This research will be conducted primarily in South Africa at the University of Cape Town (UCT) and Groote Schuur Hospital (GSH) in Cape Town, South Africa, in collaboration with Drs Mayosi, Meintjes, and Smedema, as an extension of NIH grant #1 RO1 EB 001763. The overall aim is to apply cine-DENSE (Displacement Encoding with Stimulated Echos) MR)to the study of wall motion abnormalities that occur in left-and right ventricular forms of cardiomyopathy that are particularly prevalent in sub-Saharan Africa and to develop highly automated algorithms for the quantitative analysis of such images. The cardiomyopathies pose the greatest challenge of all the cardiovascular diseases in Africa because of their greater prevalence in societies still plagued by diseases of famine and pestilence; difficulty in diagnosis due to the lack of specialized cardiological investigations in resource poor environments;the lack of accessto effective interventions such as heart transplantation;and the high mortality associated with these often irreversible disorders of heart muscle. Wall motion abnormality in cardiomyopathy may be the earliest functional abnormality before clinically detectable heart failure and may provide direct information about the contractile function of the heart. Previous wall motion imaging techniques suffer from either low spatial resolution or poor accuracy. Cine-DENSE achieves tracking of the myocardial motion through the cardiac cycle at pixel-wise spatial resolution with high accuracy. Slice following has recently been implemented for cine-DENSE achieving accurate 3D left ventricular (LV) motion tracking. These methodologies have not been applied to the RV and automatic segmentation of the LV and RV myocardium has not been achieved. Cine-DENSE has not been applied to the study of wall motion abnormalities that occur in cardiomyopathy and offers higher spatial resolution than techniques used in previous studies. Specifically, wall motion abnormality will be correlated with outcome at 1year in patients with arrhythmogenic right ventricular cardiomyopathy (ARVC) and peripartum cardiomyopathy (PCM). Tools will be developed for 3D tracking of the myocardial motion of both the LV and RV by developing a more robust phase unwrapping algorithm; automatic myocardial segmentation will be achieved using a motion-based algorithm, and 3D intramyocardial strain tensors will be computed at a pixel resolution by combining Strain Encoded (SENC) and DENSE MRI.
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