Deep vectorised operators for pulsatile hemodynamics estimation in coronary arteries from a steady-state prior
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arXiv
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| Format: | Preprint |
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2024
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| author | Suk, Julian Nannini, Guido Rygiel, Patryk Brune, Christoph Pontone, Gianluca Redaelli, Alberto Wolterink, Jelmer M. |
| author_facet | Suk, Julian Nannini, Guido Rygiel, Patryk Brune, Christoph Pontone, Gianluca Redaelli, Alberto Wolterink, Jelmer M. |
| contents | Cardiovascular hemodynamic fields provide valuable medical decision markers for coronary artery disease. Computational fluid dynamics (CFD) is the gold standard for accurate, non-invasive evaluation of these quantities in silico. In this work, we propose a time-efficient surrogate model, powered by machine learning, for the estimation of pulsatile hemodynamics based on steady-state priors. We introduce deep vectorised operators, a modelling framework for discretisation-independent learning on infinite-dimensional function spaces. The underlying neural architecture is a neural field conditioned on hemodynamic boundary conditions. Importantly, we show how relaxing the requirement of point-wise action to permutation-equivariance leads to a family of models that can be parametrised by message passing and self-attention layers. We evaluate our approach on a dataset of 74 stenotic coronary arteries extracted from coronary computed tomography angiography (CCTA) with patient-specific pulsatile CFD simulations as ground truth. We show that our model produces accurate estimates of the pulsatile velocity and pressure (approximation disparity 0.368 $\pm$ 0.079) while being agnostic ($p < 0.05$ in a one-way ANOVA test) to re-sampling of the source domain, i.e. discretisation-independent. This shows that deep vectorised operators are a powerful modelling tool for cardiovascular hemodynamics estimation in coronary arteries and beyond. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_11920 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep vectorised operators for pulsatile hemodynamics estimation in coronary arteries from a steady-state prior Suk, Julian Nannini, Guido Rygiel, Patryk Brune, Christoph Pontone, Gianluca Redaelli, Alberto Wolterink, Jelmer M. Quantitative Methods Machine Learning Cardiovascular hemodynamic fields provide valuable medical decision markers for coronary artery disease. Computational fluid dynamics (CFD) is the gold standard for accurate, non-invasive evaluation of these quantities in silico. In this work, we propose a time-efficient surrogate model, powered by machine learning, for the estimation of pulsatile hemodynamics based on steady-state priors. We introduce deep vectorised operators, a modelling framework for discretisation-independent learning on infinite-dimensional function spaces. The underlying neural architecture is a neural field conditioned on hemodynamic boundary conditions. Importantly, we show how relaxing the requirement of point-wise action to permutation-equivariance leads to a family of models that can be parametrised by message passing and self-attention layers. We evaluate our approach on a dataset of 74 stenotic coronary arteries extracted from coronary computed tomography angiography (CCTA) with patient-specific pulsatile CFD simulations as ground truth. We show that our model produces accurate estimates of the pulsatile velocity and pressure (approximation disparity 0.368 $\pm$ 0.079) while being agnostic ($p < 0.05$ in a one-way ANOVA test) to re-sampling of the source domain, i.e. discretisation-independent. This shows that deep vectorised operators are a powerful modelling tool for cardiovascular hemodynamics estimation in coronary arteries and beyond. |
| title | Deep vectorised operators for pulsatile hemodynamics estimation in coronary arteries from a steady-state prior |
| topic | Quantitative Methods Machine Learning |
| url | https://arxiv.org/abs/2410.11920 |