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Highly accelerated free-breathing real-time myocardial tagging for exercise cardiovascular magnetic resonance

BACKGROUND: Exercise cardiovascular magnetic resonance (Ex-CMR) myocardial tagging would enable quantification of myocardial deformation after exercise. However, current electrocardiogram (ECG)-segmented sequences are limited for Ex-CMR. METHODS: We developed a highly accelerated balanced steady-sta...

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Autores principales: Morales, Manuel A., Yoon, Siyeop, Fahmy, Ahmed, Ghanbari, Fahime, Nakamori, Shiro, Rodriguez, Jennifer, Yue, Jennifer, Street, Jordan A., Herzka, Daniel A., Manning, Warren J., Nezafat, Reza
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10544487/
https://www.ncbi.nlm.nih.gov/pubmed/37784153
http://dx.doi.org/10.1186/s12968-023-00961-w
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author Morales, Manuel A.
Yoon, Siyeop
Fahmy, Ahmed
Ghanbari, Fahime
Nakamori, Shiro
Rodriguez, Jennifer
Yue, Jennifer
Street, Jordan A.
Herzka, Daniel A.
Manning, Warren J.
Nezafat, Reza
author_facet Morales, Manuel A.
Yoon, Siyeop
Fahmy, Ahmed
Ghanbari, Fahime
Nakamori, Shiro
Rodriguez, Jennifer
Yue, Jennifer
Street, Jordan A.
Herzka, Daniel A.
Manning, Warren J.
Nezafat, Reza
author_sort Morales, Manuel A.
collection PubMed
description BACKGROUND: Exercise cardiovascular magnetic resonance (Ex-CMR) myocardial tagging would enable quantification of myocardial deformation after exercise. However, current electrocardiogram (ECG)-segmented sequences are limited for Ex-CMR. METHODS: We developed a highly accelerated balanced steady-state free-precession real-time tagging technique for 3 T. A 12-fold acceleration was achieved using incoherent sixfold random Cartesian sampling, twofold truncated outer phase encoding, and a deep learning resolution enhancement model. The technique was tested in two prospective studies. In a rest study of 27 patients referred for clinical CMR and 19 healthy subjects, a set of ECG-segmented for comparison and two sets of real-time tagging images for repeatability assessment were collected in 2-chamber and short-axis views with spatiotemporal resolution 2.0 × 2.0 mm(2) and 29 ms. In an Ex-CMR study of 26 patients with known or suspected cardiac disease and 23 healthy subjects, real-time images were collected before and after exercise. Deformation was quantified using measures of short-axis global circumferential strain (GCS). Two experienced CMR readers evaluated the image quality of all real-time data pooled from both studies using a 4-point Likert scale for tagline quality (1-excellent; 2-good; 3-moderate; 4-poor) and artifact level (1-none; 2-minimal; 3-moderate; 4-significant). Statistical evaluation included Pearson correlation coefficient (r), intraclass correlation coefficient (ICC), and coefficient of variation (CoV). RESULTS: In the rest study, deformation was successfully quantified in 90% of cases. There was a good correlation (r = 0.71) between ECG-segmented and real-time measures of GCS, and repeatability was good to excellent (ICC = 0.86 [0.71, 0.94]) with a CoV of 4.7%. In the Ex-CMR study, deformation was successfully quantified in 96% of subjects pre-exercise and 84% of subjects post-exercise. Short-axis and 2-chamber tagline quality were 1.6 ± 0.7 and 1.9 ± 0.8 at rest and 1.9 ± 0.7 and 2.5 ± 0.8 after exercise, respectively. Short-axis and 2-chamber artifact level was 1.2 ± 0.5 and 1.4 ± 0.7 at rest and 1.3 ± 0.6 and 1.5 ± 0.8 post-exercise, respectively. CONCLUSION: We developed a highly accelerated real-time tagging technique and demonstrated its potential for Ex-CMR quantification of myocardial deformation. Further studies are needed to assess the clinical utility of our technique. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12968-023-00961-w.
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spelling pubmed-105444872023-10-03 Highly accelerated free-breathing real-time myocardial tagging for exercise cardiovascular magnetic resonance Morales, Manuel A. Yoon, Siyeop Fahmy, Ahmed Ghanbari, Fahime Nakamori, Shiro Rodriguez, Jennifer Yue, Jennifer Street, Jordan A. Herzka, Daniel A. Manning, Warren J. Nezafat, Reza J Cardiovasc Magn Reson Technical Notes BACKGROUND: Exercise cardiovascular magnetic resonance (Ex-CMR) myocardial tagging would enable quantification of myocardial deformation after exercise. However, current electrocardiogram (ECG)-segmented sequences are limited for Ex-CMR. METHODS: We developed a highly accelerated balanced steady-state free-precession real-time tagging technique for 3 T. A 12-fold acceleration was achieved using incoherent sixfold random Cartesian sampling, twofold truncated outer phase encoding, and a deep learning resolution enhancement model. The technique was tested in two prospective studies. In a rest study of 27 patients referred for clinical CMR and 19 healthy subjects, a set of ECG-segmented for comparison and two sets of real-time tagging images for repeatability assessment were collected in 2-chamber and short-axis views with spatiotemporal resolution 2.0 × 2.0 mm(2) and 29 ms. In an Ex-CMR study of 26 patients with known or suspected cardiac disease and 23 healthy subjects, real-time images were collected before and after exercise. Deformation was quantified using measures of short-axis global circumferential strain (GCS). Two experienced CMR readers evaluated the image quality of all real-time data pooled from both studies using a 4-point Likert scale for tagline quality (1-excellent; 2-good; 3-moderate; 4-poor) and artifact level (1-none; 2-minimal; 3-moderate; 4-significant). Statistical evaluation included Pearson correlation coefficient (r), intraclass correlation coefficient (ICC), and coefficient of variation (CoV). RESULTS: In the rest study, deformation was successfully quantified in 90% of cases. There was a good correlation (r = 0.71) between ECG-segmented and real-time measures of GCS, and repeatability was good to excellent (ICC = 0.86 [0.71, 0.94]) with a CoV of 4.7%. In the Ex-CMR study, deformation was successfully quantified in 96% of subjects pre-exercise and 84% of subjects post-exercise. Short-axis and 2-chamber tagline quality were 1.6 ± 0.7 and 1.9 ± 0.8 at rest and 1.9 ± 0.7 and 2.5 ± 0.8 after exercise, respectively. Short-axis and 2-chamber artifact level was 1.2 ± 0.5 and 1.4 ± 0.7 at rest and 1.3 ± 0.6 and 1.5 ± 0.8 post-exercise, respectively. CONCLUSION: We developed a highly accelerated real-time tagging technique and demonstrated its potential for Ex-CMR quantification of myocardial deformation. Further studies are needed to assess the clinical utility of our technique. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12968-023-00961-w. BioMed Central 2023-10-02 /pmc/articles/PMC10544487/ /pubmed/37784153 http://dx.doi.org/10.1186/s12968-023-00961-w 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/) . 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 Technical Notes
Morales, Manuel A.
Yoon, Siyeop
Fahmy, Ahmed
Ghanbari, Fahime
Nakamori, Shiro
Rodriguez, Jennifer
Yue, Jennifer
Street, Jordan A.
Herzka, Daniel A.
Manning, Warren J.
Nezafat, Reza
Highly accelerated free-breathing real-time myocardial tagging for exercise cardiovascular magnetic resonance
title Highly accelerated free-breathing real-time myocardial tagging for exercise cardiovascular magnetic resonance
title_full Highly accelerated free-breathing real-time myocardial tagging for exercise cardiovascular magnetic resonance
title_fullStr Highly accelerated free-breathing real-time myocardial tagging for exercise cardiovascular magnetic resonance
title_full_unstemmed Highly accelerated free-breathing real-time myocardial tagging for exercise cardiovascular magnetic resonance
title_short Highly accelerated free-breathing real-time myocardial tagging for exercise cardiovascular magnetic resonance
title_sort highly accelerated free-breathing real-time myocardial tagging for exercise cardiovascular magnetic resonance
topic Technical Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10544487/
https://www.ncbi.nlm.nih.gov/pubmed/37784153
http://dx.doi.org/10.1186/s12968-023-00961-w
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