Search for: All records

Increased passive myocardial stiffness is implicated in the pathophysiology of many cardiac diseases, and its in vivo estimation can improve management of heart disease. MRI-driven computational constitutive modeling has been used extensively to evaluate passive myocardial stiffness. This approach requires subject-specific data that is best acquired with different MRI sequences: conventional cine (e.g. bSSFP), tagged MRI (or DENSE), and cardiac diffusion tensor imaging. However, due to the lack of comprehensive datasets and the challenge of incorporating multi-phase and single-phase disparate MRI data, no studies have combined in vivo cine bSSFP, tagged MRI, and cardiac diffusion tensor imaging to estimate passive myocardial stiffness. The objective of this work was to develop a personalized in silico left ventricular model to evaluate passive myocardial stiffness by integrating subject-specific geometric data derived from cine bSSFP, regional kinematics extracted from tagged MRI, and myocardial microstructure measured using in vivo cardiac diffusion tensor imaging. To demonstrate the feasibility of using a complete subject-specific imaging dataset for passive myocardial stiffness estimation, we calibrated a bulk stiffness parameter of a transversely isotropic exponential constitutive relation to match the local kinematic field extracted from tagged MRI. This work establishes a pipeline for developing subject-specific biomechanical ventricular models to probe passive myocardial mechanical behavior, using comprehensive cardiac imaging data from multiple in vivo MRI sequences.