Data assimilation performed with robust shape registration and graph neural networks: application to aortic coarctation
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866929718295527424 |
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| author | Romor, Francesco Galarce, Felipe Brüning, Jan Goubergrits, Leonid Caiazzo, Alfonso |
| author_facet | Romor, Francesco Galarce, Felipe Brüning, Jan Goubergrits, Leonid Caiazzo, Alfonso |
| contents | Image-based, patient-specific modelling of hemodynamics can improve diagnostic capabilities and provide complementary insights to better understand the hemodynamic treatment outcomes. However, computational fluid dynamics simulations remain relatively costly in a clinical context. Moreover, projection-based reduced-order models and purely data-driven surrogate models struggle due to the high variability of anatomical shapes in a population. A possible solution is shape registration: a reference template geometry is designed from a cohort of available geometries, which can then be diffeomorphically mapped onto it. This provides a natural encoding that can be exploited by machine learning architectures and, at the same time, a reference computational domain in which efficient dimension-reduction strategies can be performed. We compare state-of-the-art graph neural network models with recent data assimilation strategies for the prediction of physical quantities and clinically relevant biomarkers in the context of aortic coarctation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_12097 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data assimilation performed with robust shape registration and graph neural networks: application to aortic coarctation Romor, Francesco Galarce, Felipe Brüning, Jan Goubergrits, Leonid Caiazzo, Alfonso Numerical Analysis Image-based, patient-specific modelling of hemodynamics can improve diagnostic capabilities and provide complementary insights to better understand the hemodynamic treatment outcomes. However, computational fluid dynamics simulations remain relatively costly in a clinical context. Moreover, projection-based reduced-order models and purely data-driven surrogate models struggle due to the high variability of anatomical shapes in a population. A possible solution is shape registration: a reference template geometry is designed from a cohort of available geometries, which can then be diffeomorphically mapped onto it. This provides a natural encoding that can be exploited by machine learning architectures and, at the same time, a reference computational domain in which efficient dimension-reduction strategies can be performed. We compare state-of-the-art graph neural network models with recent data assimilation strategies for the prediction of physical quantities and clinically relevant biomarkers in the context of aortic coarctation. |
| title | Data assimilation performed with robust shape registration and graph neural networks: application to aortic coarctation |
| topic | Numerical Analysis |
| url | https://arxiv.org/abs/2502.12097 |