From Medical Imaging to Hemodynamics: AI-Enabled Modeling of Cardiovascular Flow
Abstract
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…
Read the paper
Related studies
- Learning from First-Generation Qualitative Approaches in the IS Discipline: An Evolutionary View and Some Implications for Authors and Evaluators (PART 1/2)
- Commentary on the clinical and preclinical dosage limits of interstitially administered magnetic fluids for therapeutic hyperthermia based on current practice and efficacy models
- Prosthesis: New Technological Opportunities and Innovative Biomedical Devices
- The history and future of neural modeling for cochlear implants
- Ryanodine receptors: waking up from refractoriness
- Novel Transgenic Rabbit Model Sheds Light on the Puzzling Role of Matrix Metalloproteinase-12 in Atherosclerosis
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.