X-ray Projection Research Articles

Image Quality Comparison between 10kVp Technique and 15 Percent Rule on AP Supine Abdominal X-ray Examination

The abdomen is one of the thickest parts of the human body and there are challenges in obtaining good images due to the thickness and pathology of the patient during an abdominal X-ray. The 15% rule and the 10 kVp technique are used as exposure manipulation methods in abdominal radiography, however, theoretically, the 15% rule is preferred. The 15% rule provides optimal exposure while the 10 kVp technique is simpler in terms of calculating the exposure factor. These two techniques have their advantages; therefore, this study aims to compare the image quality between the 15% rule and the 10 kVp technique of X-ray supine anteroposterior (AP) abdominal radiography image quality and evaluate whether 10 kVp could be an exposure replacement technique for the 15% rule. Five abdomen radiographic images were taken based on both techniques and evaluated quantitatively using ImageJ software and qualitatively based on scoring by two radiographers. Based on the result of the experimental study, the SNR (signal-to-noise) ratio, CNR (contrast-to-noise ratio) and image mean score had no significant differences (p-value>0.05) between the 10 kVp and 15% rule. In conclusion, the 10 kVp technique is a possible substitution for the 15% rule in obtaining valuable diagnostic image quality during AP abdominal X-ray projection and is more suitable to be implied in clinical settings compared to the 15% rule.

Cervical Sagittal Balance: Impact on Clinical Outcomes and Subsidence in Anterior Cervical Discectomy and Fusion.

Degenerative disease of the cervical spine leads to sagittal imbalance, which may affect treatment results. The purpose of this study was to evaluate changes in selected cervical sagittal balance parameters and their effects on subsidence and clinical outcomes of the procedure. This study encompassed a total of 95 evaluated patients who underwent anterior cervical discectomy and fusion (ACDF). Selected cervical sagittal balance parameters were assessed using lateral projection X-rays: C2-C7 spinal vertical axis (C2-C7 SVA), spinocranial angle (SCA), C7 slope, C2-C7 lordosis, and the segmental Cobb angle. Measurements were collected the day before, the day after, and 12 months after surgery. Changes in clinical parameters was assessed using the VAS and NDI scales. Subsidence was defined as a loss of intervertebral height of more than 30% of the baseline value. Among all the assessed parameters, only the C2-C7 SVA demonstrated a statistically significant difference between the groups with and without subsidence: 26.03 vs. 21.79 [mm], with p = 0.0182, preoperatively and 27.80 vs. 24.94 [mm], with p = 0.0449, on the day after surgery, respectively. We conclude that higher preoperative and postoperative C2-C7 SVA values might contribute to an elevated risk of implant subsidence. Furthermore, both the SCA and C7 slope could conceivably influence the clinical outcome, respectively impacting pain, as assessed by the VAS and the disability, as evaluated through the NDI scale.

The efficiency and safety of multidetector computed tomography-guided transseptal puncture during atrial fibrillation catheter ablation.

Transseptal puncture (TSP) is a crucial technique for catheter ablation of atrial fibrillation (AF). Although intracardiac echo (ICE) facilitates a safe and accurate TSP, it is not widely used in developing countries because of the expense. This study evaluated the efficiency and safety of a novel cardiac multidetector computed tomography (MDCT)-guided TSP during AF catheter ablation. The study consisted of two cohorts. In the index cohort, TSP procedure was performed under the guidance of ICE, and we recorded the angulation of right anterior oblique of X-ray projection. In the validation cohort, we compared the efficiency and safety of TSP guided by MDCT-calculated angulation with propensity-score-matched patients who underwent TSP guided by ICE. We included 50 patients in the index cohort, and the mean angles of interatrial septum (IAS) measured from MDCT and ICE were 34.8 ± 6.3 and 35.1 ± 6.5, respectively. In the validation cohort, 376 patients were enrolled in the MDCT-guided group and ICE-guided group. Both groups had 1 case of cardiac tamponade. The mean axial plane angle was 35.46 ± 6.17 degrees, which was not influenced by age, gender, BMI, and LA size, while a moderate positive linear correlation between EF and the axial plane angle (R 2 = 0.14, p = .006). Cardiac MDCT can provide a clear vision of IAS orientation, and provide the appropriate RAO angle and height for TSP. The efficiency and safety of our MDCT-guided TSP were comparable to ICE-guided TSP, which may serve as an alternative method for TSP with ICE unavailable.

Applicability and robustness of an artificial intelligence-based assessment for Greulich and Pyle bone age in a German cohort.

The determination of bone age (BA) based on the hand and wrist, using the 70-year-old Greulich and Pyle (G&P) atlas, remains a widely employed practice in various institutions today. However, a more recent approach utilizing artificial intelligence (AI) enables automated BA estimation based on the G&P atlas. Nevertheless, AI-based methods encounter limitations when dealing with images that deviate from the standard hand and wrist projections. Generally, the extent to which BA, as determined by the G&P atlas, corresponds to the chronological age (CA) of a contemporary German population remains a subject of continued discourse. This study aims to address two main objectives. Firstly, it seeks to investigate whether the G&P atlas, as applied by the AI software, is still relevant for healthy children in Germany today. Secondly, the study aims to assess the performance of the AI software in handling non-strict posterior-anterior (p. a.) projections of the hand and wrist. The AI software retrospectively estimated the BA in children who had undergone radiographs of a single hand using posterior-anterior and oblique planes. The primary purpose was to rule out any osseous injuries. The prediction error of BA in relation to CA was calculated for each plane and between the two planes. A total of 1253 patients (aged 3 to 16 years, median age 10.8 years, 55.7 % male) were included in the study. The average error of BA in posterior-anterior projections compared to CA was 3.0 (± 13.7) months for boys and 1.7 (± 13.7) months for girls. Interestingly, the deviation from CA tended to be even slightly lower in oblique projections than in posterior-anterior projections. The mean error in the posterior-anterior projection plane was 2.5 (± 13.7) months, while in the oblique plane it was 1.8 (± 13.9) months (p = 0.01). The AI software for BA generally corresponds to the age of the contemporary German population under study, although there is a noticeable prediction error, particularly in younger children. Notably, the software demonstrates robust performance in oblique projections. · Bone age, as determined by artificial intelligence, aligns with the chronological age of the contemporary German cohort under study.. · As determined by artificial intelligence, bone age is remarkably robust, even when utilizing oblique X-ray projections.. · Pape J, Hirsch F, Deffaa O et al. Applicability and robustness of an artificial intelligence-based assessment for Greulich and Pyle bone age in a German cohort. Fortschr Röntgenstr 2024; 196: 600 - 606.

Joint denoising and interpolating network for low-dose cone-beam CT reconstruction under hybrid dose-reduction strategy

Cone-beam computed tomography (CBCT) is generally reconstructed with hundreds of two-dimensional X-Ray projections through the FDK algorithm, and its excessive ionizing radiation of X-Ray may impair patients’ health. Two common dose-reduction strategies are to either lower the intensity of X-Ray, i.e., low-intensity CBCT, or reduce the number of projections, i.e., sparse-view CBCT. Existing efforts improve the low-dose CBCT images only under a single dose-reduction strategy. In this paper, we argue that applying the two strategies simultaneously can reduce dose in a gentle manner and avoid the extreme degradation of the projection data in a single dose-reduction strategy, especially under ultra-low-dose situations. Therefore, we develop a Joint Denoising and Interpolating Network (JDINet) in projection domain to improve the CBCT quality with the hybrid low-intensity and sparse-view projections. Specifically, JDINet mainly includes two important components, i.e., denoising module and interpolating module, to respectively suppress the noise caused by the low-intensity strategy and interpolate the missing projections caused by the sparse-view strategy. Because FDK actually utilizes the projection information after ramp-filtering, we develop a filtered structural similarity constraint to help JDINet focus on the reconstruction-required information. Afterward, we employ a Postprocessing Network (PostNet) in the reconstruction domain to refine the CBCT images that are reconstructed with denoised and interpolated projections. In general, a complete CBCT reconstruction framework is built with JDINet, FDK, and PostNet. Experiments demonstrate that our framework decreases RMSE by approximately 8 %, 15 %, and 17 %, respectively, on the 1/8, 1/16, and 1/32 dose data, compared to the latest methods. In conclusion, our learning-based framework can be deeply imbedded into the CBCT systems to promote the development of CBCT. Source code is available at https://github.com/LianyingChao/FusionLowDoseCBCT.

Optimization of double-wire X-pinch using prepulse current

The radiation properties including size, intensity, and pulse width of the double-wire X-pinch were optimized using prepulse current. The optimization mechanism was investigated using optical diagnostics. The X-pinch driven by a current with a peak intensity of ∼250 kA and a rise rate of 0.95 kA/ns was difficult to produce strong radiation. The application of prepulse effectively enhances the current rise rate and intensity of the radiation source. The radiation source also exhibited reduced spatial dimensions and radiation duration, thereby improving spatial and temporal resolution while used in x-ray projection shadow radiography. The magnetic field and current distribution were measured using Faraday rotation. During the early stage, the current predominantly flows in the corona plasma at a larger radius generated by the prepulse current, consequently reducing the load inductance. As the time delay between the main and prepulse current increased, the intensity of the radiation source further increased. This study provides an approach for controlling radiation sources and enables different applications of X-pinches with adjustable prepulse current.

Hoffa’s Fracture Case Report

Unicondylar fractures of the distal third of the femur in the coronal plane are known as Hoffa’s fractures, they are rare and very rare. Few cases have been reported of this type of fracture, by definition they are unstable fractures and therefore require surgical treatment. The mechanism of trauma in this type of fracture is direct with the knee in flexion. Often they can go unnoticed in the Anteroposterior (AP) X-ray projection, which is why it is important to assess the lateral projection. When they are suspected or highlighted, it is necessary to pass a Computed Tomography (CT) in order to clearly define its surgical resolution, since the approach and method of fixation are controversial. Although today the availability and characteristics of different types of implants have increased, there is no consensus in the literature mainly due to the lack of expertise in these rare cases.

Reducing radiation dose for NN-based COVID-19 detection in helical chest CT using real-time monitored reconstruction.

Computed tomography is a powerful tool for medical examination, which plays a particularly important role in the investigation of acute diseases, such as COVID-19. A growing concern in relation to CT scans is the radiation to which the patients are exposed, and a lot of research is dedicated to methods and approaches to how to reduce the radiation dose in X-ray CT studies. In this paper, we propose a novel scanning protocol based on real-time monitored reconstruction for a helical chest CT using a pre-trained neural network model for COVID-19 detection as an expert. In a simulated study, for the first time, we proposed using per-slice stopping rules based on the COVID-19 detection neural network output to reduce the frequency of projection acquisition for portions of the scanning process. The proposed method allows reducing the total number of X-ray projections necessary for COVID-19 detection, and thus reducing the radiation dose, without a significant decrease in the prediction accuracy. The proposed protocol was evaluated on 163 patients from the COVID-CTset dataset, providing a mean dose reduction of 15.1% while the mean decrease in prediction accuracy amounted to only 1.9% achieving a Pareto improvement over a fixed protocol.

Sparse-view synchrotron X-ray tomographic reconstruction with learning-based sinogram synthesis.

Synchrotron radiation can be used as a light source in X-ray microscopy to acquire a high-resolution image of a microscale object for tomography. However, numerous projections must be captured for a high-quality tomographic image to be reconstructed; thus, image acquisition is time consuming. Such dense imaging is not only expensive and time consuming but also results in the target receiving a large dose of radiation. To resolve these problems, sparse acquisition techniques have been proposed; however, the generated images often have many artefacts and are noisy. In this study, a deep-learning-based approach is proposed for the tomographic reconstruction of sparse-view projections that are acquired with a synchrotron light source; this approach proceeds as follows. A convolutional neural network (CNN) is used to first interpolate sparse X-ray projections and then synthesize a sufficiently large set of images to produce a sinogram. After the sinogram is constructed, a second CNN is used for error correction. In experiments, this method successfully produced high-quality tomography images from sparse-view projections for two data sets comprising Drosophila and mouse tomography images. However, the initial results for the smaller mouse data set were poor; therefore, transfer learning was used to apply the Drosophila model to the mouse data set, greatly improving the quality of the reconstructed sinogram. The method could be used to achieve high-quality tomography while reducing the radiation dose to imaging subjects and the imaging time and cost.

Flash X-ray radiography of internal damage in soft cellular materials during instrumented dynamic penetration

Processes of internal damage development during localized dynamic penetration represent a crucial mechanism important for relevant analysis of deformation and failure of plates and sandwich panels under high strain rate conditions. Soft cellular materials are of special importance as the internal damage defines mode of collapse and energy absorption capabilities. In this paper, a fast X-ray radiography is employed for in-situ analysis of the internal damage development in soft closed-cell aluminum foam subjected to a localized high strain rate penetration using an instrumented projectile in a direct impact Hopkinson bar apparatus. The process with a typical duration of a few milliseconds is visualized using four X-ray projections acquired using a flash X-ray system and a high-speed camera. Internal damage such as cracking, shear failure in the vicinity of the projectile, and compaction of the material is successfully identified. This unique method utilizing a laboratory based X-ray source allows for characterization of the penetration mechanism that has been usually analyzed only in post-mortem state.

Volumetric tumor tracking from a single cone-beam X-ray projection image enabled by deep learning

Radiotherapy serves as a pivotal treatment modality for malignant tumors. However, the accuracy of radiotherapy is significantly compromised due to respiratory-induced fluctuations in the size, shape, and position of the tumor. To address this challenge, we introduce a deep learning-anchored, volumetric tumor tracking methodology that employs single-angle X-ray projection images. This process involves aligning the intraoperative two-dimensional (2D) X-ray images with the pre-treatment three-dimensional (3D) planning Computed Tomography (CT) scans, enabling the extraction of the 3D tumor position and segmentation. Prior to therapy, a bespoke patient-specific tumor tracking model is formulated, leveraging a hybrid data augmentation, style correction, and registration network to create a mapping from single-angle 2D X-ray images to the corresponding 3D tumors. During the treatment phase, real-time X-ray images are fed into the trained model, producing the respective 3D tumor positioning. Rigorous validation conducted on actual patient lung data and lung phantoms attests to the high localization precision of our method at lowered radiation doses, thus heralding promising strides towards enhancing the precision of radiotherapy.

Cone-beam pair-wise data consistency conditions in helical CT.

Data consistency conditions (DCC) are mathematical equations characterizing the redundancy in X-ray projections. They have been used to correct inconsistent projections before computed tomography (CT) reconstruction. This article investigates DCC for a helical acquisition with a cylindrical detector, the geometry of most diagnostic CT scanners. The acquired projections are analyzed pair-by-pair. The intersection of each plane containing the two source positions with the corresponding cone-beams defines two fan-beams for which a DCC can be computed. Instead of rebinning the two fan-beam projections to a conventional detector, we directly derive the DCC in detector coordinates. If the line defined by two source positions intersects the field-of-view (FOV), the DCC presents a singularity which is accounted for in our numerical implementation to increase the number of DCC compared to previous approaches which excluded these pairs of source positions. Axial truncation of the projections is addressed by identifying for which set of planes containing the two source positions the fan-beams are not truncated. The ability of these DCC to detect breathing motion has been evaluated on simulated and real projections. Our results indicate that the DCC can detect motion if the baseline and the FOV do not intersect. If they do, the inconsistency due to motion is dominated by discretization errors and noise. We therefore propose to normalize the inconsistency by the noise to obtain a noise-aware metric which is mostly sensitive to inconsistencies due to motion. Combined with a moving average to reduce noise, the derived DCC can detect breathing motion.

Real-Time 3D Liver Tumor Localization via Combined Optical Surface Imaging and an X-Ray Projection from Arbitrary Imaging Angles

A deep learning (DL)-based, deformable registration-driven liver tumor localization technique was developed for onboard deformable motion tracking. The technique (Surf-X360-Bio) uses in-room optical surface imaging and an x-ray projection at an arbitrary scan angle to solve volumetric liver and liver tumor motion in real-time. Surf-X360-Bio solves the volumetric motion of the liver and localizes the liver tumor, through deforming liver and liver tumor meshes segmented on prior 4D-CTs/CBCTs. It uses real-time onboard information from an optical surface image and a simultaneously-acquired x-ray projection (from an arbitrary scan angle). Surf-X360-Bio localizes tumors via two steps: liver boundary motion estimation and intra-liver motion derivation. Surf-X360-Bio first estimates liver boundary motion by a patient-specific surface imaging model (Surf), utilizing the correlation between the external body surface motion and the internal liver boundary motion. As the correlation can be imperfect, the residual motion estimation errors were corrected by a patient-specific, angle-agnostic x-ray imaging model (X360). X360 deformed the liver boundary to match motion-related imaging features encoded in an arbitrarily-angled x-ray projection, using the Surf output for initialization. X360 adopted a geometry-aware learning module to extract and adapt to angle-varying features of arbitrarily-angled x-ray images, by calculating the projection system matrix of each x-ray image on-the-fly during model training and inference. After the liver boundary motion estimation by Surf and X360, intra-liver deformation was solved by a biomechanical model (Bio) to propagate the liver boundary motion inside to localize the tumors. The DL-based Bio model used domain knowledge of tissue biomechanics and finite element analysis (FEA) to solve intra-liver motion, with a much faster speed than conventional FEA methods to meet the real-time constraint. Surf-X360-Bio was evaluated using a dataset of 34 liver patients. Liver tumor localization accuracy was evaluated by the Dice similarity coefficient (DSC), the 95-percentile Hausdorff distance (HD95), and the center-of-mass error (COME). Using 3,306 motion scenarios spanning the 360 degree x-ray scan angles for each testing patient, Surf-X360-Bio localized the liver tumors to 0.81 (mean) ± 0.16 (s.d.) in DSC, 2.5 ± 1.7 mm in HD95, and 2.1 ± 1.8 mm in COME. In comparison, the prior reference image without deformable registration-driven localization yielded 0.42 ± 0.29 in DSC, 8.1 ± 5.2 mm in HD95, and 8.5 ± 5.2 mm in COME. Via Surf-X360-Bio, the overall inference time was below 230 ms for each case. Combining optical surface imaging and x-ray imaging, Surf-X360-Bio achieved fast (<250 ms inference time), accurate (mean error < 2.1 mm), and robust liver tumor localizations at arbitrary x-ray scan angles for real-time, marker-less 3D deformable motion tracking.

Online dynamic flat-field correction for MHz microscopy data at European XFEL.

The high pulse intensity and repetition rate of the European X-ray Free-Electron Laser (EuXFEL) provide superior temporal resolution compared with other X-ray sources. In combination with MHz X-ray microscopy techniques, it offers a unique opportunity to achieve superior contrast and spatial resolution in applications demanding high temporal resolution. In both live visualization and offline data analysis for microscopy experiments, baseline normalization is essential for further processing steps such as phase retrieval and modal decomposition. In addition, access to normalized projections during data acquisition can play an important role in decision-making and improve the quality of the data. However, the stochastic nature of X-ray free-electron laser sources hinders the use of standard flat-field normalization methods during MHz X-ray microscopy experiments. Here, an online (i.e. near real-time) dynamic flat-field correction method based on principal component analysis of dynamically evolving flat-field images is presented. The method is used for the normalization of individual X-ray projections and has been implemented as a near real-time analysis tool at the Single Particles, Clusters, and Biomolecules and Serial Femtosecond Crystallography (SPB/SFX) instrument of EuXFEL.

Improvement of imaging and image correction methods for the soft X-ray projection microscopy.

The soft X-ray projection microscope has been developed for high resolution imaging of hydrated bio-specimens. Image blurring due to X-ray diffraction can be corrected by an iteration procedure. The correction is not efficient enough for all images, especially for low contrast chromosome images. The purpose of this study is to improve X-ray imaging techniques using a finer pinhole and reducing capture time, as well as to improve image correction methods. A method of specimen staining prior to the imaging was tested in order to capture images with high contrasts. The efficiency of the iteration procedure and its combined version with an image enhancement method was also assessed. In image correction, we used the iteration procedure and its combined version with an image enhancement technique. To capture higher contrast images, we stained chromosome specimens with the Platinum blue (Pt-blue) prior to the imaging. The iteration procedure combined with image enhancement corrected the chromosome images with 329 or lower magnification effectively. Using the Pt-blue staining for the chromosome, images with high contrast have been captured and successfully corrected. The image enhancement technique combining contrast enhancement and noise removal together was effective to obtain higher contrast images. As a result, the chromosome images with 329 or lower times magnification were corrected effectively. With Pt-blue staining, chromosome images with contrasts of 2.5 times higher than unstained case could be captured and corrected by the iteration procedure.

Comparison of typical radiation doses and risks using an anthropomorphic 'bone fracture' phantom for commonly performed X-ray projections in a 5-year-old.

Diagnostic reference levels (DRLs) are typical dose levels for medical imaging examinations for groups of standard-sized patients or standard phantoms for broadly defined types of equipment used as a tool to aid optimisation of protection for medical exposures. Currently, there are no paediatric DRLs for conventional radiography (i.e. general X-rays) published in Australia. The aim of this study was to establish typical radiation doses and risks that are representative of those delivered for commonly performed X-ray projections for a 5-year-old/20 kg child using a 5-year-old anthropomorphic 'bone fracture' phantom in three dedicated paediatric radiology departments in Victoria. A total of 20 projection images were acquired for a standard 5-year-old/20 kg phantom using digital radiography X-ray equipment. The air kerma-area product (KAP) measured at each centre by a KAP metre, which was calibrated to a national primary standard, was considered to represent the median value for that centre for each X-ray projection. Organ doses and effective dose were estimated using PCXMC software, and risks of radiation-induced cancer and radiation-induced death were calculated based on the BEIR VII report. The typical doses for the individual X-ray projections ranged from 3 mGy•cm2 to 86 mGy•cm2 , whilst the effective doses ranged from 0.00004 to 0.07 mSv. The radiation risks were 'minimal' to 'negligible'. The estimation of typical radiation doses and associated risks for a 5-year-old/20 kg phantom study provides reference values for guidance and is a first step in assisting optimisation at other institutions until national DRLs, based on patient data from the clinical setting, are published.

A cascade-based dual-domain data correction network for sparse view CT image reconstruction

Computed tomography (CT) provides non-invasive anatomical structures of the human body and is also widely used for clinical diagnosis, but excessive ionizing radiation in X-rays can cause harm to the human body. Therefore, the researchers obtained sparse sinograms reconstructed sparse view CT images (SVCT) by reducing the amount of X-ray projection, thereby reducing the radiological effects caused by radiation. This paper proposes a cascade-based dual-domain data correction network (CDDCN), which can effectively combine the complementary information contained in the sinogram domain and the image domain to reconstruct high-quality CT images from sparse view sinograms. Specifically, several encoder-decoder subnets are cascaded in the sinogram domain to reconstruct artifact-free and noise-free CT images. In the encoder-decoder subnets, spatial-channel domain learning is designed to achieve efficient feature fusion through a group merging structure, providing continuous and elaborate pixel-level features and improving feature extraction efficiency. At the same time, to ensure that the original sinogram data collected can be retained, a sinogram data consistency layer is proposed to ensure the fidelity of the sinogram data. To further maintain the consistency between the reconstructed image and the reference image, a multi-level composite loss function is designed for regularization to compensate for excessive smoothing and distortion of the image caused by pixel loss and preserve image details and texture. Quantitative and qualitative analysis shows that CDDCN achieves competitive results in artifact removal, edge preservation, detail restoration, and visual improvement for sparsely sampled data under different views.

Deep Fusion Network Based Sparse View CT Reconstructions for Clinical Diagnostic Scanners.

Sparse view CT scan has the advantage of reducing radiation exposure and scanning time in clinical diagnosis. However, the limited number of x-ray projections can make the reconstruction problem ill posed and result in image artifacts. To tackle the problem, we propose a novel model-based deep fusion network(DFN) satisfying the clinical set-up. It extracts fused features encoded from both the sinogram and the preliminary reconstructed image generated by filtered back projection (FBP) to improve the quality of reconstruction. The preliminary reconstructed image endows fused features with prior knowledge that facilitate the convergence of neural network to high-quality reconstruction images. We design a custom loss for training that enforces the network to learn both the pixel value and the integrity of the tissue structure. A synthetic sparse view breast CT dataset from American Association of Physicists in Medicine(AAPM) is used for training, validation and testing. The qualitative and quantitative evaluations show that the DFN reconstruction algorithm significantly improves in balancing between the image quality and reconstruction speed, hence enables fast and high quality CT reconstruction despite the sparse view limitations.

Real-time 3D reconstruction of guidewires and stents using two update X-ray projections in a rotating imaging setup.

Vascular diseases are often treated minimally invasively. The interventional material (stents, guidewires, etc.) used during such percutaneous interventions are visualized by some form of image guidance. Today, this image guidance is usually provided by 2D X-ray fluoroscopy, that is, a live 2D image. 3D X-ray fluoroscopy, that is, a live 3D image, could accelerate existing and enable new interventions. However, existing algorithms for the 3D reconstruction of interventional material require either too many X-ray projections and therefore dose, or are only capable of reconstructing single, curvilinearstructures. Using only two new X-ray projections per 3D reconstruction, we aim to reconstruct more complex arrangements of interventional material than was previously possible. This is achieved by improving a previously presented deep learning-based reconstruction pipeline, which assumes that the X-ray images are acquired by a continuously rotating biplane system, in two ways: (a) separation of the reconstruction of different object types, (b) motion compensation using spatial transformernetworks. Our pipeline achieves submillimeter accuracy on measured data of a stent and two guidewires inside an anthropomorphic phantom with respiratory motion. In an ablation study, we find that the aforementioned algorithmic changes improve our two figuresof merit by 75% (1.76mm → 0.44mm) and 59% (1.15mm → 0.47mm) respectively. A comparison of our measured dose area product (DAP) rate to DAP rates of 2D fluoroscopy indicates a roughly similar doseburden. This dose efficiency combined with the ability to reconstruct complex arrangements of interventional material makes the presented algorithm a promising candidate to enable 3Dfluoroscopy.

The cost of perfection: An investigation into the unnecessary rejection of clinically acceptable lateral wrist imaging.

This study illustrates image rejection rates of the lateral wrist x-ray projection at a large, public teaching hospital. Rejected images were evaluated to determine the number of images that needed to be repeated based on the clinical indication. This study highlights the difference in subjective image-repeat decision-making skills existing between radiologists, experienced radiographers and junior radiographers. A retrospective review was conducted of all rejected lateral wrist x-ray images by a panel of three radiologists, three experienced radiographers and six junior radiographers. This review aimed to determine if rejected imaging met the consideration of the clinical indication and assumed appropriate acquisition of an orthogonal projection. A complement of images that had not been rejected were included in the review to create a blinded study. The review demonstrated 85.8% of rejected images were deemed to meet clinical requirements according to radiologists. The experienced radiographers agreed with radiologists regarding 75.3% of images. Junior radiographers agreed with radiologists in 34.2% of cases. Junior radiographers were three times more likely to seek repeat imaging than the radiologists and experienced radiographers. This review demonstrated a lateral wrist projection reject rate of 38.7% with unnecessary repeats according to clinical indications in 85.8% of cases. The review of experienced radiographers was comparable to radiologists; however, the difference in decision-making skills was evident in the junior radiographers. This highlights an alarming trend, should similar results be demonstrated at other health services and indicates an unnecessary burden to clinical practice. Inclusion of clinical reasoning for imaging and the need for repeat imaging is recommended for radiography training programs.