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.

Zusammenfassung: Die zerstörungsfreie Untersuchung komplexer Bauteile ist eine wichtige Prüfaufgabe in der industriellen Fertigung. Die dabei gestellten Anforderungen umfassen eine schnelle Verifikation der Maßhaltigkeit sowie die Analyse von Soll-Ist-Vergleichen. Für diese Erfordernisse ist die Computertomographie (CT) als zerstörungsfreie Prüfmethode bestens geeignet. Allerdings äußern sich physikalische Effekte wie Strahlaufhärtung und Streustrahlung in den rekonstruierten Schichten als Artefakte, die die Bildqualität verschlechtern und die Erfüllung der genannten Prüfaufgaben erschweren oder verhindern. Dieser Beitrag beschreibt, wie mit Hilfe der Iterativen Artefakt-Reduktion (IAR) die Qualität der Rekonstruktionen verbessert werden kann. Am Beispiel eines speziellen Referenzkörpers und eines Aluminiumtestkörpers werden ausgewählte Kenn- und Messgrößen ermittelt und die erzielten Genauigkeitssteigerungen quantifiziert.

ZusammenfassungMetallartefakte stellen eine große Herausforderung für das Messen mit Röntgen-Computertomographie dar. Dieser Beitrag stellt die Methode der multipositionalen Datenfusion zur Reduktion von Metallartefakten vor. Dazu werden mehrere Scans desselben Objekts bei unterschiedlicher Objekt-positionierung durchgeführt, aufeinander registriert und zur Fusion gemeinsam unter Betrachtung eines lokalen Gütemaßes rekonstruiert. In praxisnahen Experimenten wird der Mehrwert der Methode gezeigt. Insbesondere wird dargestellt, wie mit wenig Aufwand und ohne Vorwissen Kunststoffstrukturen trotz starker Metallartefakte sichtbar gemacht werden können, womit das Verfahren ein Alleinstellungsmerkmal gegenüber den existierenden Metallartefaktreduktionsverfahren aufweist.

Sparse-angle X-ray Computed Tomography (CT) plays a vital role in industrial quality control but leads to an inherent trade-off between scan time and reconstruction quality. Adaptive angle selection strategies try to improve upon this based on the idea that the geometry of the object under investigation leads to an uneven distribution of the information content over the projection angles. Deep Reinforcement Learning (DRL) has emerged as an effective approach for adaptive angle selection in X-ray CT. While previous studies focused on optimizing generic image quality measures using a fixed number of angles, our work extends them by considering a specific downstream task, namely image-based defect detection, and introducing flexibility in the number of angles used. By leveraging prior knowledge about typical defect characteristics, our task-adaptive angle selection method, adaptable in terms of angle count, enables easy detection of defects in the reconstructed images.

Quantum computing is an emerging field of technology that utilizes unique properties such as the superposition principle and quantum entanglement, potentially enabling the development of techniques that are drastically faster or even the only feasible solutions compared to classical approaches. Thus, it promises enormous gains in several domains, including the processing of n-dimensional images, such as those produced by X-ray computed tomography. However, the latter is subject to physical effects that primarily include artifacts and quantum noise. Noise, in particular, is an unavoidable issue that needs to be addressed when processing X-ray data. Therefore, this work considers the denoising of 3D volumetric data using quantum computing. An adaptation of a mathematical denoising model was transformed to be suitable for quantum implementation, and preliminary experiments demonstrated its functionality with two-dimensional images. Here, we first lift this model to be applicable to 3D images. Next, we provide an implementation in form of quantum circuits and consider issues of a practical implementation occurring specifically in 3D. The final implementation is executed on a real X-ray computed tomography dataset, showing that proper denoising can be performed on quantum devices, yet current technological limitations inhibit the application to large datasets.

For the long-term goal of determining the measurement uncertainty of dimensional computed tomography (CT) measurements with simulation, it is necessary to prove the compatibility of the digital model with its real counterpart. A study of repeated CT measurements of a calibrated aluminum specimen was performed and afterwards simulated. A first indicator for a sufficiently good digital representation is the agreement of dimensional measurands, such as diameters and center-to-center distances. Results show in general good agreement between simulation and real measurement. A detailed discussion of the results as well as a proposed criterion for testing the simulation will be shown in the full paper.