Advances in population-based imaging using cardiac magnetic resonance. (11th September 2019)

• Record Type: Journal Article
• Authors: Beyer, Sebastian E; Petersen, Steffen E
• Is Part Of: Progress in biomedical engineering. Volume 1:Number 1(2019)
• Publication Date: 2019-09-11
• Subjects: CMR -- cardiac magnetic resonance -- cohort studies -- population-based studies; Biomedical engineering -- Periodicals; 610.28
• DOI: 10.1088/2516-1091/ab3369
• Languages: English
• ISSNs: 2516-1091

Abstract

Abstract: Large population-based studies have helped to identify cardiovascular risk factors and to understand the natural progression of diseases. Cardiac magnetic resonance (CMR) is the reference method for the assessment of ventricular morphology and function given the low variance between scans. In addition, advanced sequences such as MR tagging, T1 mapping, and late gadolinium enhancement allow to assess regional ventricular function and fibrotic changes. MESA was the first study to use CMR on a large scale in a sample of the general population. Subsequent studies focused on cohorts of particular ethnicities or from certain locations with the Jackson Heart Study looking at African-Americans and the Dallas Heart Study at Dallas County Residents. More recently, the German National Cohort and UK Biobank have started to perform CMRs in a significantly larger number of participants (30 000 and 100 000, respectively). The introduction of CMR into prospective cohort studies has allowed to characterize ventricular remodeling in individuals of different age, sex, and gender and has found associations with new environmental exposures. The ability to detect subclinical changes in asymptomatic individuals has also been highlighted by reports of a high number of missed myocardial infarctions (MI) using CMR. In this review, we discuss the use of CMR in the different large population-based studies and compare the various associations found with left and right ventricular structure and function. In addition, we outline automated image analysis strategies aimed at overcoming challenges posed by the large amount of data in population-based studies.