From Medical Imaging to Hemodynamics: AI-Enabled Modeling of Cardiovascular Flow

Cardiovascular disease remains the leading cause of mortality in the United States, motivating the development of patient-specific tools for diagnosis, risk stratification, and treatment planning. Image-based computational fluid dynamics (CFD) has emerged as a powerful framework for characterizing cardiovascular hemodynamics by integrating medical imaging with physics-based simulation. This pipeline typically involves image acquisition, vascular segmentation, geometry generation, flow simulation, and post-processing, among which segmentation, geometry generation, and flow simulation are the most critical and technically challenging components.

Conventional segmentation workflows are often manual or semi-automatic, making them time-consuming, operator-dependent, and difficult to scale. To address these limitations, this thesis develops AI-enabled segmentation frameworks that automatically generate simulation-ready vascular geometries, substantially improving efficiency while reducing user effort and variability.

Beyond segmentation, the scarcity of high-quality patient-specific vascular geometries poses a major challenge for data-driven cardiovascular modeling, particularly for complex multibranch structures that are inadequately represented by traditional statistical shape models. This work introduces generative AI approaches for vascular geometry synthesis that combine hierarchical parameterizations with diffusion-based models, enabling the generation of anatomically consistent, CFD-ready geometries for data augmentation and population-level analysis.

To overcome the high computational cost of traditional CFD solvers, this thesis further develops deep learning-based surrogate models that rapidly map vascular geometries to flow fields, as well as probabilistic modeling frameworks to capture complex flow phenomena and quantify uncertainty. In parallel, a GPU-accelerated differentiable finite-volume solver based on graph neural network message passing is introduced to enable efficient simulation on unstructured meshes.

Together, this thesis presents a unified AI-enabled image-based CFD framework that advances the development of efficient, reliable, and clinically actionable cardiovascular digital twins.