Improved identifiability of myocardial material parameters by an energy-based cost function

Area of Science:

• Biomedical Engineering
• Cardiovascular Mechanics
• Computational Biology

Background:

• Myocardial stiffness is a key biomarker for heart failure (HF) monitoring.
• Cardiac finite element models assess stiffness via myocardial constitutive model parameters.
• Parameter intercorrelations in current models hinder unique material parameter estimation and clinical translation.

Purpose of the Study:

• To investigate the role of cost functions (CFs) in parameter identifiability for cardiac finite element models.
• To evaluate geometric indices and a novel energy conservation-derived CF for estimating myocardial material parameters.
• To develop a new methodology for accurate myocardial material parameter estimation.

Main Methods:

• Investigated geometric indices (displacements, strains, volume, wall thickness, dimensions) and an energy-based CF.
• Utilized the Guccione et al. transversely isotropic material model.
• Validated the new methodology using in silico data and 8 clinical datasets (7 HF, 1 control).

Main Results:

• A single geometry-based CF was insufficient for unique parameter constraint.
• The energy-based CF, combined with a geometric metric, enabled unique estimation of material parameters.
• The developed pipeline demonstrated accuracy in silico and robustness in vivo.

Conclusions:

• A novel methodology combining deformation and energetics analysis accurately estimates myocardial material parameters.
• This approach overcomes limitations of existing methods, facilitating reliable clinical assessment of myocardial stiffness in heart failure.
• The identified parameters for the Guccione material law differed between HF cases and the healthy control.