Combining statistical shape modeling, CFD, and meta-modeling to approximate the patient-specific pressure-drop across the aortic valve in real-time.
Combining statistical shape modeling, CFD, and meta-modeling to approximate the patient-specific pressure-drop across the aortic valve in real-time.
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DOI：
10.1002/cnm.3387
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发表时间：
2020-10
影响因子：
2.1
通讯作者：
Van de Vosse FN
中科院分区：
文献类型：
--
作者：
Hoeijmakers MJMM;Waechter-Stehle I;Weese J;Van de Vosse FN
医学成像、分割技术和高性能计算的进步刺激了复杂的、针对患者的三维计算流体动力学 (CFD) 模拟的使用。可以轻松获得患者特定的、与 CFD 兼容的主动脉瓣几何形状。然后可以使用 CFD 来获取患者特定的主动脉瓣压力-流量关系。然而，这种 CFD 模拟的计算成本很高，并且需要实时替代方案。这项工作的目的是评估元模型在主动脉瓣高保真三维 CFD 模拟方面的性能。主成分分析用于从主动脉瓣的 74 个同拓扑网格群构建统计形状模型 (SSM)。使用 SSM 创建合成网格，并在 50 至 650mL/s 之间的流速下进行稳态 CFD 模拟，以构建元模型。元模型将统计形状方差和流量与压降联系起来。尽管前三种形状模式仅占形状方差的 46%，但似乎捕获了与压降相关的特征。对于几何孔口面积低于 150 mm2 的主动脉瓣，三模式形状模型近似压降，平均误差为 8.8% 至 10.6%。对于 150mm2 以上的主动脉瓣面积，所提出的方法最不准确。进一步简化元模型会带来 3% 的额外误差。统计形状建模可用于捕获主动脉瓣的形状变化。通过基于 SSM 的 CFD 模拟训练的元模型可以实时提供压力-流量关系的估计。在这项研究中，统计形状建模、计算流体动力学（CFD）和元建模技术相结合，获得了一种评估成本低廉的元模型。该元模型将 74 个分段主动脉瓣的形状变化与 CFD 计算的流量与压降曲线的变化联系起来。经过训练后，元模型可以成为计算密集型 CFD 模拟的廉价而强大的替代方案。
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. 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. 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. 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. 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.
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DOI：
10.1136/heartjnl-2015-308044
发表时间：
2016-01
期刊：
Heart (British Cardiac Society)
影响因子：
--
作者：
Morris PD;Narracott A;von Tengg-Kobligk H;Silva Soto DA;Hsiao S;Lungu A;Evans P;Bressloff NW;Lawford PV;Hose DR;Gunn JP
通讯作者：
Gunn JP
影响因子：
2.4
作者：
De Hart, J;Peters, GWM;Baaijens, FPT
通讯作者：
Baaijens, FPT
DOI：
10.1002/cnm.2474
发表时间：
2012-09-01
影响因子：
2.1
作者：
Astorino, Matteo;Hamers, Jeroen;Gerbeau, Jean-Frederic
通讯作者：
Gerbeau, Jean-Frederic
DOI：
10.1136/heartjnl-2016-310423
发表时间：
2017-01-15
期刊：
Heart (British Cardiac Society)
影响因子：
--
作者：
Biglino G;Capelli C;Bruse J;Bosi GM;Taylor AM;Schievano S
通讯作者：
Schievano S
影响因子：
2.7
作者：
Baumbach, Sebastian F.;Binder, Jakob;Fischer, Lukas
通讯作者：
Fischer, Lukas