mlsanityData to 14 Oct 2026

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

arXiv 2503.12515 ↗First posted 16 Mar 2025cs.CV cs.LG physics.med-phcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 31 of its 78 references that are in the map. First posted on arXiv 16 Mar 2025.

Builds on

31 of 78 references.

References · 31

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19 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025AortaDiff: Volume-Guided Conditional Diffusion Models for Multi-Branch Aortic Surface Generationcited by 1
  2. 2026Sketch2CT: Multimodal Diffusion for Structure-Aware 3D Medical Volume Generationcited by 0