AI-Powered Automated Model Construction for Patient-Specific CFD Simulations of Aortic Flows

• URL: http://arxiv.org/abs/2503.12515v1
• Date: Sun, 16 Mar 2025 14:18:25 GMT
• Title: AI-Powered Automated Model Construction for Patient-Specific CFD Simulations of Aortic Flows
• Authors: Pan Du, Delin An, Chaoli Wang, Jian-Xun Wang,
• Abstract summary: This study introduces a deep-learning framework that automates the creation of simulation-ready vascular models from medical images.<n>The proposed approach demonstrates state-of-the-art performance in segmentation and mesh quality while significantly reducing manual effort and processing time.
• Score: 8.062885940500259
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Image-based modeling is essential for understanding cardiovascular hemodynamics and advancing the diagnosis and treatment of cardiovascular diseases. Constructing patient-specific vascular models remains labor-intensive, error-prone, and time-consuming, limiting their clinical applications. This study introduces a deep-learning framework that automates the creation of simulation-ready vascular models from medical images. The framework integrates a segmentation module for accurate voxel-based vessel delineation with a surface deformation module that performs anatomically consistent and unsupervised surface refinements guided by medical image data. By unifying voxel segmentation and surface deformation into a single cohesive pipeline, the framework addresses key limitations of existing methods, enhancing geometric accuracy and computational efficiency. Evaluated on publicly available datasets, the proposed approach demonstrates state-of-the-art performance in segmentation and mesh quality while significantly reducing manual effort and processing time. This work advances the scalability and reliability of image-based computational modeling, facilitating broader applications in clinical and research settings.

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