Velocity-based cardiac contractility personalization from images using derivative-free optimization.

Area of Science:

• Biophysics
• Medical Imaging
• Computational Biology

Background:

• Cardiac contractility personalization is crucial for clinical applications of biophysical models.
• Current gradient-based methods struggle to account for contraction and relaxation rates due to objective function limitations.
• Analytical gradient evaluation is difficult for complex cardiac models, and finite difference methods are computationally expensive.

Purpose of the Study:

• To develop a novel framework for cardiac contractility personalization that overcomes limitations of existing gradient-based methods.
• To simultaneously identify regional maximum contraction stresses, contraction rates, and relaxation rates.
• To enable accurate model personalization using derivative-free optimization with velocity-based objective functions.

Main Methods:

• Implemented a derivative-free optimization algorithm to bypass gradient computation limitations.
• Utilized a velocity-based objective function for enhanced parameter identification.
• Validated the framework using synthetic and clinical cardiac imaging data.

Main Results:

• The velocity-based objective function accurately identified maximum contraction stresses, contraction rates, and relaxation rates simultaneously.
• Derivative-free optimization demonstrated robustness and insensitivity to initial parameters.
• Personalized contractility parameters derived from clinical data aligned with patient-specific physiologies.

Conclusions:

• Derivative-free optimization with a velocity-based objective function offers a powerful approach for cardiac model personalization.
• This method enhances the accuracy of identifying key contractility parameters from medical images.
• The framework holds significant potential for improving clinical practice through patient-specific cardiac modeling.