The novel model function-based fitting method successfully identified personalized passive material parameters and unloaded reference configurations for all 19 patient cases, achieving excellent agreement with the empirical Klotz end-diastolic pressure-volume relation.
A novel, robust fixed-point algorithm enables automated identification of patient-specific myocardial passive material parameters and unloaded reference configurations from single-time-point clinical image data.
Image-based computational models of the heart represent a powerful tool to shed new light on the mechanisms underlying physiological and pathological conditions in cardiac function and to improve diagnosis and therapy planning. However, in order to enable the clinical translation of such models, it is crucial to develop personalized models that are able to reproduce the physiological reality of a given patient. There have been numerous contributions in experimental and computational biomechanics to characterize the passive behavior of the myocardium. However, most of these studies suffer from severe limitations and are not applicable to high-resolution geometries. In this work, we present a novel methodology to perform an automated identification of in vivo properties of passive cardiac biomechanics. The highly-efficient algorithm fits material parameters against the shape of a patient-specific approximation of the end-diastolic pressure-volume relation (EDPVR). Simultaneously, an unloaded reference configuration is generated, where a novel line search strategy to improve convergence and robustness is implemented. Only clinical image data or previously generated meshes at one time point during diastole and one measured data point of the EDPVR are required as an input. The proposed method can be straightforwardly coupled to existing finite element (FE) software packages and is applicable to different constitutive laws and FE formulations. Sensitivity analysis demonstrates that the algorithm is robust with respect to initial input parameters.
Marx et al. (Sat,) conducted a other in Aortic valve stenosis and aortic coarctation (n=19). Model function-based fitting method (MFF) was evaluated on Convergence and goodness of fit to the empirical Klotz end-diastolic pressure-volume relation. The novel model function-based fitting method successfully identified personalized passive material parameters and unloaded reference configurations for all 19 patient cases, achieving excellent agreement with the empirical Klotz end-diastolic pressure-volume relation.