Blood Flow Prediction Using UNET and VNET Architectures
Disciplines
Artificial Intelligence and Robotics | Computer Sciences
Abstract (300 words maximum)
Coronary artery disease (CAD) is one of the leading causes of death worldwide, making the assessment of blood flow and pressure distribution within coronary vessels essential for diagnosis and treatment planning. Fractional Flow Reserve (FFR) is a key measure used to determine the severity of arterial blockages, but traditional methods, such as invasive measurements or computational fluid dynamics (CFD) simulations, are not only time-consuming and costly, but also invasive. This project explores the use of deep learning to predict blood pressure distribution in coronary arteries using a 3D convolutional neural network. The dataset consists of Coronary Computed Tomography Angiography (CCTA) scans paired with blood pressure obtained from CFD simulations. After preprocessing and voxelizing the CCTA scans, the VNet model learns to map the vessel’s geometry to its internal pressure distribution. Our model achieved a Pearson correlation of 0.93 and an R² score of 0.84 between the predicted and simulated pressures. These results indicate that VNet delivers accurate, spatially consistent pressure predictions that closely match CFD outputs while substantially reducing computation time. This approach underscores the potential of deep learning to accelerate non-invasive FFR estimation and enhance patient-specific cardiovascular analysis
Use of AI Disclaimer
no
Academic department under which the project should be listed
CCSE – Computer Science
Primary Investigator (PI) Name
Chen Zhao
Blood Flow Prediction Using UNET and VNET Architectures
Coronary artery disease (CAD) is one of the leading causes of death worldwide, making the assessment of blood flow and pressure distribution within coronary vessels essential for diagnosis and treatment planning. Fractional Flow Reserve (FFR) is a key measure used to determine the severity of arterial blockages, but traditional methods, such as invasive measurements or computational fluid dynamics (CFD) simulations, are not only time-consuming and costly, but also invasive. This project explores the use of deep learning to predict blood pressure distribution in coronary arteries using a 3D convolutional neural network. The dataset consists of Coronary Computed Tomography Angiography (CCTA) scans paired with blood pressure obtained from CFD simulations. After preprocessing and voxelizing the CCTA scans, the VNet model learns to map the vessel’s geometry to its internal pressure distribution. Our model achieved a Pearson correlation of 0.93 and an R² score of 0.84 between the predicted and simulated pressures. These results indicate that VNet delivers accurate, spatially consistent pressure predictions that closely match CFD outputs while substantially reducing computation time. This approach underscores the potential of deep learning to accelerate non-invasive FFR estimation and enhance patient-specific cardiovascular analysis