Tissue-growth-based synthetic tree generation and perfusion simulation.

Area of Science:

• Biomedical Engineering
• Computational Biology
• Medical Imaging

Background:

• Biological tissues rely on intricate blood vessel networks for oxygen and nutrient supply, driven by a critical supply-demand relationship.
• Accurate modeling of these vascular networks is essential for understanding tissue perfusion and predicting pathologies like ischemia.
• Existing methods for vascular tree generation can be computationally intensive and may lack realism.

Purpose of the Study:

• To develop and validate a novel synthetic vascular tree generation algorithm.
• To enable efficient and realistic simulation of blood flow and perfusion in biological tissues.
• To provide a tool for quantifying tissue perfusion and identifying potential ischemic regions.

Main Methods:

• A synthetic tree generation algorithm was implemented, starting from segmented major arteries in medical image data.
• The algorithm generates extensive vessel networks to meet tissue metabolic demands, optimized for parallel execution.
• Multiscale blood flow simulations were performed using 1D blood flow and Darcy flow equations, coupled at terminal segments.

Main Results:

• The algorithm successfully generated realistic synthetic vascular trees with significantly reduced computational cost compared to existing methods.
• Simulations demonstrated the capability to model blood perfusion in idealized models and patient-specific geometries (brain and heart).
• The method proved effective in validating vascular tree generation and blood flow simulation.

Conclusions:

• The proposed method offers an efficient and accurate approach for synthetic vascular tree generation and blood flow simulation.
• This technique can be applied to patient-specific geometries for quantitative perfusion analysis and ischemia prediction.
• The developed algorithm holds potential for advancing diagnostic tools in various medical applications.