Predictive modeling of blood flow

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 [5].

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.

Quantification of blood flow in aorta and coronary arteries [1]

Relevant Publications

  1. 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.
  2. 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.
  3. 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.
  4. Sankaran, S. and Marsden, A.L., A stochastic collocation method for uncertainty quantification in cardiovascular simulations, Journal of Biomechanical Engineering, 133(3), 031001, 2011.
  5. 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.
  6. 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.