The use of biplanar high-speed x-ray fluoroscopy to study fast, 3D movements that are inaccessible from external views has grown significantly in the past decade. Owing to the development of specialised software for calibration, distortion correction, and automated tracking of radio-opaque marker implants, this technique will soon become the standard to analyse skeletal kinematics of vertebrate animals. However, tests of important characteristics of biplanar x-ray systems, such as resolution and precision, remain scarce and incomplete. We present methods to determine imaging resolution and 3D stereoscopic and dynamic resolutions to follow moving markers in 3D, and demonstrate them on a newly installed stereoscopic x-ray system with image intensifiers. Using four-megapixel cameras, image resolution slightly surpasses previous reports. The spatial resolution appears to be optimal at magnification factors (ratio of source-to-detector to source-to-subject distance) between 1.33 and 2.20. This new information will allow biplanar x-ray system users to optimise the relative position of tube, subject, and image intensifiers.

Purpose X‐ray image intensifiers (XRIIs) inevitably produce images suffering from geometric distortion. Presently, various local and global methods exist to correct for these distortions. However, the performance of global methods is limited for dominant local distortions, and local methods tend to suffer from patch discontinuity and are generally sensitive to noise. In this paper, a novel local method is presented based on digital image correlation (DIC), which does not suffer from patch discontinuity and noise. Methods As DIC is a very accurate and robust technique to analyze deformations, it is our candidate of choice to outperform the existing correction methods. The performance of our technique was first validated through distortion simulations. Next, it was validated experimentally for four different orientations of the XRII. Results A theoretical study on images suffering from a simulated distortion (including noise and blurring) yielded corrections with an average accuracy of (0.20 ± 0.04) pixels. We obtained experimental data with our 14" XRII (292 mm field of view), suffering from a maximum distortion between 9.6 and 12.9 mm, and an average distortion between (4.4 ± 1.3) mm and (6.1 ± 2.5) mm over the image field for the different orientations. For an adequate choice of the facet size in the DIC analysis (greater than 40 pixels), the weighted mean residual error of our method varied between (0.037 ± 0.003) mm and (0.054 ± 0.003) mm, regardless of the XRII orientation. The maximum residual error varied between 0.081 and 0.185 mm. Conclusions From the simulations, we concluded that the proposed technique is affected by neither Gaussian noise nor blurring. Furthermore, it is shown that our method can reach an accuracy that is on par with or better than the current standard tools. The novel method is fast, requires minimal operator intervention and can be fully automated.

Compared to single source systems, stereo X-ray CT systems allow acquiring projection data within a reduced amount of time, for an extended field-of-view, or for dual X-ray energies. To exploit the benefit of a dual X-ray system, its acquisition geometry needs to be calibrated. Unfortunately, in modular stereo X-ray CT setups , geometry misalignment occurs each time the setup is changed, which calls for an efficient calibration procedure. Although many studies have been dealing with geometry calibration of an X-ray CT system, little research targets the calibration of a dual cone-beam X-ray CT system. In this work, we present a phantom-based calibration procedure to accurately estimate the geometry of a stereo cone-beam X-ray CT system. With simulated as well as real experiments, it is shown that the calibration procedure can be used to accurately estimate the geometry of a modular stereo X-ray CT system thereby reducing the misalignment artifacts in the reconstruction volumes.

An issue in computerized X-ray tomography is the limited size of available detectors relative to objects of interest. A solution was provided in the past two decades by positioning the detector in a lateral offset position, increasing the effective field of view (FOV) and thus the diameter of the reconstructed volume. However, this introduced artifacts in the obtained reconstructions, caused by projection truncation and data redundancy. These issues can be addressed by incorporating an additional data weighting step in the reconstruction algorithms, known as redundancy weighting. In this work, we present an implementation of redundancy weighting in the widely-used Simultaneous Iterative Reconstruction Technique (SIRT), yielding the W-SIRT method. The new technique is validated using geometric phantoms and a rabbit specimen, by performing both simulation studies as well as physical experiments. The experiments are carried out in a highly flexible stereoscopic X-ray system equipped with X-ray image intensifiers (XRIIs). The simulations showed that higher values of CNR could be obtained using the W-SIRT approach as compared to a weighted implementation of SART. The convergence rate of the W-SIRT was accelerated by including a relaxation parameter in the W-SIRT algorithm, creating the aW-SIRT algorithm. This allowed to obtain the same results as the W-SIRT algorithm, but at half the number of iterations, yielding a much shorter computation time. The aW-SIRT algorithm has proven to perform well for both large as well as small regions of overlap, outperforming the pre-convolutional Feldkamp-David-Kress (FDK) algorithm for small overlap regions (or large detector offsets). The experiments confirmed the results of the simulations. Using the aW-SIRT algorithm, the effective FOV was increased by >75%, only limited by experimental constraints. Although an XRII is used in this work, the method readily applies to flat-panel detectors as well.

Accurate knowledge of the acquisition geometry of a CT scanning system is key for high quality tomographic imaging. Unfortunately, in modular X-ray CT setups, geometry misalignment occurs each time the setup is changed, which calls for an efficient calibration procedure to correct for geometric inaccuracies. Although many studies have been dealing with the calibration of X-ray CT systems, these are often specifically designed for one setup and/or expensive.In this work, we explore the possibilities of a low-cost, easy-tobuild, and modular phantom, constructed from LEGO bricks, which serves as a structure to hold small metal beads, for geometric calibration of a tomographic X-ray system. By estimating the bead coordinates using deep learning, and minimizing center-to-center distances of the metal beads between measured and reference projection data, geometry parameters are derived. With simulated as well as real experiments, it is shown that the LEGO phantom can be used to accurately estimate the geometry of a modular X-ray CT system.

Geometric distortion is inevitable in facilities using x-ray image intensifiers. When the induced distortion pattern varies over time, each recorded frame should be corrected accordingly, which is the case in conventional C-arm imaging, for example. This demonstrates the need for reliable and easy-to-use, projection-angle-dependent correction methods. In the present work, we demonstrate such a dynamic approach, based on digital image correlation (DIC). We validate the method in a set-up for high-speed x-ray tomography, where the variable distortion is induced by an inhomogeneous distribution of ferromagnetic components in the sample rotation stage. By comparing the ideal positions of metal beads in a rectilinear pattern and their positions in the corrected radiographs of that pattern, we deduced the minimum number of frames required to estimate the varying distortion behavior during a full revolution of the stage. Next, this method was validated in a geometry calibration algorithm for a tomographic set-up, as well as in a tomographic reconstruction. Before the application of any distortion correction, the recorded images suffer from, on average, a mean and maximum distortion of 11.12 pixels (1.56 mm) and 42.79 pixels (6.10 mm), respectively. From our experiments, we conclude that three projections, sampled with a 120° interval, are sufficient to correct any frame recorded under an intermediate angle, with mean and maximum residual errors respectively below 0.48 pixels (0.068 mm) and 1.68 pixels (0.240 mm) while using a 14” image intensifier covering a 292 mm × 292 mm field of view. These results imply a decrease of the mean and maximum distortion errors of at least 96%, regardless of the projection angle. Next, the results showed improved accuracy in the system’s geometry calibration, which resulted in a reduced blurring of edges and better contrast, as well as shape preservation in the tomographic reconstruction. This demonstrates that the new method is accurate and reliable and, due to the common availability of DIC software, it is very accessible and easy to use.

Knowledge of the acquisition geometry is key for tomographic reconstruction. Before image reconstruction algorithms can be applied to compute a 3D image from a set of 2D projections, calibration must be carried out to correct geometrical inaccuracies. The main source of geometric misalignment can be attributed to possible mechanical instability and slight offsets in rotation and translation of the source, detector and/or the sample stage themselves from the measured parameters. Although, many studies have been dealing with the calibration problems for a specific X-ray CT system, most of those methods require specificallydesigned and/or expensive phantoms. In this work, we introduce a low-cost, easy-to-use and readily available phantom, built from LEGO bricks that serves as a structure to hold small, ‘metal’ beads for geometric calibration of a tomographic X-ray system.

Fusion of X-ray projection images obtained with different exposure levels is a promising technique for studying objects with features beyond the dynamic range of the X-ray detector. Various multi-exposure fusion techniques are described in the literature, yet a direct comparison between these methods is not available. This was mainly due to the absence of objective quality measures dedicated to multi-exposure X-ray images and tomographic reconstructions, a problem remaining unsolved to this day. Therefore, in this work, we compare several fusion algorithms in terms of perceptual quality using recently reported quality measures based on structural similarity. Moreover, we investigate whether these quality measures apply to tomographic slices as well. Our results indicate that the reliability of the quality measures is more convincing for fused projection images as opposed to reconstructed slices. Additionally, it is shown that fusion algorithms developed for optical photography are also suitable for multi-exposure X-ray image fusion to increase perceptual quality.