  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, a stress-free reference configuration is generated,
where a novel fail-safe feature 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.
