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Cardiac Magnetic Resonance in Pulmonary Hypertension—an Update

Authors
  • Alabed, Samer1, 2
  • Garg, Pankaj1
  • Johns, Christopher S.1, 2
  • Alandejani, Faisal1
  • Shahin, Yousef1, 2
  • Dwivedi, Krit1, 2
  • Zafar, Hamza1
  • Wild, James M1, 3
  • Kiely, David G1, 4
  • Swift, Andrew J1, 2, 3
  • 1 University of Sheffield, Glossop Road, Sheffield, S10 2JF, UK , Sheffield (United Kingdom)
  • 2 Sheffield Teaching Hospitals, Sheffield, UK , Sheffield (United Kingdom)
  • 3 University of Sheffield, Sheffield, UK , Sheffield (United Kingdom)
  • 4 Royal Hallamshire Hospital, Sheffield, UK , Sheffield (United Kingdom)
Type
Published Article
Journal
Current Cardiovascular Imaging Reports
Publisher
Springer US
Publication Date
Nov 07, 2020
Volume
13
Issue
12
Identifiers
DOI: 10.1007/s12410-020-09550-2
Source
Springer Nature
Keywords
License
Green

Abstract

Purpose of ReviewThis 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, T1 mapping, machine learning and ongoing research.Recent FindingsCMR 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 T1 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.SummaryThe 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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