School of Mechanical Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, South Korea.
School of Aerospace Engineering, Huazhong University of Science and Technology, Wuhan, China.
Comput Methods Programs Biomed. 2024 Apr;246:108061. doi: 10.1016/j.cmpb.2024.108061. Epub 2024 Feb 6.
A detailed representation of the airway geometry in the respiratory system is critical for predicting precise airflow and pressure behaviors in computed tomography (CT)-image-based computational fluid dynamics (CFD). The CT-image-based geometry often contains artifacts, noise, and discontinuities due to the so-called stair step effect. Hence, an advanced surface smoothing is necessary. The existing smoothing methods based on the Laplacian operator drastically shrink airway geometries, resulting in the loss of information related to smaller branches. This study aims to introduce an unsupervised airway-mesh-smoothing learning (AMSL) method that preserves the original geometry of the three-dimensional (3D) airway for accurate CT-image-based CFD simulations.
The AMSL method jointly trains two graph convolutional neural networks (GCNNs) defined on airway meshes to filter vertex positions and face normal vectors. In addition, it regularizes a combination of loss functions such as reproducibility, smoothness and consistency of vertex positions, and normal vectors. The AMSL adopts the concept of a deep mesh prior model, and it determines the self-similarity for mesh restoration without using a large dataset for training. Images of the airways of 20 subjects were smoothed by the AMSL method, and among them, the data of two subjects were used for the CFD simulations to assess the effect of airway smoothing on flow properties.
In 18 of 20 benchmark problems, the proposed smoothing method delivered better results compared with the conventional or state-of-the-art deep learning methods. Unlike the traditional smoothing, the AMSL successfully constructed 20 smoothed airways with airway diameters that were consistent with the original CT images. Besides, CFD simulations with the airways obtained by the AMSL method showed much smaller pressure drop and wall shear stress than the results obtained by the traditional method.
The airway model constructed by the AMSL method reproduces branch diameters accurately without any shrinkage, especially in the case of smaller airways. The accurate estimation of airway geometry using a smoothing method is critical for estimating flow properties in CFD simulations.
呼吸系统气道的详细几何形状对于预测基于计算机断层扫描（CT）图像的计算流体动力学（CFD）中的精确气流和压力行为至关重要。基于 CT 图像的几何形状通常由于所谓的阶梯效应而包含伪影、噪声和不连续性。因此，需要进行先进的表面平滑处理。现有的基于拉普拉斯算子的平滑方法会极大地缩小气道几何形状，导致与较小分支相关的信息丢失。本研究旨在引入一种无监督气道网格平滑学习（AMSL）方法，该方法可保留三维（3D）气道的原始几何形状，以实现准确的 CT 图像基于 CFD 模拟。
AMSL 方法联合训练两个定义在气道网格上的图卷积神经网络（GCNNs），以过滤顶点位置和面法向量。此外，它还对顶点位置和法向量的复制性、平滑度和一致性等损失函数进行正则化。AMSL 采用深度网格先验模型的概念，无需使用大型数据集进行训练即可确定网格恢复的自相似性。采用 AMSL 方法对 20 名受试者的气道图像进行平滑处理，其中两名受试者的数据用于 CFD 模拟，以评估气道平滑对流动特性的影响。
在 20 个基准问题中的 18 个问题中，与传统或最先进的深度学习方法相比，所提出的平滑方法提供了更好的结果。与传统平滑方法不同，AMSL 成功构建了 20 个具有与原始 CT 图像一致的气道直径的平滑气道。此外，与传统方法相比，采用 AMSL 方法获得的气道的 CFD 模拟显示出较小的压降和壁面剪切应力。
AMSL 方法构建的气道模型在没有任何收缩的情况下准确再现分支直径，尤其是在较小的气道情况下。使用平滑方法准确估计气道几何形状对于估计 CFD 模拟中的流动特性至关重要。
