Automatically generated 1D and 0D reduced-order models approximated 3D blood flow and pressure with relative average errors typically ranging from 1% to 10%, requiring significantly less computational time.
Automatically generated 1D and 0D reduced-order models can accurately approximate 3D cardiovascular hemodynamics with 1-10% error while significantly reducing computational runtime.
Three-dimensional (3D) cardiovascular fluid dynamics simulations typically require hours to days of computing time on a high-performance computing cluster. One-dimensional (1D) and lumped-parameter zero-dimensional (0D) models show great promise for accurately predicting blood bulk flow and pressure waveforms with only a fraction of the cost. They can also accelerate uncertainty quantification, optimization, and design parameterization studies. Despite several prior studies generating 1D and 0D models and comparing them to 3D solutions, these were typically limited to either 1D or 0D and a singular category of vascular anatomies. This work proposes a fully automated and openly available framework to generate and simulate 1D and 0D models from 3D patient-specific geometries, automatically detecting vessel junctions and stenosis segments. 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 rigid-wall 3D solutions in a comprehensive comparison with N = 72 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 1D and 0D models, measured at the outlets of terminal vessel branches. In general, 0D model errors were only slightly higher than 1D model errors despite requiring only a third of the 1D runtime. Automatically generated ROMs can significantly speed up model development and shift the computational load from high-performance machines to personal computers.
Pfaller et al. (Mon,) conducted a other in Cardiovascular disease (various anatomies) (n=72). 1D and 0D reduced-order models (ROMs) vs. 3D computational fluid dynamics (CFD) models was evaluated on Relative average approximation errors of flows and pressures. Automatically generated 1D and 0D reduced-order models approximated 3D blood flow and pressure with relative average errors typically ranging from 1% to 10%, requiring significantly less computational time.
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