Study on Quality Assessment Methods for Enhanced Resolution Graph-Based Reconstructed Images in 3D Capacitance Tomography

This paper proposes a novel approach to assessing the quality of 3D Electrical Capacitance Tomography (ECT) images. Such images are typically represented as irregular graphs. Thus, image quality metrics typically used with raster images do not straightforwardly apply to them. However, given the recent advancements in Graph Convolutional Neural Networks (GCNs) for improving ECT image reconstruction, reliable Quality Assessment methods are essential for comparing the performance of different GCN models. To address this need, this paper applied some existing image quality and similarity assessment methods designed for raster images to the graph-based representation of 3D ECT images. Specifically, attention was paid to the Peak Signal-to-Noise Ratio (PSNR), the Structural Similarity Index Measure (SSIM), and measures based on image histograms. The proposed adaptations resulted in the development of tailored Graph Quality Assessment (GQA) techniques specifically designed for the graph-based nature of ECT images. The proposed GQA techniques were validated on 1042 phantoms and their corresponding Low-Quality (LQ) and High-Quality (HQ) reconstructions through a robust GQA benchmarking system, enabling a systematic comparison of various GQA methods. The evaluation of the proposed methods’ performances across this diverse dataset, by analyzing overall trends and specific case studies, is presented and discussed. Finally, we present our conclusions regarding the effectiveness of the proposed GQA methods, and we identify the most promising approach for assessing the quality of graph-based ECT images.