Dual and multi energy X-ray transmission imaging (DE-/ME-XRT) are powerful tools to acquire quantitative material characteristics of diverse samples without destruction. As those X-ray imaging techniques are based on the projection onto the imaging plane, only two-dimensional data can be obtained. To acquire three-dimensional information and a complete examination on topology and spatial trends of materials, computed tomography (CT) can be used. In combination, these methods may offer a robust non-destructive testing technique for research and industrial applications. For example, the iron ore mining and processing industry requires the ratio of economic iron minerals to siliceous waste material for resource and reserve estimations, and for efficient sorting prior to beneficiation, to avoid equipment destruction due to highly abrasive quartz. While XRT provides information concerning the thickness, areal density and mass fraction of iron and the respective background material, CT may deliver size, distribution and orientation of internal structures. Our study shows that the data provided by XRT and CT is reliable and, together with data processing, can be successfully applied for distinguishing iron oxide rich parts from waste. Furthermore, heavy element bearing minerals such as baryte, uraninite, galena and monazite can be detected.

The NanoXCT EU FP7 project [1] aims at developing a laboratory, i.e. bench top sized X-ray nano-CT system with a large field-of-view (FOV) for non-destructive testing needs in the micro- and nano-technology sector. The targeted voxel size is 50 nm at 0.175 mm FOV, the maximum FOV is 1 mm at 285 nm voxel size. Within the project a suitable X-ray source, detector and manipulation system have been developed. The system concept [2] omits the use of X-ray optics, to be able to provide a large FOV of up to 1 mm and to preserve the flexibility of state-of-the-art micro-CT systems. The targeted resolution will be reached via direct geometric magnification made possible by the development of a specialized high-flux nano-focus transmission X-ray tube. The end-user's demand for elemental analysis will be covered by energy-resolved measurement techniques, in particular a K-edge imaging method. Timepix [3] modules were chosen as the basis for the detector system, since a photon counting detector is advantageous for the long exposure times that come with very small focal spot sizes. Additional advantages are the small pixel size and adjustable energy threshold. To fulfill the requirements on field-of-view, a detector width 0> 300 pixels was needed. The NanoXCT detector consists of four Hexa modules with 500 μm silicon sensors supplied by X-ray Imaging Europe. An adapter board was developed to connect all four modules to one Fitpix3 readout. The final detector has an active area of 3072 × 512 pixels or approximately 17 × 3 cm².In this contribution we present the development of the Timepix based NanoXCT detector, it's application in the NanoXCT project for CT and material specific measurements and the current status of results.

One of the primary difficulties in Computed Tomography (CT) is reconstructing cross-sectional images from measured projections of a physical object. There exist several classical methods for this task of generating a digital representation of the object, including Filtered Back Projection (FBP) or Simultaneous Algebraic Reconstruction Technique (SART). Our research aims to explore the potential of Quantum Computing in the field of industrial X-ray transmission tomography. Specifically, this work focuses on the application of a method similar to that proposed by Nau et al. (2023) on real CT data to demonstrate the feasibility of QUBO-based tomographic reconstruction. Starting with simulated phantoms, results with simulated annealing as well as real annealing hardware are shown, leading to the application on measured cone-beam CT data. The results demonstrate that tomographic reconstruction using quantum annealing is feasible for both simulated and real-world applications. Yet, current limitations - involving the maximum processable size and bit-depth of voxel values of the images, both correlated to the number of densely connected qubits within the annealing hardware - imply the need of future research to further improve the results. This approach, despite its early stage, has the potential to enable more sophisticated reconstructions, providing an alternative to traditional classical methods.

The growing amount of electronic waste is a global challenge: on one hand, it poses a threat to the environment as it may contain toxic or hazardous substances, on the other hand it is a valuable ‘urban mine’ containing metals like gold and copper. Thus, recycling of electronic waste is not only a measure to reduce environmental pollution but also economically reasonable as prices for raw materials are rising. Within electronic waste, printed circuit boards (PCBs) occupy a prominent position, as they contain most of the valuable material. One important step in the overall recycling process is the evaluation and the value estimation for further treatment of the waste PCBs (WPCBs). In this article, we introduce a method for value estimation of entire WPCBs based on component detection. The value of the WPCB is then predicted by the value of the detected components. This approach allows a flexible application to different situations. In the first step, we created a dataset and labelled the components of 104 WPCBs using different component classes. The component detection is performed on dual energy X-ray images by the deep neural object detection network ‘YOLO v5’. The dataset is split into a training, validation and test subset and standard performance measures as precision, recall and F₁-score of the component detection are evaluated. Representative samples from all component classes were selected and analysed for the valuable materials to provide the ground truth of the value estimation in the subsequent step.

X-ray transmission (XRT) and computed tomography (CT) was used on five samples from the Niaz porphyry Cu–Mo deposit in Iran, representing different alteration zones. Analysis of three-dimensional CT data revealed structural information and groups of elements with low, medium and high attenuation, which were assigned to minerals previously determined by scanning electron microscopy. Thus, the mineralization can be located, and the metal/waste ratio can be estimated, leading to more precise ore body modelling and process parameter determination. CT is useful for selected samples as it is time consuming. XRT can be used as real-time process on conveyor belts.