For the one billion sufferers of respiratory disease, managing their disease with inhalers crucially influences their quality of life. Generic treatment plans could be improved with the aid of computational models that account for patient-specific features such as breathing pattern, lung pathology and morphology. Therefore, we aim to develop and validate an automated computational framework for patient-specific deposition modelling. To that end, an image processing approach is proposed that could produce 3D patient respiratory geometries from 2D chest X-rays and 3D CT images. We evaluated the airway and lung morphology produced by our image processing framework, and assessed deposition compared to in vivo data. The 2D-to-3D image processing reproduces airway diameter to 9% median error compared to ground truth segmentations, but is sensitive to outliers of up to 33% due to lung outline noise. Predicted regional deposition gave 5% median error compared to in vivo measurements. The proposed framework is capable of providing patient-specific deposition measurements for varying treatments, to determine which treatment would best satisfy the needs imposed by each patient (such as disease and lung/airway morphology). Integration of patient-specific modelling into clinical practice as an additional decision-making tool could optimise treatment plans and lower the burden of respiratory diseases.

翻译：对于全球十亿呼吸系统疾病患者而言，通过吸入器控制疾病对其生活质量具有决定性影响。考虑患者特异性特征（如呼吸模式、肺部病理及形态学）的计算模型可优化通用治疗方案。为此，本研究旨在开发并验证一种用于患者特异性药物沉积建模的自动化计算框架。针对该目标，我们提出了一种图像处理方法，能从二维胸部X光片与三维CT影像中生成患者呼吸道三维几何结构。我们评估了该图像处理框架生成的气道及肺部形态，并基于体内数据对药物沉积效果进行了验证。相较于金标准分割结果，二维到三维图像处理重建的气道直径中位误差为9%，但受肺部轮廓噪声影响，存在高达33%的离群敏感度。区域沉积预测与体内测量值的中位误差为5%。该框架能够针对不同治疗方案提供患者特异性沉积测量，从而确定最适合个体需求（如疾病类型、肺/气道形态）的治疗方案。将患者特异性建模作为辅助决策工具纳入临床实践，可优化治疗方案并降低呼吸系统疾病负担。