Geometric reconstruction methods for electron tomography

Electron tomography is becoming an increasingly important tool in materials science for studying the three-dimensional morphologies and chemical compositions of nanostructures. The image quality obtained by many current algorithms is seriously affected by the problems of missing wedge artefacts and nonlinear projection intensities due to diffraction effects. The former refers to the fact that data cannot be acquired over the full $180^\circ$ tilt range; the latter implies that for some orientations, crystalline structures can show strong contrast changes. To overcome these problems we introduce and discuss several algorithms from the mathematical fields of geometric and discrete tomography. The algorithms incorporate geometric prior knowledge (mainly convexity and homogeneity), which also in principle considerably reduces the number of tilt angles required. Results are discussed for the reconstruction of an InAs nanowire

CUDA accelerated cone‐beam reconstruction

Cone-Beam Computed Tomography (CBCT) is an imaging method that reconstructs a 3D representation of the object from its 2D X-ray images. It is an important diagnostic tool in the medical field, especially dentistry. However, most 3D reconstruction algorithms are computationally intensive and time consuming; this limitation constrains the use of CBCT. In recent years, high-end graphics cards, such as the ones powered by NVIDIA graphics processing units (GPUs), are able to perform general purpose computation. Due to the highly parallel nature of the 3D reconstruction algorithms, it is possible to implement these algorithms on the GPU to reduce the processing time to the level that is practical. Two of the most popular 3D Cone-Beam reconstruction algorithms are the Feldkamp-Davis-Kress algorithm (FDK) and the Algebraic Reconstruction Technique (ART). FDK is fast to construct 3D images, but the quality of its images is lower than the quality of ART images. However, ART requires significantly more computation. Material ART is a recently developed algorithm that uses beam-hardening correction to eliminate artifacts. In this thesis, these three algorithms were implemented on the NVIDIA\u27s CUDA platform. These CUDA based algorithms were tested on three different graphics cards, using phantom and real data. The test results show significant speedup when compared to the CPU software implementation. The speedup is sufficient to allow a moderate cost personal computer with NVIDIA graphics card to process CBCT images in real-time

OSNet & MNetO: Two Types of General Reconstruction Architectures for Linear Computed Tomography in Multi-Scenarios

Recently, linear computed tomography (LCT) systems have actively attracted attention. To weaken projection truncation and image the region of interest (ROI) for LCT, the backprojection filtration (BPF) algorithm is an effective solution. However, in BPF for LCT, it is difficult to achieve stable interior reconstruction, and for differentiated backprojection (DBP) images of LCT, multiple rotation-finite inversion of Hilbert transform (Hilbert filtering)-inverse rotation operations will blur the image. To satisfy multiple reconstruction scenarios for LCT, including interior ROI, complete object, and exterior region beyond field-of-view (FOV), and avoid the rotation operations of Hilbert filtering, we propose two types of reconstruction architectures. The first overlays multiple DBP images to obtain a complete DBP image, then uses a network to learn the overlying Hilbert filtering function, referred to as the Overlay-Single Network (OSNet). The second uses multiple networks to train different directional Hilbert filtering models for DBP images of multiple linear scannings, respectively, and then overlays the reconstructed results, i.e., Multiple Networks Overlaying (MNetO). In two architectures, we introduce a Swin Transformer (ST) block to the generator of pix2pixGAN to extract both local and global features from DBP images at the same time. We investigate two architectures from different networks, FOV sizes, pixel sizes, number of projections, geometric magnification, and processing time. Experimental results show that two architectures can both recover images. OSNet outperforms BPF in various scenarios. For the different networks, ST-pix2pixGAN is superior to pix2pixGAN and CycleGAN. MNetO exhibits a few artifacts due to the differences among the multiple models, but any one of its models is suitable for imaging the exterior edge in a certain direction.Comment: 13 pages, 13 figure

Development of a reconstruction quality metric for optical three-dimensional measurement systems in use for hot-state measurement object

Optical three-dimensional (3-D) geometry measurements are state of the art when it comes to contactless quality control and maintenance of the shape of technical components that exclude tactile measurements due to filigree or internal structures. Optical inspection methods are also characterized by a fast and high-resolution 3-D inspection of complex geometries. And due to their noncontact principle, they can carry out measurements in places that would otherwise not be accessible due to harsh environmental conditions or specimens such as hot forged parts. However, there are currently no methods to estimate the reconstruction quality for the optical 3-D geometry measurements of hot objects. The mainly used geometric measurement standards cannot be used for the characterization of hot measurements since the calibrated geometrical values are not transferable to high temperatures. For the development of such a metric, we present the fundamentals of the concepts and algorithms for an estimation of the reconstruction quality are presented and evaluated using a two-dimensional simulation model. The generated findings were applied to the 3-D geometry measurement of a hot object in a laboratory environment. The results are compared with general state-of-the-art reconstruction quality metrics

Calibration and 3D Mapping for Multi-sensor Inspection Tasks with Industrial Robots

Le ispezioni di qualità sono una parte essenziale per garantire che il processo di produzione si svolga senza intoppi e che il prodotto finale soddisfi standard elevati. I robot industriali sono diventati uno strumento fondamentale per condurre le ispezioni di qualità, consentendo precisione e coerenza nel processo di ispezione. Utilizzando tecnologie di ispezione avanzate, i robot industriali possono rilevare difetti e anomalie nei prodotti a una velocità superiore a quella degli ispettori umani, migliorando l'efficienza della produzione. Grazie alla capacità di automatizzare le attività di ispezione ripetitive e noiose, i robot industriali possono anche ridurre il rischio di errore umano e aumentare la qualità dei prodotti. Con il continuo progresso tecnologico, l'uso dei robot industriali per le ispezioni di qualità si sta diffondendo in tutti i settori industriali, da quello automobilistico e manifatturiero a quello aerospaziale. Lo svantaggio di una tale varietà di compiti di ispezione è che di solito le ispezioni industriali richiedono configurazioni robotiche specifiche e sensori appropriati, rendendo ogni ispezione molto specifica e personalizzata. Per questo motivo, la presente tesi fornisce una panoramica di un framework di ispezione generale che risolve il problema della creazione di celle di lavoro di ispezione personalizzate, proponendo moduli software generali che possono essere facilmente configurati per affrontare ogni specifico scenario di ispezione. In particolare, questa tesi si concentra sui problemi della calibrazione occhio-mano, ovvero il problema di calcolare con precisione la posizione del sensore nella cella di lavoro rispetto all'inquadratura del robot, e del Data Mapping, utilizzato per mappare i dati del sensore nella rappresentazione del modello 3D dell'oggetto ispezionato. Per la calibrazione occhio-mano proponiamo due tecniche che risolvono con precisione la posizione del sensore in più configurazioni robotiche. Entrambe considerano la configurazione robot-sensore eye-on-base e eye-in-hand, vale a dire il modo in cui discriminiamo se il sensore è montato in un punto fisso della cella di lavoro o nel braccio terminale del manipolatore robotico, rispettivamente. Inoltre, uno dei principali contributi di questa tesi è un approccio generale alla calibrazione occhio-mano che è anche in grado di gestire, grazie a una formulazione unificata di ottimizzazione del grafo di posa, configurazioni di ispezione in cui sono coinvolti più sensori (ad esempio, reti multi-camera). In definitiva, questa tesi propone un metodo generale che sfrutta un risultato preciso e accurato della calibrazione occhio-mano per affrontare il problema del Data Mapping per i robot di ispezione multiuso. Questo approccio è stato applicato in diverse configurazioni di ispezione, dall'industria automobilistica a quella aerospaziale e manifatturiera. La maggior parte dei contributi presentati in questa tesi sono disponibili come pacchetti software open-source. Riteniamo che ciò favorisca la collaborazione, consenta una precisa ripetibilità dei nostri esperimenti e faciliti la ricerca futura sulla calibrazione di complesse configurazioni robotiche industriali.Quality inspections are an essential part of ensuring the manufacturing process runs smoothly and that the final product meets high standards. Industrial robots have emerged as a key tool in conducting quality inspections, allowing for precision and consistency in the inspection process. By utilizing advanced inspection technologies, industrial robots can detect defects and anomalies in products at a faster pace than human inspectors, improving production efficiency. With the ability to automate repetitive and tedious inspection tasks, industrial robots can also reduce the risk of human error and increase product quality. As technology continues to advance, the use of industrial robots for quality inspections is becoming more widespread across industrial sectors, ranging from automotive and manufactury to aerospace industries. The drawback of such a large variety of inspection tasks is that usually industrial inspections require specific robotic setups and appropriate sensors, making every inspection very specific and custom buildt. For this reason, this thesis gives an overview of a general inspection framework that solves the problem of creating customized inspection workcells by proposing general software modules that can be easily configured to address each specific inspection scenario. In particular, this thesis is focusing on the problems of Hand-eye Calibration, that is the problem of accurately computing the position of the sensor in the workcell with respect to the robot frame, and Data Mapping that is used to map sensor data to the 3D model representation of the inspected object. For the Hand-eye Calibration we propose two techniques that accurately solve the position of the sensor in multiple robotic setups. They both consider eye-on-base and eye-in-hand robot-sensor configuration, namely, this is the way in which we discriminate if the sensor is mounted in a fixed place in the workcell or in the end-effector of the robot manipulator, respectively. Moreover, one of the main contributions of this thesis is a general hand-eye calibration approach that is also capable of handling, thanks to a unified pose-graph optimization formulation, inspection setups where multiple sensors are involved (e.g., multi-camera networks). In the end, this thesis is proposing a general method that takes advantage of a precise and accurate hand-eye calibration result to address the problem of Data Mapping for multi-purpose inspection robots. This approach has been applied in multiple inspection setups, ranging from automotive to aerospace and manufactury industry. Most of the contributions presented in this thesis are available as open-source software packages. We believe that this will foster collaboration, enable precise repeatability of our experiments, and facilitate future research on the calibration of complex industrial robotic setups

X-ray computed tomography

X-ray computed tomography (CT) can reveal the internal details of objects in three dimensions non-destructively. In this Primer, we outline the basic principles of CT and describe the ways in which a CT scan can be acquired using X-ray tubes and synchrotron sources, including the different possible contrast modes that can be exploited. We explain the process of computationally reconstructing three-dimensional (3D) images from 2D radiographs and how to segment the 3D images for subsequent visualization and quantification. Whereas CT is widely used in medical and heavy industrial contexts at relatively low resolutions, here we focus on the application of higher resolution X-ray CT across science and engineering. We consider the application of X-ray CT to study subjects across the materials, metrology and manufacturing, engineering, food, biological, geological and palaeontological sciences. We examine how CT can be used to follow the structural evolution of materials in three dimensions in real time or in a time-lapse manner, for example to follow materials manufacturing or the in-service behaviour and degradation of manufactured components. Finally, we consider the potential for radiation damage and common sources of imaging artefacts, discuss reproducibility issues and consider future advances and opportunities