A novel rule-based algorithm for assigning myocardial fiber orientation to computational heart models.

Area of Science:

• Computational Biology
• Cardiac Electrophysiology
• Medical Imaging

Background:

• Accurate ventricular myocyte fiber orientation is crucial for computational models of cardiac electrical activity and mechanical contraction.
• Current methods, primarily Diffusion Tensor Imaging (DTI), can be limited by noisy or absent data.
• A need exists for alternative, reliable methods to define fiber orientation in cardiac models.

Purpose of the Study:

• To introduce and validate a novel Laplace-Dirichlet Rule-Based (LDRB) algorithm for assigning fiber orientation in computational heart models.
• To assess the performance and usability of the LDRB algorithm as an alternative to DTI-derived fiber orientation.

Main Methods:

• Development of the Laplace-Dirichlet Rule-Based (LDRB) algorithm for automated fiber orientation assignment.
• Application of the LDRB algorithm to an image-based computational model of canine ventricles.
• Comparison of electrical activation patterns simulated using LDRB-derived versus DTI-derived fiber orientations.

Main Results:

• Simulations using LDRB-derived fiber orientation produced activation patterns nearly indistinguishable from those using DTI-derived orientation.
• Key metrics showed minimal differences: relative differences ≤6%, absolute mean activation time differences ≤3.15 ms, and correlations ≥0.99.
• The LDRB algorithm demonstrated speed, precision, and high usability in assigning fiber orientation.

Conclusions:

• The LDRB algorithm is a robust and effective alternative to DTI for defining fiber orientation in computational cardiac models.
• This method enhances the reliability and accessibility of creating accurate cardiac simulations, particularly when DTI data is compromised.
• The LDRB algorithm facilitates improved predictive power of computational models for cardiac function.