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

Effectively understanding and managing CVD requires advanced diagnostic tools capable of accurately characterizing complex hemodynamics within the cardiovascular system. While medical imaging modalities such as computed tomography (CT) and magnetic resonance imaging (MRI) provide high-resolution anatomical detail, they lack the capability to directly capture hemodynamics information (e.g., blood flow patterns, pressure, and wall shear stress fields) critical for understanding vascular function and pathology. To bridge this gap, image-based computational fluid dynamics (CFD) has emerged as a powerful computational paradigm that derives hemodynamic information from anatomical images via conservation laws. Although widely utilized in cardiovascular research, the clinical application of image-based CFD for diagnosis and surgical planning remains limited, largely due to the challenges associated with efficient and accurate model construction [2-4]. Constructing patient-specific vascular models for image-based CFD involves multiple steps, including image segmentation, geometry modeling, and mesh generation for the computational domain, all of which are critical to ensuring the fidelity of the final simulation results. However, the standard workflow heavily relies on manual methods, making it highly labor-intensive and time-consuming.