Shape-driven deep neural networks for fast acquisition of aortic 3D pressure and velocity flow fields.
Computational fluid dynamics (CFD) can be used to simulate vascular haemodynamics and analyse potential treatment options. CFD has shown to be beneficial in improving patient outcomes. However, the implementation of CFD for routine clinical use is yet to be realised. Barriers for CFD include high co...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2023-04-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011055&type=printable |
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| author | Endrit Pajaziti Javier Montalt-Tordera Claudio Capelli Raphaël Sivera Emilie Sauvage Michael Quail Silvia Schievano Vivek Muthurangu |
| author_facet | Endrit Pajaziti Javier Montalt-Tordera Claudio Capelli Raphaël Sivera Emilie Sauvage Michael Quail Silvia Schievano Vivek Muthurangu |
| author_sort | Endrit Pajaziti |
| collection | DOAJ |
| description | Computational fluid dynamics (CFD) can be used to simulate vascular haemodynamics and analyse potential treatment options. CFD has shown to be beneficial in improving patient outcomes. However, the implementation of CFD for routine clinical use is yet to be realised. Barriers for CFD include high computational resources, specialist experience needed for designing simulation set-ups, and long processing times. The aim of this study was to explore the use of machine learning (ML) to replicate conventional aortic CFD with automatic and fast regression models. Data used to train/test the model consisted of 3,000 CFD simulations performed on synthetically generated 3D aortic shapes. These subjects were generated from a statistical shape model (SSM) built on real patient-specific aortas (N = 67). Inference performed on 200 test shapes resulted in average errors of 6.01% ±3.12 SD and 3.99% ±0.93 SD for pressure and velocity, respectively. Our ML-based models performed CFD in ∼0.075 seconds (4,000x faster than the solver). This proof-of-concept study shows that results from conventional vascular CFD can be reproduced using ML at a much faster rate, in an automatic process, and with reasonable accuracy. |
| format | Article |
| id | doaj-art-88220f5e6c8f43c681a9f8ed3313e324 |
| institution | Kabale University |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2023-04-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-88220f5e6c8f43c681a9f8ed3313e3242025-08-20T03:25:16ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582023-04-01194e101105510.1371/journal.pcbi.1011055Shape-driven deep neural networks for fast acquisition of aortic 3D pressure and velocity flow fields.Endrit PajazitiJavier Montalt-TorderaClaudio CapelliRaphaël SiveraEmilie SauvageMichael QuailSilvia SchievanoVivek MuthuranguComputational fluid dynamics (CFD) can be used to simulate vascular haemodynamics and analyse potential treatment options. CFD has shown to be beneficial in improving patient outcomes. However, the implementation of CFD for routine clinical use is yet to be realised. Barriers for CFD include high computational resources, specialist experience needed for designing simulation set-ups, and long processing times. The aim of this study was to explore the use of machine learning (ML) to replicate conventional aortic CFD with automatic and fast regression models. Data used to train/test the model consisted of 3,000 CFD simulations performed on synthetically generated 3D aortic shapes. These subjects were generated from a statistical shape model (SSM) built on real patient-specific aortas (N = 67). Inference performed on 200 test shapes resulted in average errors of 6.01% ±3.12 SD and 3.99% ±0.93 SD for pressure and velocity, respectively. Our ML-based models performed CFD in ∼0.075 seconds (4,000x faster than the solver). This proof-of-concept study shows that results from conventional vascular CFD can be reproduced using ML at a much faster rate, in an automatic process, and with reasonable accuracy.https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011055&type=printable |
| spellingShingle | Endrit Pajaziti Javier Montalt-Tordera Claudio Capelli Raphaël Sivera Emilie Sauvage Michael Quail Silvia Schievano Vivek Muthurangu Shape-driven deep neural networks for fast acquisition of aortic 3D pressure and velocity flow fields. PLoS Computational Biology |
| title | Shape-driven deep neural networks for fast acquisition of aortic 3D pressure and velocity flow fields. |
| title_full | Shape-driven deep neural networks for fast acquisition of aortic 3D pressure and velocity flow fields. |
| title_fullStr | Shape-driven deep neural networks for fast acquisition of aortic 3D pressure and velocity flow fields. |
| title_full_unstemmed | Shape-driven deep neural networks for fast acquisition of aortic 3D pressure and velocity flow fields. |
| title_short | Shape-driven deep neural networks for fast acquisition of aortic 3D pressure and velocity flow fields. |
| title_sort | shape driven deep neural networks for fast acquisition of aortic 3d pressure and velocity flow fields |
| url | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011055&type=printable |
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