Scan quality estimation for industrial computed tomography using convolutional neural networks

Abstract

Artefacts in industrial Computed Tomography (CT) compromise the image quality of a CT scan and deteriorate evaluations such as inspections for material defects or dimensional measurements. Due to a large variety of scanning objects made of different materials and of various part sizes, artefacts appear in various manifestations in the reconstructed image. Existing analytical approaches allow quantifying the CT scan quality, but still a lack of generalizability exists. Thus, assessing the scan quality is complex and error-prone, as an inappropriate set of analytical quality metrics might be considered for a certain scan setup. In our work, a scan quality estimation based on a Convolutional Neural Network (CNN) is proposed. In order to train the network, projection images of various scans are used. The reconstructed scans are labeled in a pairwise comparison by an experienced user regarding their image quality. A scalar quality value is assigned to every projection image to assess the quality. The network is deployed to perform regression for the quality value. The network is trained on multiple objects that cover the range of objects which can be sufficiently acquired with the used CT scanner. In order to enrich the features from scans of different qualities, each object is captured with various scanning parameters. Our work showed a test accuracy of approximately 80 % on prior unseen data and of up to 95 % on trained objects. In order to comprehend the black box approach incorporated by the trained CNN, visualizations of feature maps are analyzed, as regions in the projection images relevant for the quality estimation are highlighted.