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Automated mitral valve vortex ring extraction from 4D‐flow MRI

PURPOSE: To present and validate a method for automated extraction and analysis of the temporal evolution of the mitral valve (MV) vortex ring from MR 4D‐flow data. METHODS: The proposed algorithm uses the divergence‐free part of the velocity vector field for Q criterion‐based identification and tra...

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Autores principales: Kräuter, Corina, Reiter, Ursula, Reiter, Clemens, Nizhnikava, Volha, Masana, Marc, Schmidt, Albrecht, Fuchsjäger, Michael, Stollberger, Rudolf, Reiter, Gert
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7540523/
https://www.ncbi.nlm.nih.gov/pubmed/32557819
http://dx.doi.org/10.1002/mrm.28361
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author Kräuter, Corina
Reiter, Ursula
Reiter, Clemens
Nizhnikava, Volha
Masana, Marc
Schmidt, Albrecht
Fuchsjäger, Michael
Stollberger, Rudolf
Reiter, Gert
author_facet Kräuter, Corina
Reiter, Ursula
Reiter, Clemens
Nizhnikava, Volha
Masana, Marc
Schmidt, Albrecht
Fuchsjäger, Michael
Stollberger, Rudolf
Reiter, Gert
author_sort Kräuter, Corina
collection PubMed
description PURPOSE: To present and validate a method for automated extraction and analysis of the temporal evolution of the mitral valve (MV) vortex ring from MR 4D‐flow data. METHODS: The proposed algorithm uses the divergence‐free part of the velocity vector field for Q criterion‐based identification and tracking of MV vortex ring core and region within the left ventricle (LV). The 4D‐flow data of 20 subjects (10 healthy controls, 10 patients with ischemic heart disease) were used to validate the algorithm against visual analysis as well as to assess the method’s sensitivity to manual LV segmentation. Quantitative MV vortex ring parameters were analyzed with respect to both their differences between healthy subjects and patients and their correlation with transmitral peak velocities. RESULTS: The algorithm successfully extracted MV vortex rings throughout the entire cardiac cycle, which agreed substantially with visual analysis (Cohen’s kappa = 0.77). Furthermore, vortex cores and regions were robustly detected even if a static end‐diastolic LV segmentation mask was applied to all frames (Dice coefficients 0.82 ± 0.08 and 0.94 ± 0.02 for core and region, respectively). Early diastolic MV vortex ring vorticity, kinetic energy and circularity index differed significantly between healthy controls and patients. In contrast to vortex shape parameters, vorticity and kinetic energy correlated strongly with transmitral peak velocities. CONCLUSION: An automated method for temporal MV vortex ring extraction demonstrating robustness with respect to LV segmentation strategies is introduced. Quantitative vortex parameter analysis indicates importance of the MV vortex ring for LV diastolic (dys)function.
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spelling pubmed-75405232020-10-09 Automated mitral valve vortex ring extraction from 4D‐flow MRI Kräuter, Corina Reiter, Ursula Reiter, Clemens Nizhnikava, Volha Masana, Marc Schmidt, Albrecht Fuchsjäger, Michael Stollberger, Rudolf Reiter, Gert Magn Reson Med Full Papers—Computer Processing and Modeling PURPOSE: To present and validate a method for automated extraction and analysis of the temporal evolution of the mitral valve (MV) vortex ring from MR 4D‐flow data. METHODS: The proposed algorithm uses the divergence‐free part of the velocity vector field for Q criterion‐based identification and tracking of MV vortex ring core and region within the left ventricle (LV). The 4D‐flow data of 20 subjects (10 healthy controls, 10 patients with ischemic heart disease) were used to validate the algorithm against visual analysis as well as to assess the method’s sensitivity to manual LV segmentation. Quantitative MV vortex ring parameters were analyzed with respect to both their differences between healthy subjects and patients and their correlation with transmitral peak velocities. RESULTS: The algorithm successfully extracted MV vortex rings throughout the entire cardiac cycle, which agreed substantially with visual analysis (Cohen’s kappa = 0.77). Furthermore, vortex cores and regions were robustly detected even if a static end‐diastolic LV segmentation mask was applied to all frames (Dice coefficients 0.82 ± 0.08 and 0.94 ± 0.02 for core and region, respectively). Early diastolic MV vortex ring vorticity, kinetic energy and circularity index differed significantly between healthy controls and patients. In contrast to vortex shape parameters, vorticity and kinetic energy correlated strongly with transmitral peak velocities. CONCLUSION: An automated method for temporal MV vortex ring extraction demonstrating robustness with respect to LV segmentation strategies is introduced. Quantitative vortex parameter analysis indicates importance of the MV vortex ring for LV diastolic (dys)function. John Wiley and Sons Inc. 2020-06-18 2020-12 /pmc/articles/PMC7540523/ /pubmed/32557819 http://dx.doi.org/10.1002/mrm.28361 Text en © 2020 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Full Papers—Computer Processing and Modeling
Kräuter, Corina
Reiter, Ursula
Reiter, Clemens
Nizhnikava, Volha
Masana, Marc
Schmidt, Albrecht
Fuchsjäger, Michael
Stollberger, Rudolf
Reiter, Gert
Automated mitral valve vortex ring extraction from 4D‐flow MRI
title Automated mitral valve vortex ring extraction from 4D‐flow MRI
title_full Automated mitral valve vortex ring extraction from 4D‐flow MRI
title_fullStr Automated mitral valve vortex ring extraction from 4D‐flow MRI
title_full_unstemmed Automated mitral valve vortex ring extraction from 4D‐flow MRI
title_short Automated mitral valve vortex ring extraction from 4D‐flow MRI
title_sort automated mitral valve vortex ring extraction from 4d‐flow mri
topic Full Papers—Computer Processing and Modeling
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7540523/
https://www.ncbi.nlm.nih.gov/pubmed/32557819
http://dx.doi.org/10.1002/mrm.28361
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