Multimodality imaging for myocardial injury in acute myocardial infarction and the assessment of valvular heart disease

Abstract

In this PhD thesis we have studied left ventricular systolic deformation with novel cardiovascular magnetic resonance (CMR) algorithm called feature-tracking in patients with acute myocardial infarction (MI). We have shown that left ventricular strain with feature-tracking CMR offers an incremental value above conventional imaging parameters in the assessment of prognosis of patients with acute MI. In addition, left ventricular strain analysis confirmed a clear clinical benefit of early intravenous betablocker treatment in patients with acute MI. Moreover, we have explored the feasibility of novel automated 3D echocardiographic algorithm in the assessment of the aortic root anatomy in patients with severe aortic stenosis prior to transcatheter aortic valve replacement (TAVR). The 3D echocardiographic algorithm performed excellent against the gold standard multidetector row computed tomography to determine the aortic annulus size and to choose the correct TAVR prosthesis size; however, the accuracy was a bit lower in patients with severely calcified aortic valves. In addition, we have explored the advantages and limitations of different cardiac imaging techniques in the assessment of patients with TAVR, the role of multimodality imaging in patients with combined valvular heart disease and heart failure, and the role of myocardial fibrosis assessment with CMR in valvular heart disease.