Detection of arterial wall abnormalities via Bayesian model selection
Patient-specific modelling of haemodynamics in arterial networks has so far relied on parameter estimation for inexpensive or small-scale models. We describe here a Bayesian uncertainty quantification framework which makes two major advances: an efficient parallel implementation, allowing parameter...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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The Royal Society
2019-10-01
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| Series: | Royal Society Open Science |
| Subjects: | |
| Online Access: | https://royalsocietypublishing.org/doi/pdf/10.1098/rsos.182229 |
| _version_ | 1828468536879611904 |
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| author | Karen Larson Clark Bowman Costas Papadimitriou Petros Koumoutsakos Anastasios Matzavinos |
| author_facet | Karen Larson Clark Bowman Costas Papadimitriou Petros Koumoutsakos Anastasios Matzavinos |
| author_sort | Karen Larson |
| collection | DOAJ |
| description | Patient-specific modelling of haemodynamics in arterial networks has so far relied on parameter estimation for inexpensive or small-scale models. We describe here a Bayesian uncertainty quantification framework which makes two major advances: an efficient parallel implementation, allowing parameter estimation for more complex forward models, and a system for practical model selection, allowing evidence-based comparison between distinct physical models. We demonstrate the proposed methodology by generating simulated noisy flow velocity data from a branching arterial tree model in which a structural defect is introduced at an unknown location; our approach is shown to accurately locate the abnormality and estimate its physical properties even in the presence of significant observational and systemic error. As the method readily admits real data, it shows great potential in patient-specific parameter fitting for haemodynamical flow models. |
| first_indexed | 2024-12-11T04:25:07Z |
| format | Article |
| id | doaj.art-ecd70939e52e44f1887ae9738a7945ae |
| institution | Directory Open Access Journal |
| issn | 2054-5703 |
| language | English |
| last_indexed | 2024-12-11T04:25:07Z |
| publishDate | 2019-10-01 |
| publisher | The Royal Society |
| record_format | Article |
| series | Royal Society Open Science |
| spelling | doaj.art-ecd70939e52e44f1887ae9738a7945ae2022-12-22T01:21:00ZengThe Royal SocietyRoyal Society Open Science2054-57032019-10-0161010.1098/rsos.182229182229Detection of arterial wall abnormalities via Bayesian model selectionKaren LarsonClark BowmanCostas PapadimitriouPetros KoumoutsakosAnastasios MatzavinosPatient-specific modelling of haemodynamics in arterial networks has so far relied on parameter estimation for inexpensive or small-scale models. We describe here a Bayesian uncertainty quantification framework which makes two major advances: an efficient parallel implementation, allowing parameter estimation for more complex forward models, and a system for practical model selection, allowing evidence-based comparison between distinct physical models. We demonstrate the proposed methodology by generating simulated noisy flow velocity data from a branching arterial tree model in which a structural defect is introduced at an unknown location; our approach is shown to accurately locate the abnormality and estimate its physical properties even in the presence of significant observational and systemic error. As the method readily admits real data, it shows great potential in patient-specific parameter fitting for haemodynamical flow models.https://royalsocietypublishing.org/doi/pdf/10.1098/rsos.182229uncertainty quantificationtransitional markov chain monte carloinverse problemone-dimensional blood flowmodel selection |
| spellingShingle | Karen Larson Clark Bowman Costas Papadimitriou Petros Koumoutsakos Anastasios Matzavinos Detection of arterial wall abnormalities via Bayesian model selection Royal Society Open Science uncertainty quantification transitional markov chain monte carlo inverse problem one-dimensional blood flow model selection |
| title | Detection of arterial wall abnormalities via Bayesian model selection |
| title_full | Detection of arterial wall abnormalities via Bayesian model selection |
| title_fullStr | Detection of arterial wall abnormalities via Bayesian model selection |
| title_full_unstemmed | Detection of arterial wall abnormalities via Bayesian model selection |
| title_short | Detection of arterial wall abnormalities via Bayesian model selection |
| title_sort | detection of arterial wall abnormalities via bayesian model selection |
| topic | uncertainty quantification transitional markov chain monte carlo inverse problem one-dimensional blood flow model selection |
| url | https://royalsocietypublishing.org/doi/pdf/10.1098/rsos.182229 |
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