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Review
. 2020;13(12):30.
doi: 10.1007/s12410-020-09550-2. Epub 2020 Nov 7.

Cardiac Magnetic Resonance in Pulmonary Hypertension-an Update

Samer Alabed  1   2 , Pankaj Garg  1 , Christopher S Johns  1   2 , Faisal Alandejani  1 , Yousef Shahin  1   2 , Krit Dwivedi  1   2 , Hamza Zafar  1 , James M Wild  1   3 , David G Kiely  1   4 , Andrew J Swift  1   2   3
Affiliations
Review

Cardiac Magnetic Resonance in Pulmonary Hypertension-an Update

Samer Alabed et al. Curr Cardiovasc Imaging Rep. 2020.

Abstract

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, T1 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 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.

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.

Keywords: CMR; Cardiac MRI; Pulmonary hypertension.

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Conflict of interest statement

Conflict of InterestThe authors declare that they no conflict of interest.

Figures

Fig. 1
Fig. 1
Images from right ventricular strain analysis in a patient with PH
Fig. 2
Fig. 2
Machine learning feature map. Features compatible with PH are in red and non-PH features are in green
See this image and copyright information in PMC

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