Cardiac MRI in arrhythmogenic right ventricular cardiomyopathy: Implementation of novel techniques

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is an inherited cardiomyopathy characterized by life-threatening ventricular arrhythmias as one of the first disease presentations. Although no cure exists for ARVC, early treatment with an ICD can be life-saving. Early diagnosis and adequate risk-stratification is the corner stone of improving the prognosis in these patients. New cardiac MRI (CMR) methods can play an important role in detecting early signs of disease expression. This thesis primarily focuses on the improvement of early diagnosis and risk stratification of ARVC patients and their relatives using three advanced CMR techniques: 1) Feature Tracking CMR (FT-CMR) for the quantification of wall motion abnormalities; 2) T1-mapping for the quantification of scar tissue; 3) Machine learning to automatize segmentations necessary to calculate function and volume on CMR. To learn more about early disease expression in ARVC, we assessed the association of CMR traits and rare and common genetic variants in the general population. Also, by using advanced techniques and combing this with CMR data we aimed to find potential drug targets for cardiomyopathies. Using three new CMR techniques we show hopeful results regarding early diagnostics and risk stratification. For example, wall motion in the subtricuspid region measured using FT-CMR can already be abnormal in asymptomatic relatives. Furthermore, we show that pathogenic variants associated with cardiomyopathies are not uncommon in the general population, however disease penetrance remains low. We also found common genetic variants associated with left and right ventricular function by performing a genome wide association study. Using drug target mendelian randomization we were able to uncover potential drug targets for cardiomyopathies. For clinical implementation of these results, it is important to assess the additional value of these new CMR techniques and the genetic variants associated with function and volume, in current diagnostic and prognostic risk models.