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A flexible framework for sequential estimation of model parameters in computational hemodynamics
A major challenge in constructing three dimensional patient specific hemodynamic models is the calibration of model parameters to match patient data on flow, pressure, wall motion, etc. acquired in the clinic. Current workflows are manual and time-consuming. This work presents a flexible computation...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7717067/ https://www.ncbi.nlm.nih.gov/pubmed/33282681 http://dx.doi.org/10.1186/s40323-020-00186-x |
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author | Arthurs, Christopher J. Xiao, Nan Moireau, Philippe Schaeffter, Tobias Figueroa, C. Alberto |
author_facet | Arthurs, Christopher J. Xiao, Nan Moireau, Philippe Schaeffter, Tobias Figueroa, C. Alberto |
author_sort | Arthurs, Christopher J. |
collection | PubMed |
description | A major challenge in constructing three dimensional patient specific hemodynamic models is the calibration of model parameters to match patient data on flow, pressure, wall motion, etc. acquired in the clinic. Current workflows are manual and time-consuming. This work presents a flexible computational framework for model parameter estimation in cardiovascular flows that relies on the following fundamental contributions. (i) A Reduced-Order Unscented Kalman Filter (ROUKF) model for data assimilation for wall material and simple lumped parameter network (LPN) boundary condition model parameters. (ii) A constrained least squares augmentation (ROUKF-CLS) for more complex LPNs. (iii) A “Netlist” implementation, supporting easy filtering of parameters in such complex LPNs. The ROUKF algorithm is demonstrated using non-invasive patient-specific data on anatomy, flow and pressure from a healthy volunteer. The ROUKF-CLS algorithm is demonstrated using synthetic data on a coronary LPN. The methods described in this paper have been implemented as part of the CRIMSON hemodynamics software package. |
format | Online Article Text |
id | pubmed-7717067 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-77170672020-12-04 A flexible framework for sequential estimation of model parameters in computational hemodynamics Arthurs, Christopher J. Xiao, Nan Moireau, Philippe Schaeffter, Tobias Figueroa, C. Alberto Adv Model Simul Eng Sci Research Article A major challenge in constructing three dimensional patient specific hemodynamic models is the calibration of model parameters to match patient data on flow, pressure, wall motion, etc. acquired in the clinic. Current workflows are manual and time-consuming. This work presents a flexible computational framework for model parameter estimation in cardiovascular flows that relies on the following fundamental contributions. (i) A Reduced-Order Unscented Kalman Filter (ROUKF) model for data assimilation for wall material and simple lumped parameter network (LPN) boundary condition model parameters. (ii) A constrained least squares augmentation (ROUKF-CLS) for more complex LPNs. (iii) A “Netlist” implementation, supporting easy filtering of parameters in such complex LPNs. The ROUKF algorithm is demonstrated using non-invasive patient-specific data on anatomy, flow and pressure from a healthy volunteer. The ROUKF-CLS algorithm is demonstrated using synthetic data on a coronary LPN. The methods described in this paper have been implemented as part of the CRIMSON hemodynamics software package. Springer International Publishing 2020-12-02 2020 /pmc/articles/PMC7717067/ /pubmed/33282681 http://dx.doi.org/10.1186/s40323-020-00186-x Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Research Article Arthurs, Christopher J. Xiao, Nan Moireau, Philippe Schaeffter, Tobias Figueroa, C. Alberto A flexible framework for sequential estimation of model parameters in computational hemodynamics |
title | A flexible framework for sequential estimation of model parameters in computational hemodynamics |
title_full | A flexible framework for sequential estimation of model parameters in computational hemodynamics |
title_fullStr | A flexible framework for sequential estimation of model parameters in computational hemodynamics |
title_full_unstemmed | A flexible framework for sequential estimation of model parameters in computational hemodynamics |
title_short | A flexible framework for sequential estimation of model parameters in computational hemodynamics |
title_sort | flexible framework for sequential estimation of model parameters in computational hemodynamics |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7717067/ https://www.ncbi.nlm.nih.gov/pubmed/33282681 http://dx.doi.org/10.1186/s40323-020-00186-x |
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