Three dimensional Electrical Capacitance Tomography (3D ECT) is an inexpensive tool for diagnosing non-conductive components of industrial processes. Although relatively mature, it still requires much work to improve its inverse nature of imaging capability. In particular, high resolution 3D ECT image reconstruction is very time-consuming and computationally heavy, and the best-known 3D ECT image reconstruction techniques have already reached their limits. Thus, there is a strong need to change a direction towards modern computational intelligence solutions. Therefore, this work proposes using graph convolutional networks (GCN) to raise the 3D ECT image quality. Mainly, it takes advantage of GCN’s ability to effectively use specific geometrical relationships hidden in the finite modeling unstructured grids commonly used to build 3D ECT images. These relationships are first encoded by a graph representing an ECT volumetric finite element grid. A GCN is next trained in a graph-to-graph framework with pairs of graphs representing high-quality nonlinear image reconstruction results as input and a simulated phantom as output. As a result, a trained GCN model fed with lower resolution 3D ECT image enhances its quality and spatial resolution. Tomographic image quality and resolution enhancement was evaluated using normalized mean square error and Pearson correlation coefficient, which improved by 35.5% and 3.74%, respectively.

三维电容断层扫描 (3D ECT) 是一种用于诊断工业过程非导电组件的廉价工具。尽管相对成熟，但仍需要大量工作来提高其成像能力的逆性质。尤其是高分辨率 3D ECT 图像重建非常耗时且计算量大，而最著名的 3D ECT 图像重建技术已经达到其极限。因此，迫切需要改变现代计算智能解决方案的方向。因此，这项工作建议使用图卷积网络（GCN）来提高 3D ECT 图像质量。主要是利用 GCN 的能力，有效地使用隐藏在通常用于构建 3D ECT 图像的有限建模非结构化网格中的特定几何关系。这些关系首先由表示 ECT 体积有限元网格的图形编码。接下来在图到图框架中训练 GCN，其中成对的图表示高质量的非线性图像重建结果作为输入，模拟的体模作为输出。因此，以较低分辨率的 3D ECT 图像馈送的训练有素的 GCN 模型可提高其质量和空间分辨率。使用归一化均方误差和 Pearson 相关系数评估断层图像质量和分辨率增强，分别提高了 35.5% 和 3.74%。接下来在图到图框架中训练 GCN，其中成对的图表示高质量的非线性图像重建结果作为输入，模拟的体模作为输出。因此，以较低分辨率的 3D ECT 图像馈送的训练有素的 GCN 模型可提高其质量和空间分辨率。使用归一化均方误差和 Pearson 相关系数评估断层图像质量和分辨率增强，分别提高了 35.5% 和 3.74%。接下来在图到图框架中训练 GCN，其中成对的图表示高质量的非线性图像重建结果作为输入，模拟的体模作为输出。因此，以较低分辨率的 3D ECT 图像馈送的训练有素的 GCN 模型可提高其质量和空间分辨率。使用归一化均方误差和 Pearson 相关系数评估断层图像质量和分辨率增强，分别提高了 35.5% 和 3.74%。