Gashi K, Bosboom E M H, van de Vosse F N
Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands.
Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands.
J Biomech. 2019 Jan 3;82:313-323. doi: 10.1016/j.jbiomech.2018.11.008. Epub 2018 Nov 14.
Computational fluid dynamics (CFD) models combined with patient-specific imaging data are used to non-invasively predict functional significance of coronary lesions. This approach to predict the fractional flow reserve (FFR) is shown to have a high diagnostic accuracy when comparing against invasively measured FFR. However, one of the main drawbacks is the high computational effort needed for preprocessing and computations. Hence, uncertainty quantification may become unfeasible. Reduction of complexity is desirable, computationally inexpensive models with high diagnostic accuracy are preferred. We present a parametric comparison study for three types of CFD models (2D axisymmetric, Semi-3D and 3D) in which we study the impact of model reduction on three models on the predicted FFR. In total 200 coronary geometries were generated for seven geometrical characteristics e.g. stenosis severity, stenosis length and vessel curvature. The effect of time-averaged flow was investigated using unsteady, mean steady and a root mean square (RMS) steady flow. The 3D unsteady model was regarded as reference model. Results show that when using an unsteady or RMS flow, predicted FFR hardly varies between models contrary to using average flows. The 2D model with RMS flow has a high diagnostic accuracy (0.99), reduces computational time by a factor 162,000 and the introduced model error is well below the clinical relevant differences. Stenosis severity, length, curvature and tapering cause most discrepancies when using a lower order model. An uncertainty analysis showed that this can be explained by the low variability that is caused by variations in stenosis asymmetry.
计算流体动力学（CFD）模型与患者特异性成像数据相结合，用于无创预测冠状动脉病变的功能意义。与侵入性测量的血流储备分数（FFR）相比，这种预测FFR的方法具有较高的诊断准确性。然而，主要缺点之一是预处理和计算所需的计算量很大。因此，不确定性量化可能变得不可行。降低复杂性是可取的，具有高诊断准确性且计算成本低的模型更受青睐。我们对三种类型的CFD模型（二维轴对称模型、半三维模型和三维模型）进行了参数比较研究，研究了模型简化对三种模型预测FFR的影响。针对七种几何特征（如狭窄严重程度、狭窄长度和血管曲率）总共生成了200个冠状动脉几何模型。使用非稳态、平均稳态和均方根（RMS）稳态流研究了时间平均流的影响。将三维非稳态模型视为参考模型。结果表明，与使用平均流相反，当使用非稳态或RMS流时，模型之间预测的FFR几乎没有变化。具有RMS流的二维模型具有较高的诊断准确性（0.99），计算时间减少了162000倍，引入的模型误差远低于临床相关差异。当使用低阶模型时，狭窄严重程度、长度、曲率和逐渐变细会导致最大差异。不确定性分析表明，这可以通过狭窄不对称性变化引起的低变异性来解释。
