bluuebunny/crossref_metadata_2025 huggingface dataset:
Non-destructive wear analysis of aged polyethylene hip inserts: Comparison of gravi-metric investigations with three-dimensional microstructure analysis in micro-CT after 10 million cycles
Authors:   Upper Austrian University of Applied Sciences (FH OÖ), Trieb Klemens ,   IMA Materialforschung und Anwendungstechnik GmbH, Langner Marcel ,   IMA Materialforschung und Anwendungstechnik GmbH, Gatzk Frank ,   Upper Austrian University of Applied Sciences (FH OÖ), Kastner Johann ,   Upper Austrian University of Applied Sciences (FH OÖ), Senck Sascha
Published: February 2023

The hip joint is the most frequently implanted artificial joint in humans. The most important parameter is the durability, i.e. the time until a replacement operation. The most common reason for material failure is mechanical abrasion/wear as a limiting factor and significantly determines the survival time of the implant. [1, 2]. In the case of the acetabular cup, the plastic insert is often the most affected. The hip model examined here is characterized by a metal sleeve into which the plastic insert is pressed. This assumes an optimal fit of the insert in the cup in order to create conditions for the lowest possible abrasion. Unfortunately, it is not yet possible to test the abrasion of this system non-destructively. The abrasion of the hip is tested on so-called simulators by biomechanical loading according to ISO standard 14 242 over 5 million cycles [3, 4]. Additional scenarios with increased loading of the cup are required, such as artificial aging of the plastic as a "worst-case" scenario. A non-destructive analysis of the polyethylene has not been possible so far; up to now, the plastic has been knocked out of the metal sleeve with a hammer blow (with force) under strong deformation (Fig.1: destroyed back side by mechanical deformation on the left, plastic insert on the right). This study is the first to enable non-destructive testing of the plastic after aging and biomechanical stress by X-ray microcomputed tomography (XCT) examination. This will be compared to conventional gravimetric examinations and surface scanning results and tested for routine use. The second goal is to test wear abrasion not after 5 million cycles but after 10 million cycles.
Matching of CAD model projections and X-ray projection images for shape inspection of metal assemblies
Authors:   The University of Tokyo, Tan Yingqi ,   The University of Tokyo, Ohtake Yutaka ,   The University of Tokyo, Yatagawa Tatsuya ,   The University of Tokyo, Suzuki Hiromasa
Published: February 2023

X-ray computed tomography (CT) facilitates non-destructive inspection of the interior of mechanical assemblies. However, industrial materials often contains heavy metals; which causes in CT artifacts in CT images and prevents accurate segmentation or shape measurement. To overcome this issue, in this study, we proposed a new inspection method that uses X-ray projection images and CAD meshes. We extracted characteristic features as edges and curved surfaces as feature images and used those for component alignments and shape measurements. The idea behind this is that these characteristic 3D features are captured as crease pixels in 2D projection images; thus, they can be extracted and compared with CAD meshes directly without conducting actual CT reconstructions. Furthermore, we conducted preliminary tests with some metal parts, which indicated that our approach can effectively inspect multi-material mechanical assemblies.
X-ray microtomographic studies of the shape change of rechargeable Zn battery anodes
Authors:   Politecnico di Milano, Bozzini Benedetto ,   University of Salento, Rossi Francesca ,   Elettra-Sincrotrone Trieste, Sodini Nicola ,   Politecnico di Milano, Caielli Tommaso ,   Elettra-Sincrotrone Trieste, Mancini Lucia
Published: February 2023

Although disposable Zn-based batteries have been developed into widespread commercial devices decades ago, successful applications of rechargeable Zn batteries are still hindered by various technical drawbacks, a crucial one being limited cycle-life, due to uncontrolled morphology changes of the anode upon applying discharge/charge sequences. In this work, we report a systematic post mortem and in situ X-ray computed microtomography-based investigation of Zn electrode cycling for traditional and innovative electrolyte chemistries and electrode configurations, at current densities and depths of discharge of practical interest. 3D imaging of intact cells revealed unprecedented morphochemical details of anode shape change that could be explicitly modelled and systematically correlated with operating conditions.
Clothing Items Classification Based on X-Ray Multi-Shot Imaging for E-Commerce
Authors:   Deggendorf Institute of Technology, Wittl Simon ,   Fraunhofer Anwendungszentrum CT in der Messtechnik, Herl Gabriel ,   Fraunhofer Anwendungszentrum CT in der Messtechnik, Miller Thomas ,   Deggendorf Institute of Technology, Hiller Jochen
Published: February 2023

The mail order business with e-commerce products such as clothing is growing steadily. This trend is associated with an increased effort in package processing of incoming and outgoing goods. These processes can be automated with X-ray technology. Therefore, we describe a method for classifying the contents of e-commerce packages using multiple X-ray projections. A selection of clothing items is classified using a Convolutional and Recurrent Neural Network model (CNN-RNN) trained on an input of multiple X-ray projections. The described workflow allows a fast generation of synthetic training data and is evaluated on real (non-synthetic) data.
Workflows for assessing lithium-ion battery cells with computed tomography and high-resolution 3D X-ray microscopes
Authors:   Carl Zeiss Industrial Metrology, Villarraga-Gómez Herminso ,   Carl Zeiss X-ray Microscopy, Norouzi Rad Mansoureh ,   Carl Zeiss X-ray Microscopy, White Robin T. ,   Carl Zeiss X-ray Microscopy, Kelly Stephen T. ,   Carl Zeiss (Shanghai), Mo Kai
Published: February 2023

In the last three decades, significant progress has been made on lithium-based battery technologies. However, lithium-ion battery cell manufacturers still face quality and process control challenges when trying to non-destructively map the battery electrodes’ microstructure, its heterogeneities, and their effect on battery aging and performance. This presentation introduces workflows that combine high-resolution X-ray microscopy and computed tomography to generate detailed three-dimensional visualization of the inside of lithium-ion batteries, without destroying them, to enable the study of their internal structure before and after charging/discharging cycles. These workflows can speed up development time, increase cost-effectiveness, and simplify failure analysis and quality inspection of lithium-ion batteries and other cells built with new emerging energy materials.
A Demonstrator for Threat Detection in Volumetric CT Scans
Authors:   University of Passau, Fischer Ruben ,   Fraunhofer Institute for Integrated Circuits (IIS), Hufnagel Eva ,   University of Passau, Lang Thomas ,   Fraunhofer Institute for Integrated Circuits (IIS), Claußen Joelle ,   Fraunhofer Institute for Integrated Circuits (IIS), Gerth Stefan ,   University of Passau, Sauer Tomas
Published: February 2023

The detection of threat items and prohibited items inside closed containers is an active field of research and an even more important real-world application. While conventional systems use X-ray projections for inspection, they lack true three-dimensional information which is crucial for a reliable detection. In this work, we introduce a demonstrator setup for three-dimensional threat detection within a 3D printed miniature container. We created a data processing pipeline that automatically integrates CT scanning, volumetric reconstruction and the actual detection of threat objects by means of a neural network. The achieved results are very promising and could be obtained using very few annotated training samples which is a realistic assumption in this application.
NDTFlix: Collaborative Remote Analysis of X-ray Computed Tomography Datasets
Authors:   Upper Austrian University of Applied Sciences (FH OÖ), Fröhler Bernhard ,   Upper Austrian University of Applied Sciences (FH OÖ), Weinberger Patrick ,   Upper Austrian University of Applied Sciences (FH OÖ), Gall Alexander ,   Upper Austrian University of Applied Sciences (FH OÖ), Heim Anja ,   Upper Austrian University of Applied Sciences (FH OÖ), List Domink ,   Upper Austrian University of Applied Sciences (FH OÖ), Schwajda Daniel ,   Upper Austrian University of Applied Sciences (FH OÖ), Anthes Christoph ,   Upper Austrian University of Applied Sciences (FH OÖ), Kumar Ankit ,   Upper Austrian University of Applied Sciences (FH OÖ), Kastner Johann ,   University of Passau, Heinzl Christoph
Published: February 2023

When analysing data collected from non-destructive testing, several areas of expertise are required, such as on imaging modalities, data analysis and visualisation, or the domain background in terms of the inspected material. Typically, this requires a team of experts, who are not necessarily situated at the same working place. We therefore propose a system supporting collaborative remote analysis facilitating easy access to a common workspace. The system supports a collaborative analysis of datasets by connecting interactions in a lightweight web-based viewer with those in an advanced analysis software package on a desktop PC. Our system supports both co-located and distributed collaboration on large complex datasets and allows for a simple spontaneous cross-device access. This allows a wide range of devices and immersive systems to be supported regardless of their capabilities. Viewpoints and annotations can be shared as visual cues for the analysis of collaborators. The proposed system will be presented using a case study of analysing an adaptive manufactured aeronautics component. The preliminary findings showed positive feedback of the experts regarding the creation of a flexible and fast collaborative analysis set-up.
Best projections selection algorithm based on constrained QDEIM for sparse-views X-ray Computed Tomography
Authors:   Commissariat Energie Atomique (CEA), Bussy Victor ,   Commissariat Energie Atomique (CEA), Vienne Caroline ,   Commissariat Energie Atomique (CEA), Escoda Julie ,   National Institute of Applied Science ( INSA-Lyon)- University of Lyon, Kaftandjian Valérie
Published: February 2023

X-ray Computed Tomography (CT) is a non-destructive testing tool increasingly used by manufacturers, with growing interest in in-line testing applications. However, it is still struggling to establish itself as a standard technique due to long acquisition times. In order to meet industrial imperatives, recent research aims at reducing this acquisition time by limiting the number of views while keeping a good CT image quality. The rise of iterative reconstruction methods has made it possible to partially solve the Sparse-View CT problem thanks to the injection of regularisation terms. Furthermore, these methods also allow more freedom in the acquisition trajectories. In this work, we propose a method for reducing the number of projections without impacting the reconstruction quality by acquiring only the most relevant views. Indeed, not all views provide the same amount of information. We, therefore, present a technique for selecting views when the geometry of the inspected object is roughly known a priori. Our method is based on the Q-Discrete Empirical Interpolation Method (QDEIM) and considers the attenuation of the rays as an additional constraint.
Single Shot X-ray Speckle Tracking Phase Contrast Imaging with a Low Brilliance Lab Source
Authors:   University of Stuttgart, Gänz Peter ,   University of Stuttgart, Kieß Steffen ,   University of Stuttgart, Simon Sven
Published: February 2023

In this paper, we present a single-shot X-ray speckle tracking (XST) phase-contrast imaging (PCI) method for a non-coherent polychromatic laboratory source. XST typically requires a coherent X-ray source, such as a synchrotron, to produce speckle. For a laboratory source without sufficient coherence, the absorption pattern of a random mask, such as sandpaper, have been proposed. However, to track the very small phase shifts in the absorption speckle below the size of the pixels of a standard flat panel detector in the range of 100μm, hundreds of detector images are taken, each moved by a linear stage by a fraction of a pixel to determine the phase shift. As a result, both acquisition time and radiation dose are high. Moreover, precise linear stages are required for sub-pixel movement of the random mask during acquisition of the projection images. In contrast, in this paper we propose a neural network called PCINet to acquire the differential phase image with only one reference image without the sample and one sample image. The proposed system does not require precise linear stages because only a single acquisition is needed. Due to the incoherent source, an absorption-based pseudo-speckle is generated as a reference image. In order to increase the contrast of the pseudo-speckle, a novel, cheap and easy to manufacture tungsten random spatial distrubution of particles is proposed as an alternative to sandpaper. A method for generating realistic training data for the neural network using real X-ray images in a laboratory setup was developed. The training data of the neural network was generated as close as possible to the real data by constructing the input image pairs pixel by pixel from multiple real X-ray images of the pseudo-speckle. The experimental resukts show that it is possible to obtain the differential phase image with only one reference image and one sample image with superimposed absorption speckles in the case of an incoherent laboratory setup.
Application of multispectral computed tomography for the characterisation of natural graphite
Authors:   RWTH Aachen University, Krebbers Leonard T. ,   RWTH Aachen University, Grozmani Natalia ,   RWTH Aachen University, Lottermoser Bernd G. ,   Fraunhofer IPT, Schmitt Robert H.
Published: February 2023

The established mineralogical and chemical analysis methods for graphite ores require complex sample preparation. Moreover, they do not produce 3D images of the polymineralic rock structure and texture. The lack of 3D information can lead to an erroneous assessment of the quality-determining properties of graphite ores, such as graphite flake size (grain size), graphite flake size distribution, and impurities. Therefore, there is a need to obtain 3D information from graphite ores and concentrates. Industrial computed tomography (CT or single-spectrum CT) is a unique technique that enables the examination of rocks in the 3D regime. The challenge in CT analysis of ores is the different absorption properties of mineral phases present. Low X-ray tube generator voltages lead to pronounced artefacts (volume defects) if highly absorbing gangue phases (e.g. iron sulfides) are present. Therefore, high X-ray tube voltages must be applied. However, graphite and associated gangue minerals, as well as contained cracks and voids in the ore, can exhibit similar absorption properties at higher generator energies (above 120 kV). Multispectral CT (MSP-CT) measurements offer a minimisation of CT artefacts and an increase in grey value contrasts. This work investigates the application of the MSP-CT measurements, significantly improving mineral differentiation.
Automated Trajectory Generation for Region of Interest Computed Tomography using Industrial Robots
Authors:   VisiConsult X-ray Systems & Solutions GmbH, Bliedtner Katharina ,   VisiConsult X-ray Systems & Solutions GmbH, Dedyaeva Polina ,   VisiConsult X-ray Systems & Solutions GmbH, Krohne Kristen ,   VisiConsult X-ray Systems & Solutions GmbH, Herold Frank
Published: February 2023

Computed Tomography (CT) is widely used as reference non-destructive testing method (NDT method) to analyze the inner structure of safety-critical parts e.g., in automotive or aerospace industry. Especially additive manufactured (AM) components often with their bionic design will lead to very complex structured parts, which are light weighted and thus will reduce the CO2 emission of cars or airplanes respectively. For metallic AM components produced in a powder bed fusion process pores, inclusions or lack-of-fusion errors (LoF) may occur typically. The latter one arises if powder could not be melted properly, i.e., it is an inclusion of loose powder inside the part. Such LoF are very low in contrast compared to the surrounding solid material and therefore harder to detect the smaller they are and the thicker the penetrated material is. Monitoring systems integrated in the 3D-Printer help to detect indications of where a defect could have occurred. Exactly on each of these predefined positions the CT-System will do a region-of-interest scan (ROI scan). In this study we are using an industrial robot as manipulator of the component. Its dedicated trajectory using only the optimal projection directions of the current ROI is derived by using our newly developed Digital Twin containing the robot, the AM component and the X-Ray imaging chain. We will show the integration of an automated trajectory planning into a robotic CT system for ROI scans of complex AM parts.
Evaluation of XCT image quality
Authors:   Laboratoire National de Métrologie et d'Essais (LNE), Obaton Anne-Françoise ,   Centre Technique des Industries de la Fonderie (CTIF), Gaillard Yves ,   Centre Technique des Industries de la Fonderie (CTIF), Bouvet Patrick ,   Novitom, Guiraud Olivier ,   Nikon, Genot Sylvain ,   SAFRAN Composites, Gay Lionel
Published: February 2023

The direct visual quality of an X-ray computed tomography (XCT) image is very subjective. Metrics to quantify the image quality have been defined for X-ray radiography, but some of these no longer seem appropriate for XCT images. This has been highlighted through an XCT interlaboratory comparison campaign, performed in the XCT working group of the French Confederation for Non-destructive Testing (COFREND), on an additively manufactured part at medium-energy XCT (230-520 kV). The calculation of several of these metrics has been performed on the images acquired with nine different XCT systems to demonstrate their representativeness and compared to visual inspections. This comparison campaign confirms that several of them are no longer representative of the image quality.
X-ray Computed Tomography for Nuclear Power Plant Maintenance
Authors:   diondo GmbH, Brierley Nicholas ,   University of Ontario Institute of Technology (UOIT), Gaber Hossam ,   diondo GmbH, Salzinger Mario ,   diondo GmbH, Chrzan Karin ,   New Vision Systems Canada Inc.,, Barakat Amr ,   Deggendorf Institute of Technology, Herl Gabriel
Published: February 2023

As part of the scheduled maintenance of nuclear power plants, specialist tools are deployed into the reactor core, for example to inspect the moderator. It is imperative that these tools operate correctly, and that no element of the tool remains in the reactor when the reactor resumes operation. The current processes for ensuring this are hugely labour intensive, and hence costly, involving a full teardown before and after deployment. This paper describes the development of a novel X-ray Computed Tomography (CT) system and workflow for ensuring the integrity of specialist reactor tools without the need for disassembly. The system hardware must be able to deal with the challenge of tools that are up to 6 metres in length and contain a significant amount of dense componentry. On the other hand, the system software must be able to confirm the correct and comprehensive assembly of the tool based on the obtained CT scan, and despite numerous potential, but benign, differences in the tool appearance. The presented approach overcomes both challenges: the hardware uses a gantry design with a high-powered X-ray source (see Fig. 1), the software employs a machine learning implementation.
Interactive Denoising of 3D Volumes Using Wavelets
Authors:   Fraunhofer-Institut für Integrierte Schaltungen IIS, Lang Thomas ,   University of Passau, Stock Andreas  Michael
Published: February 2023

Conventional signal denoising methods either focus on statistical properties or employ a (complicated) set of parameters a user needs to tweak appropriately. More recently, AI-based denoising methods are increasingly employed due to their robustness. However, these methods typically require a large number of training datasets, which are costly to produce in a computed tomography scenario. This work introduces a denoising procedure which combines classifier-based decision making with interactive user input in the wavelet domain. These interactive user inputs allows for an easy and straight-forward specification of noisy regions, which works especially well in the wavelet basis, without the need for statistical properties or large numbers of parameters. At the same time, the obtained results show that noise in the scanned data is reduced considerably while keeping the structural similarity intact.
Automated 3D Defect Detectionm based on Simulated Reference
Authors:   Fraunhofer Institute for Integrated Circuits (IIS), Sukowski Frank ,   Fraunhofer Institute for Integrated Circuits (IIS), Suth Daniel ,   Fraunhofer Institute for Integrated Circuits (IIS), Waldyra Adrian ,   Fraunhofer Institute for Integrated Circuits (IIS), Grulich Tobias ,   Fraunhofer Institute for Integrated Circuits (IIS), Jung Alexander
Published: February 2023

Bringing the Computed Tomography (CT) technology into production lines brings many advantages due to the complete and detailed 3D characterization and localization of defects. But compared to laboratory systems, Inline-CT systems must meet several challenges, the most significant one is certainly the limited available time for the measurement procedure and data evaluation. Visual inspection of 3D data sets of complex parts within the production rate is almost impossible, so automated image evaluation algorithms are mandatory for that task. Fast measurement procedures often mean short integration times and a lower number of projections which are two factors that increase quantum noise and thus decrease image quality significantly. In addition to that, scatter artifacts also have a negative influence on the image quality. All these conditions constitute challenges to the image evaluation algorithm. The Fraunhofer EZRT has developed a method for automated defect detection in cast parts by using reference (that means flawless) parts for comparison. The advantage of this method is that image artifacts are considered, since they appear in both data sets in the same way and have almost no negative influence on the reliability of the evaluation result. Most recent work took this method one step further by using not real flawless parts, but instead using simulations to generate reference data sets in a very convenient and quick way for any number of different parts.
A study of off-focal radiation in transmission geometry x-ray sources
Authors:   The Australian National University, Steklova Klara ,   Queensland University of Technology, Fielding Andrew ,   The Australian National University, Beeching Levi ,   The Australian National University, Sheppard Adrian P. ,   The Australian National University, Kingston Andrew M.
Published: February 2023

Off-focal x rays are an undesirable feature in industrial and medical x-ray computed-tomography scanners that contributes the effects of a large defocused source. They lead to artefacts in tomographic volumes that degrade contrast and resolution. A simple model of an x-ray tube with a transmission target has been developed in Topas, a Monte Carlo (MC) simulation extension to Geant4, to improve the understanding of the origin of off-focal x rays. From the analysis of the MC simulations we are able to identify the origin of various types of off-focal x rays and characterise their spatial distribution and energy spectra. We can then propose and test different mitigation methods, based both on hardware additions as well as improvements in the image reconstruction or data postprocessing.
CT scan trajectory calibration based on projected metal spheres: When and how should errors from elliptical distortion be corrected?
Authors:   University of Erlangen-Nürnberg (FAU), Butzhammer Lorenz ,   University of Erlangen-Nürnberg (FAU), Braun Matthias Robert Oskar ,   University of Erlangen-Nürnberg (FAU), Hausotte Tino
Published: February 2023

The majority of geometry calibration procedures for industrial X-ray Computed Tomography (CT) are based on determining projected sphere centres in X-ray projections. The centres are typically derived from the elliptical contours that arise for cone beam setups. However, the ellipse centre is not identical with the projected sphere centre. A correction method (also known as Deng correction) exists, but is only used in some instances in literature. To this extent, it is an open question for which cases the correction leads to a significant improvement. For specific cases, it can even lead to larger errors. In this contribution, we therefore analyse the parameter range for which the correction is suitable. This is done by means of a simulation parameter study. Additionally, we propose an adapted correction formula that can be used for 3D scan trajectory calibration where the source-detector-distance is known. It is further proposed to keep the ellipse orientation in the ellipse fitting routine fixed. The results from simulation are selectively compared with experimental findings. Overall, the adapted correction leads to superior results compared with the original Deng correction. Whether a correction leads to an improvement at all strongly depends on the ratio between the axial source-object-distance and the sphere radius and on the position of the projected sphere centre on the detector. A theoretically derived decision criterion was largely confirmed. The presented results therefore can give a guidance for potential users who want to decide whether a correction is advantageous for a given sphere radius and CT system geometry.
Automated defect recognition in X-ray projections using neural networks trained on simulated and real-world data
Authors:   Fraunhofer Institute for Integrated Circuits (IIS), Schön Tobias ,   Fraunhofer Institute for Integrated Circuits (IIS), Gosswami Bishwajit Mohan ,   Fraunhofer Institute for Integrated Circuits (IIS), Hvingelby Rasmus ,   Fraunhofer Institute for Integrated Circuits (IIS), Suth Daniel ,   Fraunhofer Institute for Integrated Circuits (IIS), Kemeter Lukas Malte ,   Fraunhofer Institute for Integrated Circuits (IIS), Sierak Paulina
Published: February 2023

In this contribution, we investigate a methodology based on neural networks for efficient learning of light metal castings for defect detection in X-ray imaging. The motivation comes from the high effort in time and costs which is currently required to set up new objects or parts. To overcome this drawback, on the one hand, we want to reduce the complexity for the user by applying neural networks for defect detection. On the other hand, we try to use as much data from the simulation as possible instead of real data which is costly to collect. The performance of the investigated approaches is evaluated using real-world data from wheel inspection. We show that training on simulated data only is inferior to training with costly real world data. Combining both types results in the best performance and the closer the simulated data matches the real-world, the better the performance.
X-Ray Scatter Correction by Deep Semi-supervised Learning of Simulated Projections with Beam-hole Array
Authors:   The University of Tokyo, Hattori Haruki ,   The University of Tokyo, Yatagawa Tatsuya ,   The University of Tokyo, Ohtake Yutaka ,   The University of Tokyo, Suzuki Hiromasa
Published: February 2023

In nondestructive testing and evaluation using cone-beam X-ray computed tomography (CBCT), X-ray scattering is a major cause of degrading the quality of CT images. To correct the negative effects of scattering, recent studies focused on the deep convolutional neural network (DCNN) for fast and accurate correction, and they trained the DCNNs using training data obtained by X-ray simulation. However, such approaches have a drawback where accurately simulating the physical behavior of Xrays requires considerable computational time. Moreover, it is unclear whether the simulated training data are effective to real measured data. To address these problems, we propose a new scatter correction method based on deep semi-supervised learning that can be applied to real measurements. Our training data are incomplete transmission images in which primary X-ray intensities are obtained for a part of pixels. We introduce a data augmentation method based on X-ray radiology, which allows us to significantly reduce training data in our semi-supervised learning. Through experiments using simulated data, this paper shows that our semi-supervised method using a few training data works as effectively as a baseline fully supervised method.
Validation of an easy-to-use beam-hardening measurement method
Authors:   Physikalisch-Technische Bundesanstalt (PTB), Borges de Oliveira Fabrício ,   Physikalisch-Technische Bundesanstalt (PTB), Illemann Jens
Published: February 2023

To improve the quality of the images, and therewith possibly measurement results of cone-beam CT, a sequence of corrections must be applied. Examples of such corrections are bad-pixel elimination, flat-field (shading) correction, image rectification, deconvolution of the detector’s PSF, scatter elimination, and beam-hardening correction. The latter can be described by a lookup table for the relation of measured grey value and radiographed material thickness. In this work, a test device with a nearly cone-like-shaped attenuator is presented for the measurement of the beam hardening curve in a straightforward way. Its usability is experimentally investigated and compared with ab-initio image simulations. The results show the strong dependence of the beam hardening curves on the detectors structure, that can be used to verify the model assumptions — especially the scintillator’s thickness and composition.
Mobile CT and cloud service makes CT inspection fast and affordable
Authors:   Microvista GmbH, Schlosser Benjamin ,   Microvista GmbH, Höhne Robin ,   Microvista GmbH, Weinreich Markus ,   Microvista GmbH, Hagner Lutz
Published: February 2023

Industrial processes should be capable and controlled. In industrial mass production, however, undesirable deviations regularly occur which must not affect the quality of the finished product. In such cases, a temporarily increased inspection effort can also be justified using industrial CT. Under these circumstances, the test equipment must be available quickly and at a reasonable cost. To achieve this aim, a mobile CT was built and supplemented by a flexible, automatically working evaluation software that was adapted to the specific test task at short notice. The software system is cloud-based and is characterized by very high availability and scalable performance. In combination with a CT available on short call directly on the production line, this solution can quickly check the part quality and provide important feedback for process optimization.
Automation of Non-Destructive Evaluation of Casting Parts based on Computed Tomography and Machine Learning
Authors:   National Institute of Applied Science ( INSA-Lyon)- University of Lyon, Dakak Abdel Rahman ,   Centre Technique des Industries de la Fonderie (CTIF), Bouvet Patrick ,   Novitom, Gueye Limamou ,   Novitom, Duy Nguyen ,   Novitom, Autret Awen ,   Novitom, Fayard Barbara ,   National Institute of Applied Science ( INSA-Lyon)- University of Lyon, Kaftandjian Valérie
Published: February 2023

The development of digital tools exploiting computed tomography (CT) data intelligently, in their entirety and in a simple manner is a major challenge for the full deployment of this technology in industrial production control environment. This work aims at developing a pipeline able to detect material discontinuities in metallic parts, in an automated way without human analysis, to classify them according to their nature and to apply criteria resulting from specifications to rule on the acceptance or not of the parts. It exploits the last advances in artificial intelligence data treatment, and it is based on a dedicated processing and visualisation platform that optimizes the handling of large volumes of data.
Thickness-Driven Sheet Metal Segmentation of CT-Scanned Body-in-White
Authors:   The University of Tokyo, Ohtake Yutaka ,   Hitotsubashi University, Yatagawa Tatsuya ,   The University of Tokyo, Suzuki Hiromawa ,   G-TEKT, Kubo Satoru ,   G-TEKT, Suzuki Sou
Published: February 2023

Large-scale and high-energy X-ray computed tomography (CT) scanners, such as XXL-CT, can fully digitalize car-sized industrial assemblies. For utilizing the data collected using these scanners in digital engineering applications, a part segmentation method is needed. Hence, we propose a segmentation method for a body-in-white primarily composed of sheet metal. The proposed sheet metal segmentation is driven by the sheet thickness, which is estimated from the CT value profile through the sheet structure. The thickness information is assigned to a triangle mesh representing the medial surface of the sheet metal. Then, a simple region-growing algorithm is used to cluster the mesh triangles with comparable sheet thicknesses. Lastly, the resulting clusters are merged manually to obtain the final part segmentation.
Ring Artifacts Removal & Noise Reduction in X-Ray Computed Tomography Using Deep Learning
Authors:   Novitom, Nguyen Duy ,   Novitom, Autret Awen ,   Novitom, Fayard Barbara ,   Novitom, Terzi Sofiane ,   Novitom, Bravais Guillaume
Published: February 2023

Analyzing data obtained from X-ray CT acquisitions may be challenging due to the presence of image artifacts. In recent years, deep learning has proven to be one of the fastest growing technologies in computer science and has become a confirmed trend in X-ray CT. In this study, we propose using deep learning-based algorithm to reduce noise and ring artifacts in CT images.
Computed tomography with or without radiation
Authors:   Fraunhofer Institute for Integrated Circuits (IIS), Makarov Jurij ,   Fraunhofer Institute for Integrated Circuits (IIS), Basting Melanie ,   Fraunhofer Institute for Integrated Circuits (IIS), Schielein Richard ,   Fraunhofer Institute for Integrated Circuits (IIS), Schmitt Michael ,   Fraunhofer Institute for Integrated Circuits (IIS), Kabitzky Dominik ,   Fraunhofer Institute for Integrated Circuits (IIS), Sukowski Frank ,   Fraunhofer Institute for Integrated Circuits (IIS), Voland-Salamon Virginia
Published: February 2023

Machine hours of CT systems are limited. They are even shortened by setup and configuration efforts, training of unexperienced staff or maintenance of high performance components. The parameter choice for high quality scans, on the other hand, requires practical experience. It was shown that X-ray simulation is suitable for the optimization of trajectories and for the determination of the expected measurement uncertainty. Our objective is hence to maximize the available machine hours by creating a digital twin of the CT system. For that purpose, we designed a smart architectonical concept for the control of both, the real CT system and the digital twin. The approach is designed to simplify and accelerate the workflow during CT application.
Multiscale Phase-Contrast Tomography at BM18
Authors:   Fraunhofer Institut für Integrierte Schaltungen IIS/NCTS, Lang Thomas ,   Fraunhofer Institut für Integrierte Schaltungen IIS/NCTS, Saeidnezhad Nazila ,   Fraunhofer Institut für Integrierte Schaltungen IIS/NCTS, Dremel Kilian ,   Fraunhofer Institut für Integrierte Schaltungen IIS/NCTS, Weller Daniel ,   Julius-Maximilians-University Würzburg, Diez Matthias ,   University of Passau, Stock Andreas  Michael ,   Fraunhofer Institut für Integrierte Schaltungen IIS/NCTS, Sauer Tomas ,   European Synchrotron Radiation Facility, Cianciosi Filippo ,   European Synchrotron Radiation Facility, Jarnias Christophe ,   European Synchrotron Radiation Facility, Tafforeau Paul ,   Fraunhofer Institut für Integrierte Schaltungen IIS/NCTS, Zabler Simon
Published: February 2023

Synchrotron phase-contrast micro-tomography is considered the gold standard in non-destructive volumetric imaging of millimeter and centimeter-sized objects. Micro-tomography beamlines are operating at many synchrotron radiation facilities, including the Extremely Brilliant Source (EBS) at the European Synchrotron Radiation Facility (ESRF). Applications favor objects which cannot be sufficiently resolved by state-of-the-art industrial micro-CT scanners. Synchrotron tomography features much superior time resolution, e.g., for in situ micro-CT, as well as superior photon flux, thus making this method suitable for Region of Interest scanning (i.e., multiresolution tomography). This report presents first results from the EBS' latest industrial beamline BM18, which was built for hierarchical propagation phase-contrast tomography. BM18 makes use of the exceptionally high coherence of the new ESRF-EBS lattice. Thanks to the wide polychromatic high-energy beam, one can scan and zoom into larger parts, e.g., from additive manufacturing or battery packs. In particular, BM18 allows for recording multiple scans at different resolutions ranging from 42 μm down to 0.6 μm, without the need to remove the object or manually change detectors. Free space propagation of up to 36 m yields excellent phase-contrast which in turn enhances the resolving power of the system. BM18 is generating an unprecedented amount of very large datasets. Consequently, novel strategies for data processing, storage and visualization are under development for this particular instrument.
Keep Attention to the Mapping:Application of AI for Geometric X-Ray CT Scan Calibration
Authors:   Deggendorf Institute of Technology, Wittl Simon ,   Deggendorf Institute of Technology, Weiss Anton ,   Deggendorf Institute of Technology, Herl Gabriel ,   Deggendorf Institute of Technology, Zabler Simon
Published: February 2023

This paper presents a method for mapping markers from CT calibration bodies in X-ray projections. In twin robotic CT systems, determining projection geometry from projection views is essential for reconstruction. Many algorithms, such as direct linear transformation (DLT), use a calibration body for this purpose. This consists of individual markers that must be individually assigned in a single X-ray projection. In the described approach, a transformation model is used to map the coordinates of the 2D marker centers to the correct index. This allows the use of freely designed calibration bodies, where the arrangement of the markers is not limited to geometric arrangements.
Learning-based Trajectory Optimization for a Twin Robotic CT System
Authors:   University of Erlangen-Nürnberg (FAU), Schneider Linda-Sophie ,   University of Erlangen-Nürnberg (FAU), Thies Mareike ,   Fraunhofer Institute for Integrated Circuits (IIS), Schielein Richard ,   Fraunhofer Institute for Integrated Circuits (IIS), Syben Christopher ,   Johns Hopkins University, Unberath Mathias ,   University of Erlangen-Nürnberg (FAU), Maier Andreas
Published: February 2023

Non-circular twin robot computed tomography (CT) systems provide a high degree of flexibility in positioning the X-ray source and detector. This makes the design of optimal CT trajectories particularly important, as it can potentially allow for a reduction in the number of projections required for image acquisition. Therefore, the optimization of CT trajectories is a key area of research in this field. This paper presents a method for optimising the trajectory of a non-circular twin robot computed tomography system, using a so-called detectability index to guide the optimization process. The proposed method involves simulating possible X-ray projections that are reachable by the robotic CT system and using a regressive ResNet-34 to predict the detectability index of each projection. An integer optimization problem is then used to generate an optimised CT trajectory based on this prediction, with the haversine distance between projections serving as an additional constraint. The performance of the ResNet-34 is evaluated on unseen projections. It effectively predicted the detectability index for a spherical sampling space, allowing for the optimization of arbitrary CT trajectories. In addition, the method was able to identify valuable projections according to the detectability index, enabling the reconstruction of a test object with only 30 out of 800 of the previously acquired projections without significant loss of information in the reconstruction domain.
Canny-Net: Known Operator Learning for Edge Detection
Authors:   Deggendorf Institute of Technology, Wittmann Julian ,   Deggendorf Institute of Technology, Herl Gabriel
Published: February 2023

We present Canny-Net, a modification of the 2D Canny edge detector into a pre-weighted neural network. CT scans of objects that contain metal components are characterized by artifacts like beam hardening, total absorption and scatter. Edge detection with classical methods, e.g. the Canny edge detector, is therefore prone to error. Using known operator learning, Canny-Net reduces maximum error bounds for edge detection in such CT scans. We show that Canny-Net is a light-weight neural network with a small number of trainable parameters. After training, we observe an increase of 11% in F1 score on 2304 test images when compared to the Canny edge detector. Due to its adaptability and low computational cost, Canny-Net can be considered for a wide range of different applications.
Ex- and in-situ tests of materials: From design to materials parameters via motion estimation
Authors:   Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau (RPTU) former Technische Universität Kaiserslautern, Nogatz Tessa ,   Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau (RPTU) former Technische Universität Kaiserslautern, Redenbach Claudia ,   Fraunhofer-Institut für Techno- und Wirtschaftsmathematik (ITWM), Schladitz Katja
Published: February 2023

Materials tests are inevitable to investigate how materials behave under load. This also holds for materials that are not manufactured from bulk material but exhibit an interesting microstructure. Unfortunately, in this case classical stress-strain investigations are no longer sufficient to deduce a proper description of failure mechanisms. To overcome this problem, materials testing is usually combined with an imaging modality to gain information on local material behavior. The combination with classical digital cameras is quite common, but only yields surface information. Here, we present a framework to combine mechanical tests with computed tomography. Such in situ tests are not new, however, we also give information on how to perform tests ex situ. Our special focus is on motion estimation. We use an algorithm that is particularly suitable for delicate behavior of materials with interesting microstructures such as foams. The whole pipeline for materials tests, either in situ or ex situ, is outlined using two foam examples.
Simulated and experimental evaluation of the accuracy of twin robotic CT systems
Authors:   Deggendorf Institute of Technology, Weiss Anton ,   Deggendorf Institute of Technology, Wittl Simon ,   Deggendorf Institute of Technology, Herl Gabriel ,   Deggendorf Institute of Technology, Zabler Simon
Published: February 2023

Conventional industrial X-Ray CT systems are limited in their movement because of their limited degrees of freedom and overall design. Equipping CT with two industrial robotic arms, e.g. for manipulating source and detector, overcomes this limitation. Industrial robots are less restricted in the positioning and orientation of their X-ray tools than comparable industrial CT systems, making them flexible manipulators. However, their absolute positioning accuracy is usually below the minimum requirement for use in computed tomography. This paper proposes a calibration method with a variable, low-cost calibration body that allows pose correction of arbitrary projection geometries. The results from a twin robotic CT system will be evaluated via simulated projection data and actual experimental computer tomographic reconstruction.
Geometry Calibration Correction for Truncated Detector CT
Authors:   Technische Universität Braunschweig (TU), Wedekind Markus ,   Carl Zeiss GOM Metrology GmbH, Mertsch Darvin ,   Technische Universität Braunschweig (TU), Castillo Susana ,   Technische Universität Braunschweig (TU), Magnor Marcus
Published: February 2023

In industrial CT, objects are often reconstructed from a circular scan trajectory. This reconstruction is possible even if only half of the object is visible in the detector at any given time, but at the cost of redundancies that can be used for the correction of miscalibrations. We propose a scheme for recalibration using only a sinogram of projections truncated in such a manner. The sinogram does not need to be of any particular phantom and can depict any object. We show that the scheme is effective and produces calibrations that are similarly accurate as their untruncated counterparts.
Adapting an XCT-scanner to enable edge illumination X-ray phase contrast imaging
Authors:   University of Antwerpen, Huyge Ben ,   University of Antwerpen, Vanthienen Pieter-Jan ,   University of Antwerpen, Six Nathanaël ,   University of Antwerpen, Sijbers Jan ,   University of Antwerpen, De Beenhouwer Jan
Published: February 2023

Conventional X-ray computed tomography (XCT) is a non-destructive imaging technique to visualize and inspect the internal structure of materials in 3D, where materials are distinguished solely on the basis of their attenuation coefficient. However, with more specialized X-ray phase contrast imaging methods, sensitivity to complementary contrasts can be achieved, namely phase contrast and dark field contrast. Edge illumination X-ray phase contrast imaging (EI-XPCI) is a technique that is especially suited for laboratory application, because it has no strict coherence requirements for the source. While there is a wide variety of XCTscanners available on the market, a commercial EI-XPCI scanner does not yet exist. In this work, we present the procedure of outfitting an existing XCT-scanner with components to enable EI-XPCI scans and discuss the alignment procedure of these added components. Finally, phase contrast images are shown demonstrating the successful upgrade of a conventional XCT system with edge illumination based phase contrast possibilities.
AI-Powered Multi-Class Defect Segmentation in Industrial CT Data
Authors:   Hochschule Karlsruhe – University of Applied Sciences, Schanz Tim ,   Hochschule Karlsruhe – University of Applied Sciences, Tenscher-Philipp Robin ,   Hochschule Karlsruhe – University of Applied Sciences, Marschall Fabian ,   Hochschule Karlsruhe – University of Applied Sciences, Simon Martin
Published: February 2023

Industrial computed tomography is a key technology for non-destructive testing for many applications. The growing demand of automated testing requires an efficient automated data analysis. In this work we propose a solution using a neural network for voxel-accurate segmentation of 4 relevant defect classes in industrial casting parts and additively manufactured parts. Defect classes are pores, cracks, inclusions and pore nests. The network was trained and evaluated with a dataset of 20,000 samples. We used a modified U-Net architecture originally developed for medical image segmentation which we optimized for our industrial CT-Data and the multi-class segmentation task. In this work we show that we were able to achieve promising results of ~82 % MeanIoU score and individual defect class MeanIoU values of up to 95 % segmentation performance and qualitatively a high classification performance.