A 3D personalized cardiac myocyte aggregate orientation model using MRI data-driven low-rank basis functions.

Area of Science:

• Biomedical Engineering
• Cardiac Imaging
• Computational Biology

Background:

• Cardiac myocyte orientation significantly influences cardiac electrophysiology and mechanics.
• Accurate microstructural representation is crucial for understanding cardiac function and simulating cardiac cycles.
• Existing models often lack the flexibility for personalized computational approaches.

Purpose of the Study:

• To develop a data-driven, personalized parametric model for cardiac myocyte orientation.
• To exploit structural similarities in cardiac microstructure for efficient representation.
• To compare dimensionality reduction techniques for modeling myocyte orientation.

Main Methods:

• Utilized high-resolution ex-vivo cardiac diffusion tensor imaging (cDTI) in porcine hearts.
• Applied Proper Generalized Decomposition with Singular Value Decomposition and Proper Orthogonal Decomposition for data reduction.
• Extracted a low-rank representation of predominant myocyte orientation in the left ventricle.

Main Results:

• Demonstrated a general set of basis functions for aggregated myocyte orientation.
• Developed a flexible, data-driven parametric model superior to atlas-based methods.
• Showcased the feasibility of transferring learned basis functions to human hearts.

Conclusions:

• The proposed parametric model enhances the accuracy of personalized computational cardiac models.
• This approach allows for more detailed microstructural representation tailored to patient data.
• The method shows promise for improving in-silico studies of human cardiac function.