Abstract Even with the advance in medical imaging techniques such as CT/MRI, it is still challenging and time-consuming to reconstruct anatomically accurate lung geometries. It is even more challenging to study variability in inhalation dosimetry or pulmonary drug delivery, which requires a large cohort of lung models to ensure statistically significant results. This study used the statistical shape modeling (SSM) that bases on a limited number of lung models (40) to generate infinitely large numbers of parameterized models, which can span all major features inherent in the database of lung geometries. We demonstrated this model in lung models with more than 400 outlets (G9), which first identified the principal components (PCs) of base models, and then regenerated new models by systematically varying the mode (eigenvector) and its eigenvalues. The new models included airway remodeling at varying locations (left upper lobe and right lower lobe) and with varying levels of airway distensibility (compliance) and constriction (resistance). Airflow and aerosol dynamics within these lung geometries were numerically computed and compared. Results showed that even though the airway remodeling can be local, its influences on flow partition and deposition distribution can be global. Asthma-induced bronchiolar constriction, when severe, can strikingly alter the airflow and particle deposition mapping throughout the lungs. The highest deposition variability due to airway remodeling was found to come from particles of 4–10 μm in the upper lobes, and of 10–20 μm in the lower lobe. Statistical shape modeling is an imaging processing method that has often been used in computer sciences. This is the first study, to the author's knowledge, that SSM was applied in lung models with high complexity to quantify the resultant variances from these geometry remodeling. This method was also applied to lung models with 3000 outlets (G11) to generate diseased lung models at varying locations.

中文翻译：

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肺形态统计形状建模在气溶胶吸入剂量测定中的应用

摘要 即使随着 CT/MRI 等医学成像技术的进步，重建解剖学上准确的肺几何结构仍然具有挑战性和耗时。研究吸入剂量学或肺部药物输送的变异性更具挑战性，这需要大量的肺模型来确保具有统计学意义的结果。本研究使用基于有限数量的肺模型 (40) 的统计形状建模 (SSM) 来生成无限数量的参数化模型，这些模型可以涵盖肺几何数据库中固有的所有主要特征。我们在具有 400 多个出口 (G9) 的肺模型中演示了该模型，该模型首先确定了基本模型的主成分 (PC)，然后通过系统地改变模式（特征向量）及其特征值来重新生成新模型。新模型包括不同位置（左上叶和右下叶）的气道重塑，以及不同程度的气道扩张性（顺应性）和收缩性（阻力）。对这些肺几何结构内的气流和气溶胶动力学进行了数值计算和比较。结果表明，尽管气道重塑可以是局部的，但其对流动分配和沉积分布的影响可能是全球性的。哮喘引起的细支气管收缩严重时会显着改变整个肺部的气流和颗粒沉积分布图。发现由于气道重塑引起的最高沉积变异性来自上叶 4-10 μm 和下叶 10-20 μm 的颗粒。统计形状建模是计算机科学中经常使用的一种成像处理方法。据作者所知，这是第一项研究，将 SSM 应用于具有高度复杂性的肺模型，以量化这些几何重构的结果差异。该方法还应用于具有 3000 个出口 (G11) 的肺模型，以在不同位置生成患病肺模型。