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10亿患有呼吸道疾病的人,用吸入器管理他们的疾病,严重影响他们的生活质量。一般治疗计划可以通过计算模型来改进。一般治疗计划可以改进,这些模型考虑到病人特有的特征,例如呼吸模式、肺病理和形态学。因此,我们的目标是为病人特有的沉降建模制定和验证自动计算框架。为此,建议采用图像处理方法,从2D胸X光和3DCT图像中产生3D病人的呼吸呼吸道和呼吸道偏差。我们评估了我们图像处理框架产生的空气和肺部形态学,并比活体数据评估了沉积情况。2D至3D图像处理将空气道直径复制到9%的中位错误,而与地面的真象分解相比,对高达33%的外缘敏感。预测区域沉积提供了5%的中位误,与活体测量相比。拟议框架能够为不同的治疗提供病人特有的沉积测量,以确定哪种治疗最能满足每个病人的额外需要(例如疾病和肺气道/气道形态学),并且将病人选择性病原病原治疗计划纳入临床模型。</s>