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

2025-03-16

AI Summary

Current patient-specific vascular modeling heavily relies on manual intervention, resulting in time-consuming workflows, high error rates, and limited clinical deployability. To address these challenges, we propose a unified framework integrating voxel-based segmentation with unsupervised anatomically consistent surface deformation—enabling, for the first time, fully automatic generation of simulation-ready aortic models directly from medical images. Our method jointly optimizes, in an end-to-end manner, a deep learning segmentation network and a data-driven implicit surface deformation module, achieving both high geometric fidelity and substantial computational efficiency gains. Evaluated on public benchmarks, our approach achieves state-of-the-art performance in segmentation accuracy and mesh quality, reduces modeling time by over 80%, and eliminates nearly all manual intervention. This work delivers an efficient, robust, and generalizable automation solution for clinical computational fluid dynamics (CFD) simulations.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Deep Generative Models & Autoencoders
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Web Mining and Content Analysis: Web data generation and simulation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• Automates patient-specific vascular model creation from medical images
• Enhances geometric accuracy and computational efficiency in modeling
• Reduces manual effort and processing time for clinical applications

Innovation

• Deep-learning automates patient-specific vascular models.
• Integrates segmentation and surface deformation modules.
• Enhances accuracy and reduces manual processing time.