Automated generation of 0D and 1D reduced-order models of patient-specific blood flow

Three-dimensional (3D) cardiovascular fluid dynamics simulations typically require hours to days of computing time on a high-performance computer. One-dimensional (1D) and lumped-parameter zero-dimensional (0D) models show great promise for accurately predicting blood flow and pressure with only a fraction of the cost. They can accelerate uncertainty quantification, optimization, and design parameterization studies. Previously, these models needed to be created laboriously by hand, and this limited assessment of their approximation accuracy to very few models in prior studies. This work proposes a fully automated and openly available framework to generate and simulate 0D and 1D models from 3D patient-specific geometries. Our only input is the 3D geometry; we do not use any prior knowledge from 3D simulations. All computational tools presented in this work are implemented in the open-source software platform SimVascular. We demonstrate the reduced-order approximation quality against full 3D solutions in a comprehensive comparison with N=73 publicly available models from various anatomies, vessel types, and disease conditions. Relative average approximation errors of flows and pressures typically ranged from 1% to 10% for both 0D and 1D models, at the caps and inside the vessel branches. Though they have minimally higher approximation errors than 1D, we recommend using 0D models due to their robustness and computational efficiency. Automatically generated reduced-order models can significantly speed up model development and shift the computational load from high-performance to personal computers.