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基于计算机断层扫描成像的个体全肺沉积模型。

A computed tomography imaging-based subject-specific whole-lung deposition model.

机构信息

Department of Mechanical Engineering, 2406 Seamans Center for the Engineering Art and Science, University of Iowa, Iowa City, Iowa 52242, USA; IIHR-Hydroscience and Engineering, University of Iowa, Iowa City, Iowa, USA.

IIHR-Hydroscience and Engineering, University of Iowa, Iowa City, Iowa, USA; Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa, USA.

出版信息

Eur J Pharm Sci. 2022 Oct 1;177:106272. doi: 10.1016/j.ejps.2022.106272. Epub 2022 Jul 29.

Abstract

The respiratory tract is an important route for beneficial drug aerosol or harmful particulate matter to enter the body. To assess the therapeutic response or disease risk, whole-lung deposition models have been developed, but were limited by compartment, symmetry or stochastic approaches. In this work, we proposed an imaging-based subject-specific whole-lung deposition model. The geometries of airways and lobes were segmented from computed tomography (CT) lung images at total lung capacity (TLC), and the regional air-volume changes were calculated by registering CT images at TLC and functional residual capacity (FRC). The geometries were used to create the structure of entire subject-specific conducting airways and acinar units. The air-volume changes were used to estimate the function of subject-specific ventilation distributions among acinar units and regulate flow rates in respiratory airway models. With the airway dimensions rescaled to a desired lung volume and the airflow field simulated by a computational fluid dynamics model, particle deposition fractions were calculated using deposition probability formulae adjusted with an enhancement factor to account for the effects of secondary flow and airway geometry in proximal airways. The proposed model was validated in silico against existing whole-lung deposition models, three-dimensional (3D) computational fluid and particle dynamics (CFPD) for an acinar unit, and 3D CFPD deep lung model comprising conducting and respiratory regions. The model was further validated in vivo against the lobar particle distribution and the coefficient of variation of particle distribution obtained from CT and single-photon emission computed tomography (SPECT) images, showing good agreement. Subject-specific airway structure increased the deposition fraction of 10.0-μm particles and 0.01-μm particles by approximately 10%. An enhancement factor increased the overall deposition fractions, especially for particle sizes between 0.1 and 1.0 μm.

摘要

呼吸道是有益药物气溶胶或有害颗粒物进入人体的重要途径。为了评估治疗反应或疾病风险,已经开发了全肺沉积模型,但受到隔室、对称性或随机方法的限制。在这项工作中,我们提出了一种基于成像的个体全肺沉积模型。在总肺容量 (TLC) 时,从计算机断层扫描 (CT) 肺部图像中分割气道和叶的几何形状,并通过注册 TLC 和功能残气量 (FRC) 时的 CT 图像计算区域空气体积变化。这些几何形状用于创建整个个体特定传导气道和腺泡单元的结构。空气体积变化用于估计腺泡单元之间个体特定通气分布的功能,并调节呼吸气道模型中的流量。通过将气道尺寸缩放到所需的肺容量,并通过计算流体动力学模型模拟气流场,使用调整了增强因子的沉积概率公式计算粒子沉积分数,以考虑次级流和近端气道中气道几何形状的影响。该模型通过使用计算流体和粒子动力学 (CFPD) 模型模拟的三维 (3D) 腺泡单元和包含传导和呼吸区域的三维 CFPD 深部肺模型,对现有全肺沉积模型、3D CFPD 进行了计算机模拟验证。进一步通过 CT 和单光子发射计算机断层扫描 (SPECT) 图像获得的叶状粒子分布和粒子分布的变异系数进行体内验证,显示出良好的一致性。个体特定的气道结构增加了 10.0-μm 粒子和 0.01-μm 粒子的沉积分数约 10%。增强因子增加了整体沉积分数,特别是对于 0.1 到 1.0 μm 之间的粒径。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/381d/9477651/c6458d1eed8d/nihms-1833271-f0001.jpg

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