Automatic image-to-model framework for patient-specific electromechanical modeling of the heart

A key requirement for recent advances in computational modeling to be clinically applicable is the ability to fit models to patient data. Various personalization techniques have been proposed for isolated sub-components of complex models of heart physiology. However, no work has been presented that focuses on personalizing full electromechanical (EM) models in a streamlined, consistent and automatic fashion, which has been evaluated on a large population. We present an integrated system for full EM personalization from routinely acquired clinical data. The importance of mechanical parameters is analyzed in a comprehensive sensitivity study, revealing that myocyte contraction and Young's modulus are the main determinants of model output variation, what lead to the proposed personalization strategy. On a large, physiologically diverse set of 15 patients, we demonstrate the effectiveness of our framework by comparing measured and calculated parameters, yielding left ventricular ejection fraction and stroke volume errors of 6.6% and 9.2 mL, respectively.