Relation between branching patterns and perfusion in stochastic generated coronary arterial trees.

Area of Science:

• * Physiology and Anatomy
• * Biomedical Engineering
• * Computational Biology

Background:

• * Biological variation in branching patterns can affect tissue perfusion.
• * Understanding these variations is crucial for comprehending physiological processes.
• * Coronary tree anatomy plays a vital role in cardiac function.

Purpose of the Study:

• * To assess the fundamental consequences of branching characteristics on coronary trees.
• * To investigate the impact of area growth and symmetry variations on perfusion.
• * To compare stochastic asymmetrical trees with a non-stochastic symmetrical model.

Main Methods:

• * Generation of 50 stochastic asymmetrical coronary trees and one non-stochastic symmetrical tree.
• * Variation of area growth (A) and symmetry (S) parameters in stochastic models.
• * Analysis of flow, volume, vessel number, and pressure drop in generated trees.

Main Results:

• * Random S and A values led to significant variations in flow and volume, linearly related to vessel count.
• * Larger A values increased vessel number, flow, and volume, suggesting parallel vessel arrangements.
• * Lowering S values increased vessel number without altering flow, indicating series vessel dominance.
• * Both large A and small S values produced more realistic gradual pressure drops.

Conclusions:

• * Branching rules significantly influence coronary tree architecture and tissue perfusion.
• * Stochastic variations in branching patterns reflect real biological variability.
• * Understanding these branching rules is essential for predicting tissue perfusion outcomes.