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 computing cluster. One‐dimensional (1D) and lumped‐parameter zero‐dimensional (0D) models show great promise for accurately predicting blood bulk flow and pressure...
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| Published in | International journal for numerical methods in biomedical engineering Vol. 38; no. 10; pp. e3639 - n/a |
|---|---|
| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Hoboken, USA
John Wiley & Sons, Inc
01.10.2022
Wiley Subscription Services, Inc |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2040-7939 2040-7947 2040-7947 |
| DOI | 10.1002/cnm.3639 |
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| 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 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.
We present an open‐source framework to automatically generate 1D and 0D reduced‐order fluid dynamics models from 3D geometries. We demonstrate the robustness of the framework and the quality of the 1D and 0D approximations in a comprehensive comparison of N = 72 subject‐specific models against 3D fluid dynamics. Despite their significantly reduced computational effort, 1D and 0D models show good agreement with 3D models even in severely stenosed cases. |
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| AbstractList | 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.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. 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. 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. 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. We present an open-source framework to automatically generate 1D and 0D reduced-order fluid dynamics models from 3D geometries. We demonstrate the robustness of the framework and the quality of the 1D and 0D approximations in a comprehensive comparison of N=72 subject-specific models against 3D fluid dynamics. Despite their significantly reduced computational effort, 1D and 0D models show good agreement with 3D models even in severely stenosed cases. 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. We present an open‐source framework to automatically generate 1D and 0D reduced‐order fluid dynamics models from 3D geometries. We demonstrate the robustness of the framework and the quality of the 1D and 0D approximations in a comprehensive comparison of N = 72 subject‐specific models against 3D fluid dynamics. Despite their significantly reduced computational effort, 1D and 0D models show good agreement with 3D models even in severely stenosed cases. |
| Author | Marsden, Alison L. Pham, Jonathan Verma, Aekaansh Wilson, Nathan M. Pfaller, Martin R. Parker, David W. Yang, Weiguang Pegolotti, Luca |
| AuthorAffiliation | 1 Pediatric Cardiology, Stanford University, CA, USA 6 Stanford Research Computing, Stanford University, CA, USA 5 Open Source Medical Software Corporation, CA, USA 2 Institute for Computational and Mathematical Engineering, Stanford University, CA, USA 3 Cardiovascular Institute, Stanford University, CA, USA 7 Bioengineering, Stanford University, CA, USA 4 Mechanical Engineering, Stanford University, CA, USA |
| AuthorAffiliation_xml | – name: 7 Bioengineering, Stanford University, CA, USA – name: 2 Institute for Computational and Mathematical Engineering, Stanford University, CA, USA – name: 6 Stanford Research Computing, Stanford University, CA, USA – name: 5 Open Source Medical Software Corporation, CA, USA – name: 1 Pediatric Cardiology, Stanford University, CA, USA – name: 3 Cardiovascular Institute, Stanford University, CA, USA – name: 4 Mechanical Engineering, Stanford University, CA, USA |
| Author_xml | – sequence: 1 givenname: Martin R. orcidid: 0000-0001-5760-2617 surname: Pfaller fullname: Pfaller, Martin R. email: pfaller@stanford.edu organization: Stanford University – sequence: 2 givenname: Jonathan orcidid: 0000-0002-5676-6561 surname: Pham fullname: Pham, Jonathan organization: Stanford University – sequence: 3 givenname: Aekaansh surname: Verma fullname: Verma, Aekaansh organization: Stanford University – sequence: 4 givenname: Luca orcidid: 0000-0002-4290-6391 surname: Pegolotti fullname: Pegolotti, Luca organization: Stanford University – sequence: 5 givenname: Nathan M. surname: Wilson fullname: Wilson, Nathan M. organization: Open Source Medical Software Corporation – sequence: 6 givenname: David W. surname: Parker fullname: Parker, David W. organization: Stanford University – sequence: 7 givenname: Weiguang orcidid: 0000-0003-3324-4346 surname: Yang fullname: Yang, Weiguang organization: Stanford University – sequence: 8 givenname: Alison L. orcidid: 0000-0003-1902-171X surname: Marsden fullname: Marsden, Alison L. organization: Stanford University |
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| Snippet | Three‐dimensional (3D) cardiovascular fluid dynamics simulations typically require hours to days of computing time on a high‐performance computing cluster.... Three-dimensional (3D) cardiovascular fluid dynamics simulations typically require hours to days of computing time on a high-performance computing cluster.... |
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| SubjectTerms | Approximation Automation Blood flow Blood pressure Blood vessels cardiovascular fluid dynamics Computer applications Computers Computing time Design optimization Design parameters Errors Fluid dynamics Hydrodynamics lumped‐parameter networks One dimensional models one‐dimensional blood flow Open source software Parameterization Personal computers reduced‐order models Simulation Stenosis Three dimensional flow Three dimensional models Waveforms zero‐dimensional blood flow |
| Title | Automated generation of 0D and 1D reduced‐order models of patient‐specific blood flow |
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