Quantifying the effect of X-ray scattering for data generation in real-time defect detection.

Background: X-ray imaging is widely used for the non-destructive detection of defects in industrial products on a conveyor belt. In-line detection requires highly accurate, robust, and fast algorithms. Deep Convolutional Neural Networks (DCNNs) satisfy these requirements when a large amount of labeled data is available.

CT-based data generation for foreign object detection on a single X-ray projection.

Although X-ray imaging is used routinely in industry for high-throughput product quality control, its capability to detect internal defects has strong limitations. The main challenge stems from the superposition of multiple object features within a single X-ray view. Deep Convolutional neural networks can be trained by annotated datasets of X-ray images to detect foreign objects in real-time.

Quantitative Comparison of Deep Learning-Based Image Reconstruction Methods for Low-Dose and Sparse-Angle CT Applications.

The reconstruction of computed tomography (CT) images is an active area of research. Following the rise of deep learning methods, many data-driven models have been proposed in recent years. In this work, we present the results of a that we organized, bringing together algorithm experts from different institutes to jointly work on quantitative evaluation of several data-driven methods on two large, public datasets during a ten day sprint.

Unsupervised Foreign Object Detection Based on Dual-Energy Absorptiometry in the Food Industry.

X-ray imaging is a widely used technique for non-destructive inspection of agricultural food products. One application of X-ray imaging is the autonomous, in-line detection of foreign objects in food samples. Examples of such inclusions are bone fragments in meat products, plastic and metal debris in fish, and fruit infestations.

Registration-based multi-orientation tomography.

We propose a combination of an experimental approach and a reconstruction technique that leads to reduction of artefacts in X-ray computer tomography of strongly attenuating objects. Through fully automatic data alignment, data generated in multiple experiments with varying object orientations are combined. Simulations and experiments show that the solutions computed using algebraic methods based on multiple acquisitions can achieve a dramatic improvement in the reconstruction quality, even when each acquisition generates a reduced number of projections.