Blood flow is a key indicator for prognosis and treatment of disease in the cardiovascular system. Hemodynamic factors have emerged as a golden standard to decide between medical therapy and percutaneous coronary intervention (stenting). I have developed algorithms for simulating blood flow in the human cardiovascular system using patient-specific models derived from imaging scans. We use computational fluid dynamics on the patient-derived geometry and boundary conditions derived from a model of microcirculatory resistance to predict blood flow. Due to the complexity of patient-specific models, high performance computing is used to solve the millions of linear equations derived from the Navier-Stokes partial differential equations. Recently, we were able to demonstrate a good agreement between machine-learning based prediction of hemodynamics with full three-dimensional physics-based simulations .
Recently, I developed an algorithm for real-time prediction of blood flow with colleagues at HeartFlow that has been approved by the FDA and being used by physicians in the United States and outside.
- Sankaran, S., Moghadam, M.E., Kahn, A.M., Tseng, E.E., Guccione, G.M., and Marsden, A.L., Patient-specific multiscale modeling of blood flow for coronary artery bypass graft surgery, Annals of Biomedical Engineering, 40(10) pp. 2228-2242, 2012.
- Modi, B.N., Sankaran, S., et al., Predicting the Physiological Effect of Revascularization in Serially Diseased Coronary Arteries: Clinical Validation of a Novel CT Coronary Angiography Based Technique, Circulation: Cardiovascular Interventions, 12/2, 2019.
- Sankaran, S., Kim, H-J., Choi, G. and Taylor, C., Uncertainty quantification in coronary flow simulations: impact of geometry, boundary conditions and blood viscosity, Journal of Biomechanics, 49 (12), pp. 2540–2547, 2016.
- Sankaran, S. and Marsden, A.L., A stochastic collocation method for uncertainty quantification in cardiovascular simulations, Journal of Biomechanical Engineering, 133(3), 031001, 2011.
- Sankaran, S., Grady, L., and Taylor, C., Fast computation of hemodynamic sensitivity to lumen segmentation uncertainty, IEEE Transactions on Medical Imaging, 34 (12), pp. 2562-2571, 2015.
- Sankaran, S., Lesage, D., Tombropoulos, R., Xiao, N., Kim, H.J., Spain, D., Schaap, M. and Taylor, C.A., Physics driven reduced order model for real time blood flow simulations, arxiv preprint, 2019.