Computational Framework for Parametric Tissue Modeling

Le résumé fourni par la source

Cardiovascular diseases are the leading cause of death worldwide, with coronary heart disease (CHD) being the most prevalent form. Advances in computational frameworks and digital twin technology have redefined vascular modeling, providing new opportunities for understanding disease progression and improving personalized treatment strategies. This study presents a high-performance computational framework for automated 3D vascular modeling and quantitative analysis, addressing key limitations of existing methods, such as reproducibility, scalability, and computational efficiency. The framework integrates parametric computer-aided diagnosis (CAD) modeling with automated data processing to streamline 3D model generation from imaging data, ensuring rapid and reproducible results at scale. It automates key processes such as surface reconstruction, metric extraction, and geometry optimization, minimizing manual intervention and enabling high-throughput analysis. Validation confirmed the accuracy and consistency of the reconstructed geometries, with minimal deviations in key measurements. The framework's modular design and support for standardized data formats allow seamless integration with computational workflows. By improving scalability and reducing computational demands, the proposed system supports population-scale studies, predictive modeling, and in silico clinical trials, offering a robust tool for advancing precision medicine and computational cardiovascular research.

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