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

Abstract

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 opensource 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.

Hemodynamics in Patients with Aortic Coarctation: A Comparison ofin vivo4D-Flow MRI and FSI Simulation

“…Anatomic Model Generation 3D geometries of patient-specific aortas were generated by importing the MRI magnitude images into SimVascular and manually segmenting the vessels. Centerlines were automatically extracted from the 3D geometry to create the 0D model comprising a network of lumped parameter elements (resistors, capacitors and inductors) [5]. The 3D volume was discretized using a mesh of tetrahedral elements.…”

Non-invasive estimation of pressure drop across aortic coarctations: validation of 0D and 3D computational models within vivomeasurements

“…To create the 0D models, the vessel's centerlines and cross-sectional areas were automatically extracted from the 3D models [23]. Vessel junctions (bifurcations) were then automatically identified and the centerline network was split into branches and junctions.…”

“…0D models are comprised of lumped-parameter elements such as resistors, capacitors, and inductors that connect to form an entire network. Previous work by Pfaller et al demonstrated the robustness of 0D models by comparing to 3D results in 72 patient-specific cardiovascular models [23].…”

Multiscale constitutive framework of 1D blood flow modeling: Asymptotic limits and numerical methods

“…Several studies have already shown that, in general, one-dimensional (1D) modeling coupled with lumped-parameter, zero-dimensional (0D) models, derived from full threedimensional (3D) models by means of simplifying assumptions about flow, structure, and their interaction, is sufficient to obtain realistic and accurate numerical results, particularly when the flow is predominantly unidirectional [46,33,44]. Moreover, in contrast to 3D simulations with prohibitively high computational costs, 1D models allow for the investigation of the hemodynamics of the entire main circulatory system [1,31,34].…”