Aiding Airway Obstruction Diagnosis With Computational Fluid Dynamics and Convolutional Neural Network: A New Perspective and Numerical Case Study
Aiding Airway Obstruction Diagnosis With Computational Fluid Dynamics and Convolutional Neural Network: A New Perspective and Numerical Case Study
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DOI：
10.1115/1.4053651
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发表时间：
2022-08-01
影响因子：
2
通讯作者：
Wang, Qingsheng
中科院分区：
文献类型：
--
作者：
Hu, Pingfan;Cai, Changjie;Wang, Qingsheng
使用现有的医学成像技术直接从可视化中定位慢性阻塞性肺疾病（COPD）引起的小气道阻塞具有挑战性。因此，本研究提出了一种创新的非侵入性诊断方法，使用计算流体动力学（CFD）和卷积神经网络（CNN）检测阻塞位置。具体而言，呼气气流速度轮廓从对象特定的3D气管支气管树中的CFD模拟获得。使用CFD研究了1例代表正常气道的病例和990例与不同阻塞部位相关的病例。标记气管中选定横截面处的呼气气流速度轮廓并将其存储为用于训练和测试两个CNN模型的数据库，即，ResNet 50和YOLOv 4。采用加权类别激活映射（Grad-CAM）和Pearson相关系数对小气道阻塞位置和肺气流模式变化进行分类，并突出显示轮廓中高度相关的区域以定位阻塞部位。结果表明，气流速度模式的变化是很难直接可视化的基础上比较计算流体动力学速度等值线。CNN结果表明，阻塞的位置和呼气气流速度轮廓之间存在很强的相关性。两个基于CNN的模型都能够使用CFD模拟的气流轮廓图像对左肺、右肺和双肺阻塞进行分类，总准确率高于95.07%。这两种自动分类算法对于使用呼气气流速度分布来早期诊断肺中的阻塞位置的临床实践具有高度的变革性，这可以使用超极化磁共振成像来成像。
It is challenging to locate small-airway obstructions induced by chronic obstructive pulmonary disease (COPD) directly from visualization using available medical imaging techniques. Accordingly, this study proposes an innovative and noninvasive diagnostic method to detect obstruction locations using computational fluid dynamics (CFD) and convolutional neural network (CNN). Specifically, expiratory airflow velocity contours were obtained from CFD simulations in a subject-specific 3D tracheobronchial tree. One case representing normal airways and 990 cases associated with different obstruction sites were investigated using CFD. The expiratory airflow velocity contours at a selected cross section in the trachea were labeled and stored as the database for training and testing two CNN models, i.e., ResNet50 and YOLOv4. Gradient-weighted class activation mapping (Grad-CAM) and the Pearson correlation coefficient were employed and calculated to classify small-airway obstruction locations and pulmonary airflow pattern shifts and highlight the highly correlated regions in the contours for locating the obstruction sites. Results indicate that the airflow velocity pattern shifts are difficult to directly visualize based on the comparisons of CFD velocity contours. CNN results show strong relevance exists between the locations of the obstruction and the expiratory airflow velocity contours. The two CNN-based models are both capable of classifying the left lung, right lung, and both lungs obstructions well using the CFD simulated airflow contour images with total accuracy higher than 95.07%. The two automatic classification algorithms are highly transformative to clinical practice for early diagnosis of obstruction locations in the lung using the expiratory airflow velocity distributions, which could be imaged using hyperpolarized magnetic resonance imaging.