Multiparametric Cardiovascular Magnetic Resonance Approach in Diagnosing, Monitoring, and Prognostication of Myocarditis

Abstract

Myocarditis represents the entity of an inflamed myocardium and is a diagnostic challenge caused by its heterogeneous presentation. Contemporary noninvasive evaluation of patients with clinically suspected myocarditis using cardiac magnetic resonance (CMR) includes dimensions and function of the heart chambers, conventional T2-weighted imaging, late gadolinium enhancement, novel T1 and T2 mapping, and extracellular volume fraction calculation. CMR feature-tracking, texture analysis, and artificial intelligence emerge as potential modern techniques to further improve diagnosis and prognostication in this clinical setting. This review describes the evidence surrounding different CMR methods and image postprocessing methods and highlights their values for clinical decision making, monitoring, and risk stratification across stages of this condition.

Keywords: ECV; LGE; Lake Louise criteria (LLC); T1 mapping; T2 mapping; artificial intelligence; cardiac magnetic resonance (CMR); feature-tracking; myocardial strain; myocarditis; postprocessing; radiomics; texture analysis.