Computational Framework for Parametric Tissue Modeling.

Area of Science:

• Cardiovascular research
• Computational biology
• Medical imaging analysis

Background:

• Cardiovascular diseases, particularly coronary heart disease (CHD), are a leading global cause of mortality.
• Digital twin technology and advanced computational frameworks offer new avenues for vascular modeling and personalized treatment.
• Existing vascular modeling methods face limitations in reproducibility, scalability, and computational efficiency.

Purpose of the Study:

• To present a high-performance computational framework for automated 3D vascular modeling and quantitative analysis.
• To address limitations in reproducibility, scalability, and efficiency of current vascular modeling techniques.
• To facilitate high-throughput analysis and support precision medicine in cardiovascular research.

Main Methods:

• Integration of parametric computer-aided diagnosis (CAD) modeling with automated data processing for 3D model generation from imaging data.
• Automation of surface reconstruction, metric extraction, and geometry optimization to minimize manual intervention.
• Development of a modular framework supporting standardized data formats for seamless integration into computational workflows.

Main Results:

• The framework enables rapid and reproducible 3D vascular model generation at scale.
• Automated processes significantly reduce manual intervention, allowing for high-throughput analysis.
• Validation confirmed the accuracy and consistency of reconstructed geometries with minimal measurement deviations.

Conclusions:

• The proposed framework enhances scalability and reduces computational demands for vascular modeling.
• It supports population-scale studies, predictive modeling, and in silico clinical trials.
• This tool advances precision medicine and computational cardiovascular research by providing a robust solution for vascular analysis.