Integrated Cardiopulmonary MRI Assessment of Pulmonary Hypertension.
Laura C SaundersPaul J C HughesSamer AlabedDavid J CapenerHelen MarshallJens Vogel-ClaussenEdwin J R van BeekDavid G KielyAndrew J SwiftJames M WildPublished in: Journal of magnetic resonance imaging : JMRI (2021)
Pulmonary hypertension (PH) is a heterogeneous condition that can affect the lung parenchyma, pulmonary vasculature, and cardiac chambers. Accurate diagnosis often requires multiple complex assessments of the cardiac and pulmonary systems. MRI is able to comprehensively assess cardiac structure and function, as well as lung parenchymal, pulmonary vascular, and functional lung changes. Therefore, MRI has the potential to provide an integrated functional and structural assessment of the cardiopulmonary system in a single exam. Cardiac MRI is used in the assessment of PH in most large PH centers, whereas lung MRI is an emerging technique in patients with PH. This article reviews the current literature on cardiopulmonary MRI in PH, including cine MRI, black-blood imaging, late gadolinium enhancement, T1 mapping, myocardial strain analysis, contrast-enhanced perfusion imaging and contrast-enhanced MR angiography, and hyperpolarized gas functional lung imaging. This article also highlights recent developments in this field and areas of interest for future research including cardiac MRI-based diagnostic models, machine learning in cardiac MRI, oxygen-enhanced 1 H imaging, contrast-free 1 H perfusion and ventilation imaging, contrast-free angiography and UTE imaging. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 3.
Keyphrases
- contrast enhanced
- magnetic resonance imaging
- diffusion weighted
- computed tomography
- pulmonary hypertension
- magnetic resonance
- high resolution
- diffusion weighted imaging
- left ventricular
- machine learning
- systematic review
- optical coherence tomography
- dual energy
- pulmonary artery
- mass spectrometry
- intensive care unit
- heart failure
- risk assessment
- pulmonary arterial hypertension
- climate change
- big data