Learning Patient-Specific Lumped Models for Interactive Coronary Blood Flow Simulations.

We propose a parametric lumped model (LM) for fast patient-specific computational fluid dynamic simulations of blood flowin elongated vessel networks to alleviate the computational burden of 3D finite element (FE) simulations. We learn the coefficients balancing the local nonlinear hydraulic effects from a training set of precomputed FE simulations. Our LM yields pressure predictions accurate up to 2.76mmHg on 35 coronary trees obtained from 32 coronary computed tomography angiograms. We also observe a very good predictive performance on a validation set of 59 physiologicalmeasurements suggesting thatFEsimulations can be replaced by our LM. As LM predictions can be computed extremely fast, our approach paves the way to use a personalised interactive biophysical model with realtime feedback in clinical practice.