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.

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.

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.

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 ....

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.