X-ray Projection Research Articles

A Demonstrator for Threat Detection in Volumetric CT Scans

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.

Possible Application of Computed Tomography for Numerical Simulation of the Damage Mechanism of Cementitious Materials—A Method Review

Modeling the meso-scale behavior of a material allows one to answer many problems in an inductive reasoning approach. By simulating the behavior of the sample, researchers can correct the overall behavior of the material. The purpose of this article was to gather the main information linking the issue of tomographic imaging to the modeling of damage mechanisms. This paper addresses the topic of meso-scale modeling of cementitious materials. The authors have mapped the interconnectedness of the issues of cementitious materials and X-ray computed tomography (µCT) according to the Web of Science database. The main interconnecting threads are indicated. The authors focused on the use of µCT. The theoretical basis for conducting tomographic measurements is presented. Reference was made to the basics of physics and mathematics in X-ray projection and data reconstruction. Tools and analyses for data processing are indicated. The benefits of in situ µCT are indicated with reference to the practical application of the method. In addition, the main developments of recent years in the most widely used computational methods for meso-scale models are presented.

Feature shape inspection of metal parts by matching X-ray projection images with CAD model projections

X-ray computed tomography (CT) provides a three-dimensional (3D) volume image of the scanned object and can be applied for nondestructive inspection of industrial materials. The object surface extracted from the X-ray CT volume is examined by comparing it with the pre-obtained computer-aided design (CAD) mesh model of the scanned object. However, industrial materials often contain heavy metals that significantly attenuate X-rays and cause CT artifacts in CT images, preventing an accurate shape extraction. This study proposes a method that extracts the geometric features of two-dimensional (2D) X-ray projection images and matches them with pre-obtained 3D CAD meshes. The proposed method is based on the principle that most of the geometric features of the object, such as edges, in the X-ray 2D projection images are captured and can be directly compared with CAD meshes. We first extract the features in each of the 2D projection images as X-ray feature projections. Subsequently, CAD mesh contours are projected onto 2D mesh feature projections and matched with the X-ray feature projections. Finally, 3D feature shapes of the CAD mesh are obtained by back-projecting the matching results. The proposed method requires only a small number of projection images, thus reducing computational time while maintaining shape accuracy.

CT-based data generation for foreign object detection on a single X-ray projection

Although X-ray imaging is used routinely in industry for high-throughput product quality control, its capability to detect internal defects has strong limitations. The main challenge stems from the superposition of multiple object features within a single X-ray view. Deep Convolutional neural networks can be trained by annotated datasets of X-ray images to detect foreign objects in real-time. However, this approach depends heavily on the availability of a large amount of data, strongly hampering the viability of industrial use with high variability between batches of products. We present a computationally efficient, CT-based approach for creating artificial single-view X-ray data based on just a few physically CT-scanned objects. By algorithmically modifying the CT-volume, a large variety of training examples is obtained. Our results show that applying the generative model to a single CT-scanned object results in image analysis accuracy that would otherwise be achieved with scans of tens of real-world samples. Our methodology leads to a strong reduction in training data needed, improved coverage of the combinations of base and foreign objects, and extensive generalizability to additional features. Once trained on just a single CT-scanned object, the resulting deep neural network can detect foreign objects in real-time with high accuracy.

Technical note: Extended longitudinal and lateral 3D imaging with a continuous dual-isocenter CBCT scan.

The clinical benefits of intraoperative cone beam CT (CBCT) during orthopedic procedures include (1) improved accuracy for procedures involving the placement of hardware and (2) providing immediate surgical verification. Orthopedic interventions often involve long and wide anatomical sites (e.g., lower extremities). Therefore, in order to ensure that the clinical benefits are available to all orthopedic procedures, we investigate the feasibility of a novel imaging trajectory to simultaneously expand the CBCT field-of-view longitudinally and laterally. A continuous dual-isocenter imaging trajectory was implemented on a clinical robotic CBCT system using additional real-time control hardware. The trajectory consisted of 200° circular arcs separated by alternating lateral and longitudinal table translations. Due to hardware constraints, the direction of rotation (clockwise/anticlockwise) and lateral table translation (left/right) was reversed every 400°. X-ray projections were continuously acquired at 15frames/s throughout all movements. A whole-body phantom was used to verify the trajectory. As comparator, a series of conventional large volume acquisitions were stitched together. Image quality was quantified using Root Mean Square Deviation (RMSD), Mean Absolute Percentage Deviation (MAPD), Structural Similarity Index Metric (SSIM) and Contrast-to-Noise Ratio (CNR). The imaging volume produced by the continuous dual-isocenter trajectory had dimensions of L=95cm×W=45cm×H=45cm. This enabled the hips to the feet of the whole-body phantom to be captured in approximately half the imaging dose and acquisition time of the 11 stitched conventional acquisitions required to match the longitudinal and lateral imaging dimensions. Compared to the stitched conventional images, the continuous dual-isocenter acquisition had RMSD of 4.84, MAPD of 6.58% and SSIM of 0.99. The CNR of the continuous dual-isocenter and stitched conventional acquisitions were 1.998 and 1.999, respectively. Extended longitudinal and lateral intraoperative volumetric imaging is feasible on clinical robotic CBCT systems.

IntelliScan: Improving the quality of x-ray computed tomography surface data through intelligent selection of projection angles.

X-ray computed tomography (XCT) enables the dimensional measurement and inspection of highly geometrically complex engineering components that are unmeasurable using optical and tactile instruments. Conventional XCT scans use a circular scan trajectory where X-ray projections are acquired with a uniform angular spacing; this approach treats all projections as being of equal importance, in practice, some projections contain more object information than others. In this work we capitalize on this concept by intelligently selecting projections with a view to improve the quality of surface models extracted from an XCT data-set. Our approach relies on using a priori object information to select X-ray projections in which the surfaces of the object are aligned with a ray-path, thus ensuring the surface of the object is fully sampled. Results are presented showing that the proposed method is able to reduce CAD comparison errors by 16%, reduce surface form error by 3%, and improve edge contrast by 14% for a machined aluminium component.

2D/3D Non-Rigid Image Registration via Two Orthogonal X-ray Projection Images for Lung Tumor Tracking

Two-dimensional (2D)/three-dimensional (3D) registration is critical in clinical applications. However, existing methods suffer from long alignment times and high doses. In this paper, a non-rigid 2D/3D registration method based on deep learning with orthogonal angle projections is proposed. The application can quickly achieve alignment using only two orthogonal angle projections. We tested the method with lungs (with and without tumors) and phantom data. The results show that the Dice and normalized cross-correlations are greater than 0.97 and 0.92, respectively, and the registration time is less than 1.2 seconds. In addition, the proposed model showed the ability to track lung tumors, highlighting the clinical potential of the proposed method.

Highly Efficient, Flexible, and Eco-Friendly Manganese(II) Halide Nanocrystal Membrane with Low Light Scattering for High-Resolution X-ray Imaging.

Scintillators enable invisible X-ray to be converted into ultraviolet (UV)/visible light that can be collected using a sensor array and is the core component of the X-ray imaging system. However, combining the excellent properties of high light output, high spatial resolution, flexibility, non-toxicity, and cost effectiveness into a single X-ray scintillator remains a great challenge. Herein, a novel scintillator based on benzyltriphenylphosphonium manganese(II) bromide (BTP2MnBr4) nanocrystal (NC) membranes was developed by the in situ fabrication strategy. The long Mn-Mn distance provided by the large BTP cation allows the nonradiative energy dissipation in this manganese(II) halide to be significantly suppressed. As a result, the flexible BTP2MnBr4 NC scintillator shows an excellent linear response to the X-ray dose rate, a high light yield of ∼71,000 photon/MeV, a low detection limit of 86.2 nGyair/s at a signal-to-noise ratio of 3, a strong radiation hardness, and a long-term thermal stability. Thanks to the low Rayleigh scattering associated with the dense distribution of nanometer-scale emitters, light cross-talk in X-ray imaging is greatly suppressed. The impressively high-spatial resolution X-ray imaging (23.8 lp/mm at modulation transfer function = 0.2 and >20 lp/mm for a standard pattern chart) was achieved on this scintillator. Moreover, well-resolved 3D dynamic rendering X-ray projections were also successfully demonstrated using this scintillator. These results shed light on designing efficient, flexible, and eco-friendly scintillators for high-resolution X-ray imaging.

CG-DRR：digital reconstructed radiograph generation algorithm based on Cycle-GAN

CG-DRR：digital reconstructed radiograph generation algorithm based on Cycle-GAN

The Management of Femoroacetabular Impingement

Management of the Femoroacetabular Impingement (FAI) starts with good diagnosis. What one must remember that the natural course of FAI is the precursor to hip osteoarthritis. An excellent history taking and physical examination is mandatory to exclude many differential diagnosis surrounding the hip. A layered approach could be used. 90 degrees of flexion and internal rotation (FADIR) is specific for hip problem, although doesn’t mean the main culprit is always FAI. Radiology study is very important, several hip projection Xray that can be chosen and specific measurement like Alpha angle, Cam or Pincer or mixed deformity, and so on. Additional imaging with CT or MR Arthrography is also important. With confirmed FAI, controversies exist whether to proceed conservatively or surgery, with nuance of the evidence depends on the background of the writer. Nowadays, surgery for FAI already pushed toward full arthroscopic, while surgical hip dislocation remains a powerful arsenal. Rehabilitation after surgery is utmost importance, and it took time for the results to show it benefits, therefore, a good communication with patient and the whole team of care is advisable.

Feasibility study of deep learning-based markerless real-time lung tumor tracking with orthogonal X-ray projection images.

The feasibility of a deep learning-based markerless real-time tumor tracking (RTTT) method was retrospectively studied with orthogonal kV X-ray images and clinical tracking records acquired during lung cancer treatment. Ten patients with lung cancer treated with marker-implanted RTTT were included. The prescription dose was 50Gy in four fractions, using seven- to nine-port non-coplanar static beams. This corresponds to 14-18 X-ray tube angles for an orthogonal X-ray imaging system rotating with the gantry. All patients underwent 10 respiratory phases four-dimensional computed tomography. After a data augmentation approach, for each X-ray tube angle of a patient, 2250 digitally reconstructed radiograph (DRR) images with gross tumor volume (GTV) contour labeled were obtained. These images were adopted to train the patient and X-ray tube angle-specific GTV contour prediction model. During the testing, the model trained with DRR images predicted GTV contour on X-ray projection images acquired during treatment. The predicted three-dimensional (3D) positions of the GTV were calculated based on the centroids of the contours in the orthogonal images. The 3D positions of GTV determined by the marker-implanted RTTT during the treatment were considered as the ground truth. The 3D deviations between the prediction and the ground truth were calculated to evaluate the performance of the model. The median GTV volume and motion range were 7.42 (range, 1.18-25.74) cm3 and 22 (range, 11-28) mm, respectively. In total, 8993 3D position comparisons were included. The mean calculation time was 85ms per image. The overall median value of the 3D deviation was 2.27 (interquartile range: 1.66-2.95) mm. The probability of the 3D deviation smaller than 5mm was 93.6%. The evaluation results and calculation efficiency show the proposed deep learning-based markerless RTTT method may be feasible for patients with lung cancer.

Reconstructing a 3D Medical Image from a Few 2D Projections Using a B-Spline-Based Deformable Transformation

(1) Background: There was a need for 3D image reconstruction from a series of 2D projections in medical applications. However, additional exposure to X-ray projections may harm human health. To alleviate it, minimizing the projection numbers is a solution to reduce X-ray exposure, but this would cause significant image noise and artifacts. (2) Purpose: In this study, a method was proposed for the reconstruction of a 3D image from a minimal set of 2D X-ray projections using a B-spline-based deformable transformation. (3) Methods: The inputs of this method were a 3D image which was acquired in previous treatment and used as a prior image and a minimal set of 2D projections which were acquired during the current treatment. The goal was to reconstruct a new 3D image in current treatment from the two inputs. The new 3D image was deformed from the prior image via the displacement matrixes that were interpolated by the B-spline coefficients. The B-spline coefficients were solved with the objective function, which was defined as the mean square error between the reconstructed and the ground-truth projections. In the optimization process the gradient of the objective function was calculated, and the B-spline coefficients were then updated. For the acceleration purpose, the computation of the 2D and 3D image reconstructions and B-spline interpolation were implemented on a graphics processing unit (GPU). (4) Results: When the scan angles were more than 60°, the image quality was significantly improved, and the reconstructed image was comparable to that of the ground-truth image. As the scan angles were less than 30°, the image quality was significantly degraded. The influence of the scan orientation on the image quality was minor. With the application of GPU acceleration, the reconstruction efficiency was improved by hundred times compared to that of the conventional CPU. (5) Conclusions: The proposed method was able to generate a high-quality 3D image using a few 2D projections, which amount to ~ 20% of the total projections required for a standard image. The introduction of the B-spline-interpolated displacement matrix was effective in the suppressing noise in the reconstructed image. This method could significantly reduce the imaging time and the radiation exposure of patients under treatment.

The Projection and Transmission X-ray Microscopy at Taiwan Photon Source

Projection X-ray microscopy (PXM) and transmission X-ray microscopy (TXM) have been established at beamline 31 at the Taiwan Photon Source (TPS). The TPS 31A beamline is sourced by a wiggler with multiple beam modes: white beam, high-flux mono-beam, and high-resolution mono-beam modes. The PXM is based on a collimated beam, and the TXM is full-field microscopy based on a zoneplate. The PXM system and TXM share the same detector platform, which is equipped with different kinds of detector systems for PXM and TXM. The PXM was designed for high-speed 3D tomography with an automatic tray system for loading and unloading samples, including a robotic arm and pre-alignment system. Both the PXM and TXM systems were designed for use in situ, which requires longer working distance for the sample’s environmental cell.

Evaluation of patient doses for routine digital radiography procedures toward establishing an institutional diagnostic reference levels: A case study in Sri Lanka.

The present study was conducted as part of a comprehensive work to establish National Diagnostic Reference Levels (NDRLs) in Sri Lanka for the first time. DRLs can be used as an effective optimization tool for identifying unusually high or low patient doses during X-ray examinations. This study aims to propose institutional DRLs (IDRLs) by measuring the kerma-area product (KAP) of adult patients undergoing routine projection X-ray examinations. The median and the 75th percentile KAP values obtained were compared with that of the single institution KAP values reported from India and Greece. This descriptive cross-sectional study was conducted in a public hospital in Uva province, Sri Lanka, with 400 adult patients aged 18-87 years and weighing 58±20kg. The patient-specific information (age, sex, weight, and height) and corresponding exposure parameters (tube voltage and current-exposure time product) were obtained. The KAP values were measured, and descriptive statistics were utilized for data analysis. The median KAP values obtained were proposed as IDRLs. The IDRLs in Gy.cm2 were 0.23 for cervical spine anterior-posterior (AP), 0.19 for cervical spine lateral (LAT), 0.10 for chest posterior-anterior (PA), 0.06 for knee joint AP, 0.05 for knee joint LAT, 1.47 for KUB AP, 0.85 for lumbar spine AP, 1.97 for lumbar spine LAT, 0.29 for shoulder joint AP, 0.61 for skull PA, and 0.60 for skull LAT examinations. The maximum to minimum ratio of KAP values ranged from 2.4 for KUB AP to 6.3 for the cervical spine AP examinations. The median and the 75th percentile of most of the examinations were comparable to corresponding KAP values reported by the countries mentioned above, except for the skull PA and LAT examinations. Accordingly, interquartile ranges of exposure parameters are recommended for skull examinations to improve the optimization of patient doses.

EXTP Large Area Detector: Qualification procedure of the mass production

A 7.24 × 12.03 cm2 sensor, Silicon Drift Detector (SDD), has been developed for the enhanced X-ray Timing and Polarimetry (eXTP) mission of the Chinese Academy of Science, with a large contribution by a European consortium inherited from the ESA-M3 LOFT mission study. In the frame of the project X-ray Observatories (XRO), active in the National Scientific Commission 2 of the INFN, we report the details of the qualification procedure to select from the mass production the 640 sensors that will equip the Large Area Detector (the eXTP instrument dedicated to the X-ray spectroscopy in the range 2-30 keV), with energy resolution below 240 eV FWHM at 6 keV during the entire mission duration of at least 5 years. This stringent requirement dictates the need to thoroughly verify the characteristics of each single sensor before integration in the final layout. We describe the dedicated testing facilities that have been developed. We report on the detector selection criteria and test results obtained in the pre-series production.

An Analytical Algorithm for Tensor Tomography From Projections Acquired About Three Axes.

Tensor fields are useful for modeling the structure of biological tissues. The challenge to measure tensor fields involves acquiring sufficient data of scalar measurements that are physically achievable and reconstructing tensors from as few projections as possible for efficient applications in medical imaging. In this paper, we present a filtered back-projection algorithm for the reconstruction of a symmetric second-rank tensor field from directional X-ray projections about three axes. The tensor field is decomposed into a solenoidal and irrotational component, each of three unknowns. Using the Fourier projection theorem, a filtered back-projection algorithm is derived to reconstruct the solenoidal and irrotational components from projections acquired around three axes. A simple illustrative phantom consisting of two spherical shells and a 3D digital cardiac diffusion image obtained from diffusion tensor MRI of an excised human heart are used to simulate directional X-ray projections. The simulations validate the mathematical derivations and demonstrate reasonable noise properties of the algorithm. The decomposition of the tensor field into solenoidal and irrotational components provides insight into the development of algorithms for reconstructing tensor fields with sufficient samples in terms of the type of directional projections and the necessary orbits for the acquisition of the projections of the tensor field.

Calibration of 3D scan trajectories for an industrial computed tomography setup with 6-DOF object manipulator system using a single sphere

In industrial x-ray computed tomography (CT), the application of more complex scan paths in comparison to the typical circular trajectory ( rotation of the measurement object) can extend the potential of CT. One way to enable such 3D scan trajectories is to use a 6-degrees-of-freedom (DOF) object manipulator system. In our case, a hexapod is mounted on top of the rotary table of a commercial CT scanner. This allows for adaptive tilting of the measurement object during the scan. For high accuracy, the geometry calibration of such setups is typically done using the x-ray projections of a calibrated multi-sphere object. Contrary to this, here, we demonstrate a procedure that is based on only a single sphere and can therefore experimentally be implemented with low effort. Using the intrinsic geometry parameters of the CT device as prior information, the hexapod coordinate system with respect to the CT machine coordinate system is determined by means of a one-step optimization approach. The resulting parameters are used to calculate projection matrices that enable the volume reconstruction for 3D scan trajectories. The method is validated using simulated x-ray images and experimental investigations including dimensional measurements. For the used setup, geometric measurement results for 3D scan trajectories that are calibrated with the presented method show in sum increased errors compared to the circular scans. A limited pose accuracy of the manipulator system is discussed as a potential cause. The results nevertheless indicate that the presented method is generally feasible for dimensional CT measurements provided that the pose accuracy is sufficient. The calibration procedure can therefore be a low-cost and easier to implement alternative compared to trajectory calibration methods based on multi-sphere objects, but with a tendency towards lower measurement accuracy. The methodology can in principle be transferred to different setups with 6-DOF manipulator systems, e.g. C-arm CT devices with a robot arm.

Comparison Between Hallux Rigidus Parameters Assessed by Conventional Radiographs and Weight- Bearing CT

Category: Midfoot/Forefoot; Other Introduction/Purpose: The etiology of hallux rigidus (HR) is not well understood and its association with first-ray hypermobility, hallux valgus and metatarsus primus elevatus (MPE) has not been clearly proven. Recent studies have supported MPE in HR by measuring on weightbearing radiographs (WBR), but there are limitations of WBR due to variation in X-ray projection and superimposed metatarsals. WBCT represent a reliable tool for radiographic analysis of the first ray and can be important for the study pathologies such as HR, which involve several anatomical points to be evaluated for a correct clinical- radiographic diagnosis Our objective was to evaluate radiographic parameters associated with Hallux Rigidus via WBR and WBCT for convergent validity and to evaluate the use of the classification system used in conventional radiography with WBCT. Methods: This is a single-center, retrospective study from prospectively collected data. 20 symptomatic hallux rigidus patient with weightbearing radiograph and WBCT were enrolled from October 2014 to December 2020. Measured parameters included hallux valgus angle (HVA), intermetatarsal angle (IMA), 1st TMT joint version, First and second metatarsal lengths, 2nd cuneiform- 2nd metatarsal angle, Talus-1st Metatarsal angle, First and second metatarsal declination angles, and MPE. MPE was measured as the direct distance between 1st and 2nd metatarsals (modified Horton index). All patients were graded according to the radiographic criteria of Coughlin and Shurnas classification on radiographs and WBCT, separately. Paired T-test was performed to compare radiographic measurements with WBCT. Results: Mean age was 55.9. HVA (15.73° in X-ray vs 14.04° in WBCT), AP first TMT version (16.25° vs 16.47°), 2nd cuneiform- 2nd metatarsal angle (24.54° vs 26.60°), Talus-1st Metatarsal Angle (-7.67° vs -7.89°) were not different between radiograph and WBCT. MPE was measured higher in WBCT by 0.86 mm compared to radiograph. First metatarsal declination angle was lower in WBCT by 2.9° indicating increased MPE. When graded with radiographic findings, 5 (25%) patients were grade 1, 5 (25%) patients were grade 2 and 10 (50%) grade 3, when graded with WBCT, 1 (5%) patient had grade 1, 3 (15%) patients were grade 2 and 16 (80%) grade 3. When graded radiologically, subchondral cyst in proximal phalanx and metatarsal head were better delineated resulting in higher radiographic grade with WBCT. Dorsal subluxation/translation of first metatarsal at first TMT joint was observed 3 (15%) in radiograph and 9 (45%) in WBCT. Conclusion: Hallux Valgus Angle, AP first TMT version, 2nd cuneiform-2nd metatarsal angle, Talus-1st Metatarsal Angel demonstrated consistent measurements in radiograph and WBCT. MPE was measured higher in WBCT when measured with direct distance (0.86 mm) between the first and second metatarsals using modified Horton index and first metatarsal declination angle (2.9°). When graded with WBCT, the subchondral cyst in the proximal phalanx and metatarsal head were better delineated leading to higher grade in WBCT which infer WBCT grading system for HR can aid in early detection of advanced HR with high radiographic grades and potentially guide treatment accordingly.

Academic program recommendations for graduate degrees in medical physics: AAPM Report No. 365 (Revision of Report No. 197).

Academic program recommendations for graduate degrees in medical physics: AAPM Report No. 365 (Revision of Report No. 197).

Self-supervised learning enables 3D digital subtraction angiography reconstruction from ultra-sparse 2D projection views: A multicenter study

Summary3D digital subtraction angiography (DSA) reconstruction from rotational 2D projection X-ray angiography is an important basis for diagnosis and treatment of intracranial aneurysms (IAs). The gold standard requires approximately 133 different projection views for 3D reconstruction. A method to significantly reduce the radiation dosage while ensuring the reconstruction quality is yet to be developed. We propose a self-supervised learning method to realize 3D-DSA reconstruction using ultra-sparse 2D projections. 202 cases (100 from one hospital for training and testing, 102 from two other hospitals for external validation) suspected to be suffering from IAs were conducted to analyze the reconstructed images. Two radiologists scored the reconstructed images from internal and external datasets using eight projections and identified all 82 lesions with high diagnostic confidence. The radiation dosages are approximately 1/16.7 compared with the gold standard method. Our proposed method can help develop a revolutionary 3D-DSA reconstruction method for use in clinic.