Kannan Ravishekar Ravi, Singh Narender, Przekwas Andrzej
CFD Research Corporation, 701 McMillian Way NW, Suite D, Huntsville, AL, 35806, USA.
Int J Numer Method Biomed Eng. 2018 May;34(5):e2973. doi: 10.1002/cnm.2973. Epub 2018 Mar 30.
Spirometry is a widely used pulmonary function test to detect the airflow limitations associated with various obstructive lung diseases, such as asthma, chronic obstructive pulmonary disease, and even obesity-related complications. These conditions arise due to the change in the airway resistance, alveolar compliance, and inductance values. Currently, zero-dimensional compartmental models are commonly used for calibrating these resistance, compliance, and inductance values, ie, solving the inverse spirometry problem. However, zero-dimensional compartments cannot capture the flow physics or the spatial geometry effects, thereby generating a low fidelity prediction of the diseased lung. Computational fluid dynamics (CFD) models offer higher fidelity solutions but may be impractical for certain applications due to the duration of these simulations. Recently, a novel, fast-running, and robust Quasi-3D (Q3D) wire model for simulating the airflow in the human lung airway was developed by CFD Research Corporation. This Q3D method preserved the 3D spatial nature of the airways and was favorably validated against CFD solutions. In the present study, the Q3D compartmental multi-scale combination is further improved to predict regional lung constriction of diseased lungs using spirometry data. The Q3D mesh is resolved up to the eighth lung airway generation. The remainder of the airways and the alveoli sections are modeled using a compartmental approach. The Q3D geometry is then split into different spatial sections, and the resistance values in these regions are obtained using parameter inversion. Finally, the airway diameter values are then reduced to create the actual diseased lung model, corresponding to these resistance values. This diseased lung model can be used for patient-specific drug deposition predictions and the subsequent optimization of the orally inhaled drug products.
肺量测定法是一种广泛应用的肺功能测试，用于检测与各种阻塞性肺病相关的气流受限情况，如哮喘、慢性阻塞性肺病，甚至肥胖相关并发症。这些病症是由于气道阻力、肺泡顺应性和电感值的变化而产生的。目前，零维房室模型通常用于校准这些阻力、顺应性和电感值，即解决逆肺量测定问题。然而，零维房室无法捕捉流动物理或空间几何效应，从而对患病肺部产生低精度预测。计算流体动力学（CFD）模型提供了更高精度的解决方案，但由于这些模拟的持续时间，对于某些应用可能不切实际。最近，CFD研究公司开发了一种新颖、快速运行且稳健的准三维（Q3D）线模型，用于模拟人肺气道中的气流。这种Q3D方法保留了气道的三维空间特性，并与CFD解决方案进行了良好的验证。在本研究中，进一步改进了Q3D房室多尺度组合，以使用肺量测定数据预测患病肺部的区域肺收缩。Q3D网格解析到第八级肺气道生成。其余气道和肺泡部分采用房室方法建模。然后将Q3D几何形状划分为不同的空间部分，并使用参数反演获得这些区域的阻力值。最后，减小气道直径值以创建与这些阻力值对应的实际患病肺部模型。这种患病肺部模型可用于患者特异性药物沉积预测以及随后口服吸入药物产品的优化。
