ArticleInternational journal for numerical methods in biomedical engineering2020
Combining statistical shape modeling, CFD, and meta-modeling to approximate the patient-specific pressure-drop across the aortic valve in real-time.
Article in International journal for numerical methods in biomedical engineering, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- HUG-VAS: A Hierarchical NURBS-Based Generative Model for Aortic Geometry Synthesis and Controllable Editing.Computer methods in applied mechanics and engineering · 2026Article
- Morphological Variation in the Human Mitral Valve Using Statistical Shape Modelling.Annals of biomedical engineering · 2026Article
- Evaluation of the Haemodynamic Behaviour of Stenosed Aortic Heart Valves Using Fluid Structure Interaction Modelling.International journal for numerical methods in biomedical engineering · 2026Article
- Generation of a virtual cohort of TAVI patients for in silico trials: a statistical shape and machine learning analysis.Medical & biological engineering & computing · 2025Article
- A Novel Attention-based Network for Geometry Reconstruction with Error Estimation from Medical Images.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- Algorithmic Generation of Parameterized Geometric Models of the Aortic Valve and Left Ventricle.Sensors (Basel, Switzerland) · 2024Article
- Virtual Shape-Editing of Patient-Specific Vascular Models Using Regularized Kelvinlets.IEEE transactions on bio-medical engineering · 2024Article
- Euclidean and Shape-Based Analysis of the Dynamic Mitral Annulus in Children using a Novel Open-Source Framework.Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography · 2024Article
- Article
- A parametric geometry model of the aortic valve for subject-specific blood flow simulations using a resistive approach.Biomechanics and modeling in mechanobiology · 2023Article
- Shape-driven deep neural networks for fast acquisition of aortic 3D pressure and velocity flow fields.PLoS computational biology · 2023Article
- Modelling blood flow in patients with heart valve disease using deep learning: A computationally efficient method to expand diagnostic capabilities in clinical routine.Frontiers in cardiovascular medicine · 2023Article
- The impact of shape uncertainty on aortic-valve pressure-drop computations.International journal for numerical methods in biomedical engineering · 2021Article
- Data-driven cardiovascular flow modelling: examples and opportunities.Journal of the Royal Society, Interface · 2021Review
- Combining statistical shape modeling, CFD, and meta-modeling to approximate the patient-specific pressure-drop across the aortic valve in real-time.International journal for numerical methods in biomedical engineering · 2020Article
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4 authors.
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Abstract
backgroundAdvances in medical imaging, segmentation techniques, and high performance computing have stimulated the use of complex, patient-specific, three-dimensional Computational Fluid Dynamics (CFD) simulations. Patient-specific, CFD-compatible geometries of the aortic valve are readily obtained. CFD can then be used to obtain the patient-specific pressure-flow relationship of the aortic valve. However, such CFD simulations are computationally expensive, and real-time alternatives are desired.
aimThe aim of this work is to evaluate the performance of a meta-model with respect to high-fidelity, three-dimensional CFD simulations of the aortic valve.
methodsPrincipal component analysis was used to build a statistical shape model (SSM) from a population of 74 iso-topological meshes of the aortic valve. Synthetic meshes were created with the SSM, and steady-state CFD simulations at flow-rates between 50 and 650 mL/s were performed to build a meta-model. The meta-model related the statistical shape variance, and flow-rate to the pressure-drop.
resultsEven though the first three shape modes account for only 46% of shape variance, the features relevant for the pressure-drop seem to be captured. The three-mode shape-model approximates the pressure-drop with an average error of 8.8% to 10.6% for aortic valves with a geometric orifice area below 150 mm
conclusionsStatistical shape modeling can be used to capture shape variation of the aortic valve. Meta-models trained by SSM-based CFD simulations can provide an estimate of the pressure-flow relationship in real-time.
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