Why the study?
Creating patient-specific models of the heart is promising for predicting outcomes and customizing therapies, but integrating multimodal imaging data to construct them is a nontrivial task.
An open-source method using prolate spheroidal coordinates enables the integration of multimodal cardiac MRI data into personalized 3D left ventricular models.
Enables unified multimodal mapping for patient-specific heart models; leaves open prospective validation before clinical use.
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 multimodal imaging data to construct patient-specific models is a nontrivial 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 (LV). We demonstrate the mapping of the location and transmural extent of postinfarction scar segmented from late gadolinium enhancement (LGE) magnetic resonance imaging (MRI), as well as mechanical activation calculated from displacement encoding with stimulated echoes (DENSE) MRI. As a supplement to this paper, we provide MATLAB and Python versions of the routines employed here for download from SimTK.
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Phung et al. (2019) studied this question.
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