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Cardiac Magnetic Resonance in Pulmonary Hypertension—an Update

PURPOSE OF REVIEW: This article reviews advances over the past 3 years in cardiac magnetic resonance (CMR) imaging in pulmonary hypertension (PH). We aim to bring the reader up-to-date with CMR applications in diagnosis, prognosis, 4D flow, strain analysis, T(1) mapping, machine learning and ongoing...

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Autores principales: Alabed, Samer, Garg, Pankaj, Johns, Christopher S., Alandejani, Faisal, Shahin, Yousef, Dwivedi, Krit, Zafar, Hamza, Wild, James M, Kiely, David G, Swift, Andrew J
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7648000/
https://www.ncbi.nlm.nih.gov/pubmed/33184585
http://dx.doi.org/10.1007/s12410-020-09550-2
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author Alabed, Samer
Garg, Pankaj
Johns, Christopher S.
Alandejani, Faisal
Shahin, Yousef
Dwivedi, Krit
Zafar, Hamza
Wild, James M
Kiely, David G
Swift, Andrew J
author_facet Alabed, Samer
Garg, Pankaj
Johns, Christopher S.
Alandejani, Faisal
Shahin, Yousef
Dwivedi, Krit
Zafar, Hamza
Wild, James M
Kiely, David G
Swift, Andrew J
author_sort Alabed, Samer
collection PubMed
description PURPOSE OF REVIEW: This article reviews advances over the past 3 years in cardiac magnetic resonance (CMR) imaging in pulmonary hypertension (PH). We aim to bring the reader up-to-date with CMR applications in diagnosis, prognosis, 4D flow, strain analysis, T(1) mapping, machine learning and ongoing research. RECENT FINDINGS: CMR volumetric and functional metrics are now established as valuable prognostic markers in PH. This imaging modality is increasingly used to assess treatment response and improves risk stratification when incorporated into PH risk scores. Emerging techniques such as myocardial T(1) mapping may play a role in the follow-up of selected patients. Myocardial strain may be used as an early marker for right and left ventricular dysfunction and a predictor for mortality. Machine learning has offered a glimpse into future possibilities. Ongoing research of new PH therapies is increasingly using CMR as a clinical endpoint. SUMMARY: The last 3 years have seen several large studies establishing CMR as a valuable diagnostic and prognostic tool in patients with PH, with CMR increasingly considered as an endpoint in clinical trials of PH therapies. Machine learning approaches to improve automation and accuracy of CMR metrics and identify imaging features of PH is an area of active research interest with promising clinical utility.
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spelling pubmed-76480002020-11-10 Cardiac Magnetic Resonance in Pulmonary Hypertension—an Update Alabed, Samer Garg, Pankaj Johns, Christopher S. Alandejani, Faisal Shahin, Yousef Dwivedi, Krit Zafar, Hamza Wild, James M Kiely, David G Swift, Andrew J Curr Cardiovasc Imaging Rep Cardiac Magnetic Resonance (V. Puntmann, Section Editor) PURPOSE OF REVIEW: This article reviews advances over the past 3 years in cardiac magnetic resonance (CMR) imaging in pulmonary hypertension (PH). We aim to bring the reader up-to-date with CMR applications in diagnosis, prognosis, 4D flow, strain analysis, T(1) mapping, machine learning and ongoing research. RECENT FINDINGS: CMR volumetric and functional metrics are now established as valuable prognostic markers in PH. This imaging modality is increasingly used to assess treatment response and improves risk stratification when incorporated into PH risk scores. Emerging techniques such as myocardial T(1) mapping may play a role in the follow-up of selected patients. Myocardial strain may be used as an early marker for right and left ventricular dysfunction and a predictor for mortality. Machine learning has offered a glimpse into future possibilities. Ongoing research of new PH therapies is increasingly using CMR as a clinical endpoint. SUMMARY: The last 3 years have seen several large studies establishing CMR as a valuable diagnostic and prognostic tool in patients with PH, with CMR increasingly considered as an endpoint in clinical trials of PH therapies. Machine learning approaches to improve automation and accuracy of CMR metrics and identify imaging features of PH is an area of active research interest with promising clinical utility. Springer US 2020-11-07 2020 /pmc/articles/PMC7648000/ /pubmed/33184585 http://dx.doi.org/10.1007/s12410-020-09550-2 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 Cardiac Magnetic Resonance (V. Puntmann, Section Editor)
Alabed, Samer
Garg, Pankaj
Johns, Christopher S.
Alandejani, Faisal
Shahin, Yousef
Dwivedi, Krit
Zafar, Hamza
Wild, James M
Kiely, David G
Swift, Andrew J
Cardiac Magnetic Resonance in Pulmonary Hypertension—an Update
title Cardiac Magnetic Resonance in Pulmonary Hypertension—an Update
title_full Cardiac Magnetic Resonance in Pulmonary Hypertension—an Update
title_fullStr Cardiac Magnetic Resonance in Pulmonary Hypertension—an Update
title_full_unstemmed Cardiac Magnetic Resonance in Pulmonary Hypertension—an Update
title_short Cardiac Magnetic Resonance in Pulmonary Hypertension—an Update
title_sort cardiac magnetic resonance in pulmonary hypertension—an update
topic Cardiac Magnetic Resonance (V. Puntmann, Section Editor)
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7648000/
https://www.ncbi.nlm.nih.gov/pubmed/33184585
http://dx.doi.org/10.1007/s12410-020-09550-2
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