Combining statistical shape modeling, CFD, and meta‐modeling to approximate the patient‐specific pressure‐drop across the aortic valve in real‐time. (13th September 2020)

• Record Type: Journal Article
• Authors: Hoeijmakers, M. J. M. M.; Waechter‐Stehle, I.; Weese, J.; Van de Vosse, F. N.
• Is Part Of: International journal for numerical methods in biomedical engineering. Volume 36:Number 10(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-09-13
• Subjects: aortic valve stenosis -- computational fluid dynamics -- heart valve disease -- meta‐modeling -- statistical shape modeling; Biomedical engineering -- Periodicals; Imaging systems in medicine -- Periodicals; Numerical analysis -- Periodicals; Engineering mathematics -- Periodicals; 610.28
• DOI: 10.1002/cnm.3387
• Languages: English
• ISSNs: 2040-7939

Abstract

Abstract: 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 mm 2 . The proposed methodology was least accurate for aortic valve areas above 150 mm 2 . 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. Abstract : 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.