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Open-Source Routines for Building Personalized Left Ventricular Models from Cardiac MRI Data (2019)  View

Creating patient-specific models of the heart is a promising approach for predicting outcomes in response to congenital malformations, injury, or disease, as well as an important tool for developing and customizing therapies. However, integrating multi-modal imaging data to construct patient-specific models is a non-trivial task. Here, we propose an approach that employs a prolate spheroidal coordinate system to interpolate information from multiple imaging datasets and map those data onto a single geometric model of the left ventricle. We demonstrate the mapping of the location and transmural extent of post-infarction scar segmented from late gadolinium enhancement (LGE) MRI, as well as mechanical activation calculated from displacement encoding with stimulated echoes (DENSE) MRI. As a supplement to this article, we provide MATLAB and Python versions of the routines employed here for download from SimTK (