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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...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2020
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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. |
format | Online Article Text |
id | pubmed-7648000 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
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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