Simon Wittl

This study presents a comprehensive comparison of image quality between synchrotron-based and laboratory-based nano-computed tomography (nano-CT) systems, using a standardized 3D test phantom composed of monodisperse silica spheres. A round-robin approach was employed across multiple international facilities to benchmark performance across various imaging modalities and setups, including absorption and phase contrast techniques. Image quality was quantitatively assessed using three-dimensional signal-to-noise ratio (SNR _3D ), detection effectiveness (DE _3D ) and modulation transfer function (MTF _3D ) metrics. The results reveal that, while synchrotron-based nano-CT consistently delivers superior spatial resolution and shorter scan times, laboratory-based systems demonstrate competitive image quality at the cost of extended acquisition durations. Beyond the experimental comparison, the main contribution of this work is a standardized, open-source analysis framework that quantifies nano-CT image quality using SNR _3D , DE _3D and MTF _3D of a specific phantom. This combination of metrics provides a reproducible basis for cross-platform benchmarking of synchrotron and laboratory nano-CT implementations and can be readily applied to future instruments.

Robotic CT systems offer several advantages over conventional systems due to their high flexibility. They can perform almost any CT trajectory and are particularly well suited for region-of-interest (ROI) scans of objects that exceed the size limitations of conventional CT systems. However, robot-based manipulators have a significantly lower absolute positioning accuracy compared to conventional manipulators, necessitating additional calibration methods to refine the geometric information about the spatial position and orientation of the X-ray source and detector for each projection for higher resolution reconstructions. We propose a geometric calibration method for CT systems with twelve degrees of freedom that does not require additional calibration scans. The method is easy to use, computationally efficient, and supports continuous CT trajectories. It utilises spheres with unknown positions that are attached to the specimen. The calibration process is divided into two stages. First, the spatial positions of the spheres are estimated using the initial geometric information and the acquired projections. Second, these estimates serve as input to an iterative optimisation that calibrates each projection individually. The applicability of the proposed method is demonstrated through simulations and real-world scans using a twin robotic CT system. Both quantitative and qualitative evaluations show a significant improvement in scan quality, comparable to results obtained via offline calibration. Moreover, evaluations on simulated data confirm the method’s robustness even for systems with positioning errors in the millimetre range. This novel online calibration technique is computationally efficient, compatible with highly flexible CT systems, and holds promise for enabling future mobile CT applications.

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.

Twin robotic X-ray computed tomography (CT) refers to CT systems in which two robotic arms are used to independently move the X-ray source and the X-ray detector around the object. This setup enables flexible CT scans by using robots to move the X-ray source and the X-ray detector around an object’s region of interest. This allows scans of large objects, image quality optimization and scan time reduction. Despite these advantages, robotic CT systems still face challenges that limit their widespread adoption. This paper discusses the state of twin robotic CT and its current main challenges. These challenges include the optimization of scanning trajectories, precise geometric calibration and advanced 3D reconstruction techniques.

Twin robotic X-ray computed tomography (CT) systems enable flexible CT scans by using robots to move the X-ray source and the X-ray detector around an object’s region of interest. This allows scans of large objects, image quality optimization and scan time reduction. Despite these advantages, robotic CT systems still face challenges that limit their widespread adoption. This paper discusses the state of twin robotic CT and its current main challenges. These challenges include optimization of scanning trajectories, precise geometric calibration and advanced 3D reconstruction techniques.

Conventional industrial computed tomography (CT) systems are constrained in their choice of acquisition trajectories due to their mechanical design. These systems are very precise instruments since they do only move on primarily highly accurate rotational stages. In order to be able to scan an arbitrary Region of Interest (ROI), regardless of the position, size and weight of the specimen, conventional industrial robots can be used as flexible 6 degrees of freedom (DOF) manipulators. For example in a twin robot computed tomography system, acquisition geometries with arbitrary tool poses can be realized. In scientific applications, the quality of the CT volume image is of primary interest, whereas in an industrial environment it is often a matter of balancing quality and acquisition time. Common industrial robots cannot achieve the required positional accuracy without calibration to generate an ideal reconstruction. In the presented study, methods for the geometric correction of CT scans are compared. Image based correction is compared to general machine calibration and full pose tracking by laser trackers. Image quality metrics such as the Modulation Transfer Function, Shannon entropy and Tenengrad variance are utilized to evaluate and compare the reconstruction quality of the various correction and calibration approaches. The assessment of the reconstruction quality revealed a comparable reconstruction quality between the approaches, with the machine calibration approach emerging as one of the best, while also reducing the time-intensive correction overhead.

Low-dose CT scans are very fast, but they feature strong pixel noise which, in turn, invites for efficient denoising the same way that strong image blur invites for sharpening the images. Wiener-deconvolution serves as a starting point, combining these two operations in a linear Fourier filter. It may apply in 2D or 3D and requires merely estimates of the Signal-to-Noise Ratio SNR2 as well as the system Modulation Transfer Function (MTF). Meanwhile, any linear filter is unable to adapt to local contrast variations in the images. Therefore, convolutional neural networks (CNN) such as MSDnet or UNet can significantly improve reconstructions from low-dose CT scans thanks to their non-linear nature. The Noise2Inverse framework allows for training these CNN using a split-and-merge of two subsets generated from one raw CT dataset. This study investigates denoising by N2I with respect to the input data quality, the CNN network architecture and the fidelity of the features in the resulting denoised volume images. Unlike Wiener filtering, N2I is far more effective in removing pixel noise. Yet, these models give the vague impression that they might generate ghosts of small features (pores or spots) in the object. Image quality metrics like Fourier Shell Correlation and SNR2 power spectra show clear improvement. In particular, Noise2Inverse preserves the signal power spectrum of all scans reliably. Nevertheless, locally inspecting image features must remain a core technique when the CNN models are applied, i.e. for industrial inspection.

Industrial x-ray computed tomography (CT) systems with high geometric flexibility are increasingly utilized for large-scale measurement objects or challenging measurement tasks. To maintain high accuracy when deviating from the established circular scan trajectory, trajectory calibration methods using multi-sphere reference objects with known marker positions are commonly employed. These multi-sphere objects can either be scanned together with the measurement object (online trajectory calibration) or in a separate scan (offline trajectory calibration). While offline calibration increases machine time, it generally results in higher scan quality. However, a sufficient pose repeatability is necessary to ensure comparable or even superior accuracy to online calibration. In this contribution, we present a straightforward procedure to compare both types of trajectory calibration in a way that the differences of the results can directly be traced back to the influence of the pose repeatability. The multi-sphere reference object is not only used for trajectory calibration, but simultaneously as a measurement object for repeated measurements. The methodology is tested on both a twin robotic CT system and a conventional CT system that is additionally equipped with a hexapod manipulator for adaptive object tilting. Results showed, independent from the type of trajectory calibration, systematic measurement errors in the order of 10⁻⁵–10⁻⁴ of measured sphere distances and sphericity values below 50 μ m . For sphere distances, random errors were increased by a factor of 5 due to the offline trajectory calibration, but were still low ( < 1 μ m ) in comparison to systematic errors and the spread of different measurement features. Overall, both investigated systems demonstrated sufficient positioning repeatability for offline trajectory calibration. The method is in general also applicable to any other types of manipulator systems used for CT devices. It provides a workflow for the decision which type of trajectory calibration is preferable for a given CT system.

Industrial Computed Tomography (CT) scan control software must meet the highest standards of reliability, performance, and efficiency, which is why these criteria also form the main focus in their system design. Current systems excel when utilized for t....

Twin robotic computed tomography (RoboCT) offers a high degree of flexibility. RoboCT systems are commonly used for either flexible 2D inspection of large areas or 3D inspection of small areas in large objects. This study introduces a workflow to perform ....