and 0.73, p < 0.02) to discriminate segments with LGE. CONCLUSIONS: We confirm good agreement of CMR FT and little intervendor difference for GLS and GCS evaluation, with variable agreement for GRS. For regional strain evaluation, intervendor difference was larger, especially for RS, and the diagnostic performance varied more substantially among different vendors for regional strain analysis. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12968-021-00742-3. BioMed Central 2021-05-13 /pmc/articles/PMC8117295/ /pubmed/33980259 http://dx.doi.org/10.1186/s12968-021-00742-3 Text en © The Author(s) 2021 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Militaru, Sebastian Panovsky, Roman Hanet, Vincent Amzulescu, Mihaela Silvia Langet, Hélène Pisciotti, Mary Mojica Pouleur, Anne-Catherine Vanoverschelde, Jean-Louis J. Gerber, Bernhard L. Multivendor comparison of global and regional 2D cardiovascular magnetic resonance feature tracking strains vs tissue tagging at 3T |
title | Multivendor comparison of global and regional 2D cardiovascular magnetic resonance feature tracking strains vs tissue tagging at 3T |
title_full | Multivendor comparison of global and regional 2D cardiovascular magnetic resonance feature tracking strains vs tissue tagging at 3T |
title_fullStr | Multivendor comparison of global and regional 2D cardiovascular magnetic resonance feature tracking strains vs tissue tagging at 3T |
title_full_unstemmed | Multivendor comparison of global and regional 2D cardiovascular magnetic resonance feature tracking strains vs tissue tagging at 3T |
title_short | Multivendor comparison of global and regional 2D cardiovascular magnetic resonance feature tracking strains vs tissue tagging at 3T |
title_sort | multivendor comparison of global and regional 2d cardiovascular magnetic resonance feature tracking strains vs tissue tagging at 3t |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8117295/ https://www.ncbi.nlm.nih.gov/pubmed/33980259 http://dx.doi.org/10.1186/s12968-021-00742-3 |
work_keys_str_mv | AT militarusebastian multivendorcomparisonofglobalandregional2dcardiovascularmagneticresonancefeaturetrackingstrainsvstissuetaggingat3t AT panovskyroman multivendorcomparisonofglobalandregional2dcardiovascularmagneticresonancefeaturetrackingstrainsvstissuetaggingat3t AT hanetvincent multivendorcomparisonofglobalandregional2dcardiovascularmagneticresonancefeaturetrackingstrainsvstissuetaggingat3t AT amzulescumihaelasilvia multivendorcomparisonofglobalandregional2dcardiovascularmagneticresonancefeaturetrackingstrainsvstissuetaggingat3t AT langethelene multivendorcomparisonofglobalandregional2dcardiovascularmagneticresonancefeaturetrackingstrainsvstissuetaggingat3t AT pisciottimarymojica multivendorcomparisonofglobalandregional2dcardiovascularmagneticresonancefeaturetrackingstrainsvstissuetaggingat3t AT pouleurannecatherine multivendorcomparisonofglobalandregional2dcardiovascularmagneticresonancefeaturetrackingstrainsvstissuetaggingat3t AT vanoverscheldejeanlouisj multivendorcomparisonofglobalandregional2dcardiovascularmagneticresonancefeaturetrackingstrainsvstissuetaggingat3t AT gerberbernhardl multivendorcomparisonofglobalandregional2dcardiovascularmagneticresonancefeaturetrackingstrainsvstissuetaggingat3t |