Lab. of Fluid Dynamics and Technical Flows, Otto von Guericke University Magdeburg, Germany.
Institute of Experimental Physics, Otto von Guericke University Magdeburg, Germany; Institute of Biometry and Medical Informatics, Otto von Guericke University Magdeburg, Germany.
Comput Biol Med. 2019 Dec;115:103507. doi: 10.1016/j.compbiomed.2019.103507. Epub 2019 Oct 16.
Hemodynamic simulations are restricted by modeling assumptions and uncertain initial and boundary conditions, whereas Phase-Contrast Magnetic Resonance Imaging (PC-MRI) data is affected by measurement noise and artifacts. To overcome the limitations of both techniques, the current study uses a Localization Ensemble Transform Kalman Filter (LETKF) to fully incorporate noisy, low-resolution Phase-Contrast MRI data into an ensemble of high-resolution numerical simulations. The analysis output provides an improved state estimate of the three-dimensional blood flow field in an intracranial aneurysm model. Benchmark measurements are carried out in a silicone phantom model of an idealized aneurysm under pulsatile inflow conditions. Validation is ensured with high-resolution Particle Imaging Velocimetry (PIV) obtained in the symmetry plane of the same geometry. Two data assimilation approaches are introduced, which differ in their way to propagate the ensemble members in time. In both cases the velocity noise is significantly reduced over the whole cardiac cycle. Quantitative and qualitative results indicate an improvement of the flow field prediction in comparison to the raw measurement data. Although biased measurement data reveal a systematic deviation from the truth, the LETKF is able to account for stochastically distributed errors. Through the implementation of the data assimilation step, physical constraints are introduced into the raw measurement data. The resulting, realistic high-resolution flow field can be readily used to assess further patient-specific parameters in addition to the velocity distribution, such as wall shear stress or pressure.
血流动力学模拟受到建模假设和不确定的初始和边界条件的限制，而相位对比磁共振成像 (PC-MRI) 数据则受到测量噪声和伪影的影响。为了克服这两种技术的局限性，本研究使用局部化集合变换卡尔曼滤波器 (LETKF) 将嘈杂的、低分辨率的相位对比 MRI 数据完全纳入高分辨率数值模拟的集合中。分析输出提供了颅内动脉瘤模型中三维血流场的改进状态估计。在脉动流入条件下，在理想动脉瘤的硅酮体模模型中进行基准测量。通过相同几何形状的对称平面中获得的高分辨率粒子成像速度测量法 (PIV) 进行验证。引入了两种数据同化方法，它们在时间上传播集合成员的方式不同。在这两种情况下，整个心动周期内的速度噪声都显著降低。定量和定性结果表明，与原始测量数据相比，流场预测得到了改善。尽管有偏差的测量数据显示出与真实值的系统偏差，但 LETKF 能够解释随机分布的误差。通过实施数据同化步骤，将物理约束引入原始测量数据中。由此产生的现实的高分辨率流场可以很容易地用于评估除速度分布之外的其他患者特定参数，例如壁面切应力或压力。
