Mining data from hemodynamic simulations via Bayesian emulation
Background: Arterial geometry variability is inevitable both within and across individuals. To ensure realistic prediction of cardiovascular flows, there is a need for efficient numerical methods that can systematically account for geometric uncertainty. Methods and results: A statistical framework...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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BioMed Central Ltd
2010
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| Online Access: | http://hdl.handle.net/1721.1/58922 |
| _version_ | 1811085404995059712 |
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| author | Bressloff, Neil W Kolachalama, Vijaya Bhasker Nair, Prasanth B. |
| author2 | Harvard University--MIT Division of Health Sciences and Technology |
| author_facet | Harvard University--MIT Division of Health Sciences and Technology Bressloff, Neil W Kolachalama, Vijaya Bhasker Nair, Prasanth B. |
| author_sort | Bressloff, Neil W |
| collection | MIT |
| description | Background: Arterial geometry variability is inevitable both within and across individuals. To ensure realistic prediction of cardiovascular flows, there is a need for efficient numerical methods that can systematically account for geometric uncertainty. Methods and results: A statistical framework based on Bayesian Gaussian process modeling was proposed for mining data generated from computer simulations. The proposed approach was applied to analyze the influence of geometric parameters on hemodynamics in the human carotid artery bifurcation. A parametric model in conjunction with a design of computer experiments strategy was used for generating a set of observational data that contains the maximum wall shear stress values for a range of probable arterial geometries. The dataset was mined via a Bayesian Gaussian process emulator to estimate: (a) the influence of key parameters on the output via sensitivity analysis, (b) uncertainty in output as a function of uncertainty in input, and (c) which settings of the input parameters result in maximum and minimum values of the output. Finally, potential diagnostic indicators were proposed that can be used to aid the assessment of stroke risk for a given patient's geometry. |
| first_indexed | 2024-09-23T13:09:03Z |
| format | Article |
| id | mit-1721.1/58922 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T13:09:03Z |
| publishDate | 2010 |
| publisher | BioMed Central Ltd |
| record_format | dspace |
| spelling | mit-1721.1/589222022-10-01T13:19:09Z Mining data from hemodynamic simulations via Bayesian emulation Bressloff, Neil W Kolachalama, Vijaya Bhasker Nair, Prasanth B. Harvard University--MIT Division of Health Sciences and Technology Kolachalama, Vijaya Bhasker Background: Arterial geometry variability is inevitable both within and across individuals. To ensure realistic prediction of cardiovascular flows, there is a need for efficient numerical methods that can systematically account for geometric uncertainty. Methods and results: A statistical framework based on Bayesian Gaussian process modeling was proposed for mining data generated from computer simulations. The proposed approach was applied to analyze the influence of geometric parameters on hemodynamics in the human carotid artery bifurcation. A parametric model in conjunction with a design of computer experiments strategy was used for generating a set of observational data that contains the maximum wall shear stress values for a range of probable arterial geometries. The dataset was mined via a Bayesian Gaussian process emulator to estimate: (a) the influence of key parameters on the output via sensitivity analysis, (b) uncertainty in output as a function of uncertainty in input, and (c) which settings of the input parameters result in maximum and minimum values of the output. Finally, potential diagnostic indicators were proposed that can be used to aid the assessment of stroke risk for a given patient's geometry. University of Southampton. School of Engineering Sciences 2010-10-06T20:18:17Z 2010-10-06T20:18:17Z 2007-12 2007-06 2010-09-03T16:14:15Z Article http://purl.org/eprint/type/JournalArticle 1475-925X http://hdl.handle.net/1721.1/58922 BioMedical Engineering OnLine. 2007 Dec 13;6(1):47 en http://dx.doi.org/10.1186/1475-925X-6-47 BioMedical Engineering Online Creative Commons Attribution http://creativecommons.org/licenses/by/2.0 Kolachalama et al.; licensee BioMed Central Ltd. application/pdf BioMed Central Ltd BioMed Central Ltd |
| spellingShingle | Bressloff, Neil W Kolachalama, Vijaya Bhasker Nair, Prasanth B. Mining data from hemodynamic simulations via Bayesian emulation |
| title | Mining data from hemodynamic simulations via Bayesian emulation |
| title_full | Mining data from hemodynamic simulations via Bayesian emulation |
| title_fullStr | Mining data from hemodynamic simulations via Bayesian emulation |
| title_full_unstemmed | Mining data from hemodynamic simulations via Bayesian emulation |
| title_short | Mining data from hemodynamic simulations via Bayesian emulation |
| title_sort | mining data from hemodynamic simulations via bayesian emulation |
| url | http://hdl.handle.net/1721.1/58922 |
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