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CFD Modeling and Image Analysis of Exhaled Aerosols due to a Growing Bronchial Tumor: towards Non-Invasive Diagnosis and Treatment of Respiratory Obstructive Diseases

机译:支气管肿瘤生长引起的呼出气溶胶的CFD建模和图像分析:面向无创性诊断和治疗呼吸道阻塞性疾病

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摘要

Diagnosis and prognosis of tumorigenesis are generally performed with CT, PET, or biopsy. Such methods are accurate, but have the limitations of high cost and posing additional health risks to patients. In this study, we introduce an alternative computer aided diagnostic tool that can locate malignant sites caused by tumorigenesis in a non-invasive and low-cost way. Our hypothesis is that exhaled aerosol distribution is unique to lung structure and is sensitive to airway structure variations. With appropriate approaches, it is possible to locate the disease site, determine the disease severity, and subsequently formulate a targeted drug delivery plan to treat the disease. This study numerically evaluated the feasibility of the proposed breath test in an image-based lung model with varying pathological stages of a bronchial squamous tumor. Large eddy simulations and a Lagrangian tracking approach were used to model respiratory airflows and aerosol dynamics. Respirations of tracer aerosols of 1 µm at a flow rate of 20 L/min were simulated, with the distributions of exhaled aerosols recorded on a filter at the mouth exit. Aerosol patterns were quantified with multiple analytical techniques such as concentration disparity, spatial scanning and fractal analysis. We demonstrated that a growing bronchial tumor induced notable variations in both the airflow and exhaled aerosol distribution. These variations became more apparent with increasing tumor severity. The exhaled aerosols exhibited distinctive pattern parameters such as spatial probability, fractal dimension, and multifractal spectrum. Results of this study show that morphometric measures of the exhaled aerosol pattern can be used to detect and monitor the pathological states of respiratory diseases in the upper airway. The proposed breath test also has the potential to locate the site of the disease, which is critical in developing a personalized, site-specific drug delivery protocol.
机译:肿瘤发生的诊断和预后通常通过CT,PET或活检进行。这样的方法是准确的,但是具有高成本的局限性并且给患者带来额外的健康风险。在这项研究中,我们介绍了一种替代性的计算机辅助诊断工具,该工具可以以无创且低成本的方式定位由肿瘤发生引起的恶性部位。我们的假设是呼出的气溶胶分布是肺结构所独有的,并且对气道结构的变化很敏感。通过适当的方法,可以找到疾病的位置,确定疾病的严重程度,然后制定针对性的药物治疗计划来治疗该疾病。这项研究在数值上评估了在具有不同病理阶段的支气管鳞状肿瘤的基于图像的肺模型中提出的呼气试验的可行性。大型涡流模拟和拉格朗日跟踪方法用于模拟呼吸气流和气溶胶动力学。以20 L / min的流速模拟了1 µm示踪气溶胶的呼吸,并在出口处的过滤器上记录了呼出气溶胶的分布。使用多种分析技术(例如浓度差异,空间扫描和分形分析)对气溶胶模式进行了量化。我们证明了正在生长的支气管肿瘤在气流和呼出气溶胶分布中均引起了显着变化。随着肿瘤严重程度的增加,这些变化变得更加明显。呼出的气溶胶显示出独特的模式参数,例如空间概率,分形维数和多重分形谱。这项研究的结果表明,呼出气溶胶形态的形态学测量可以用于检测和监测上呼吸道呼吸系统疾病的病理状态。拟议的呼气测验还具有定位疾病部位的潜力,这对于开发个性化的,针对特定部位的药物输送协议至关重要。

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