Combining statistical shape modeling, CFD, and meta-modeling to approximate the patient-specific pressure-drop across the aortic valve in real-time

Autor: M. J. M. M. Hoeijmakers, Irina Waechter-Stehle, F.N. van de Vosse, Juergen Weese
Přispěvatelé: Eindhoven MedTech Innovation Center, Cardiovascular Biomechanics, EAISI Health
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Aortic valve
Computer science
heart valve disease
0206 medical engineering
Population
Biomedical Engineering
aortic valve stenosis
02 engineering and technology
computational fluid dynamics
030204 cardiovascular system & hematology
Computational fluid dynamics
meta-modeling
meta‐modeling
03 medical and health sciences
0302 clinical medicine
Research Article ‐ Applications
medicine
Humans
Polygon mesh
Segmentation
education
Molecular Biology
Research Article ‐ Application
Pressure drop
education.field_of_study
business.industry
Applied Mathematics
Hemodynamics
Models
Cardiovascular
medicine.disease
020601 biomedical engineering
medicine.anatomical_structure
Computational Theory and Mathematics
Aortic Valve
Modeling and Simulation
Aortic valve stenosis
Hydrodynamics
Reduction (mathematics)
business
Algorithm
Software
statistical shape modeling
Zdroj: International Journal for Numerical Methods in Biomedical Engineering
International Journal for Numerical Methods in Biomedical Engineering, 36(10):e3387. Wiley-Blackwell
ISSN: 2040-7939
Popis: Background Advances 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. Aim The 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. Methods Principal 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. Results Even 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 mm2. The proposed methodology was least accurate for aortic valve areas above 150 mm2. Further reduction to a meta‐model introduces an additional 3% error. Conclusions Statistical 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.
In this study, statistical shape modeling, computational fluid dynamics (CFD), and meta‐modeling techniques were combined to obtain a cheap‐to‐evaluate meta‐model. The meta‐model relates shape variation of 74 segmented aortic valves, to variations in CFD‐computed flow vs pressure‐drop curves. Once trained, meta‐models can be a cheap and robust alternative to compute intensive CFD simulations.
Databáze: OpenAIRE
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