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Aim: To assess the feasibility of immediate (IIP), early (EIP) and delayed implant placement (DIP) for single tooth replacement in the premaxilla on the basis of the complete indication area of each approach in routine practice. Materials and Methods: Data from 100 patients (59 women, 41 men, all Caucasians) aged between 19 and 81 years old (mean age 51.71) who had been consecutively treated with a single implant in the premaxilla (13–23) in one private periodontal practice were retrospectively collected. Demographic data, diagnostic information and linear measurements were extracted from patient files and CBCTs. The feasibility of IIP, EIP and DIP was assessed for all cases by both authors, based on the following criteria: availability of apical bone, position of the tooth in relation to the morphology of the alveolar process, buccal bone morphology and presence of midfacial recession. The reasons for not being able to perform an approach, and the viable alternatives for each approach were secondary outcomes. DIP was considered to have been preceded by alveolar ridge preservation (ARP). Results: Ninety‐two patients could be treated by means of IIP, EIP or DIP. In eight patients none of these approaches were possible as they required bone augmentation prior to implant placement. Fifty‐two patients (95% CI: 42%–62%) could by treated with IIP, 58 (95% CI: 48%–67%) with EIP and 88 (95% CI: 80%–93%) with DIP. The feasibility proportions of IIP and EIP were significantly lower than the one of DIP (p < 0.001). All patients who could be treated with IIP could also be treated with EIP or DIP. Lack of apical bone for implant anchorage was the main reason for not being able to perform IIP and EIP. Complete loss of the buccal bone wall and the need for bone augmentation prior to implant placement were the reasons for not being able to perform DIP. Conclusion: From the results of this retrospective CBCT analysis, DIP is nearly always possible in contrast to IIP and EIP. Therefore, and since it is much easier than IIP and EIP, inexperienced clinicians should mainly focus on ARP and DIP in clinical practice deferring IIP and EIP until more surgical skills have been acquired. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusion STereotactic Arrhythmia Radioablation (STAR) is a novel noninvasive method for treating arrythmias in which external beam radiation is directed towards subregions of the heart. Challenges for accurate STAR targeting include small target volumes and relatively large patient motion, which can lead to radiation related patient toxicities. 4D Cone‐beam CT (CBCT) images are used for stereotactic lung treatments to account for respiration‐related patient motion. 4D‐CBCT imaging could similarly be used to account for respiration‐related patient motion in STAR; however, the poor contrast of heart tissue in CBCT makes identifying cardiac substructures in 4D‐CBCT images challenging. If cardiac structures can be identified in pre‐treatment 4D‐CBCT images, then the location of the target volume can be more accurately identified for different phases of the respiration cycle, leading to more accurate targeting and a reduction in patient toxicities.The aim of this simulation study is to investigate the accuracy of different cardiac substructure segmentation methods for 4D‐CBCT images.Repeat 4D‐CT scans from 13 lung cancer patients were obtained from The Cancer Imaging Archive. Synthetic 4D‐CBCT images for each patient were simulated by forward projecting and reconstructing each respiration phase of a chosen “testing” 4D‐CT scan. Eighteen cardiac structures were segmented from each respiration phase image in the testing 4D‐CT using the previously validated platipy toolkit. The platipy segmentations from the testing 4D‐CT were defined as the ground truth segmentations for the synthetic 4D‐CBCT images. Five different 4D‐CBCT cardiac segmentation methods were investigated: 3D Rigid Alignment, 4D Rigid Alignment, Direct CBCT Segmentation, Contour Transformation, and Synthetic CT Segmentation methods. For all methods except the Direct CBCT segmentation method, a separate 4D‐CT (Planning CT) was used to assist in generating 4D‐CBCT segmentations. Segmentation performance was measured using the Dice similarity coefficient (DSC), Hausdorff distance (HD), mean surface distance (MSD), and volume ratio (VR) metrics.The mean ± standard deviation DSC for all cardiac substructures for the 3D Rigid Alignment, 4D Rigid Alignment, Direct CBCT Segmentation, Contour Transformation, and Synthetic CT Segmentation methods were 0.48 ± 0.29, 0.52 ± 0.29, 0.37 ± 0.32, 0.53 ± 0.29, 0.57 ± 0.28, respectively. Similarly, the HD values were 10.9 ± 3.6 , 9.9 ± 2.6 , 17.3 ± 5.3 , 9.9 ± 2.8 , 9.3 ± 3.0 mm, the MSD values were 2.9 ± 0.6 , 2.9 ± 0.6 , 6.3 ± 2.5 , 2.5 ± 0.6 , 2.4 ± 0.8 mm, and the VR Values were 0.81 ± 0.12, 0.78 ± 0.14, 1.10 ± 0.47, 0.72 ± 0.15, 0.98 ± 0.44, respectively. Of the five methods investigated the Synthetic CT segmentation method generated the most accurate segmentations for all calculated segmentation metrics.This simulation study investigates the accuracy of different cardiac substructure segmentation methods for 4D‐CBCT images. Accurate 4D‐CBCT cardiac segmentation will provide more accurate information on the location of cardiac anatomy during STAR treatments which can lead to safer and more effective STAR. As the data and segmentation methods used in this study are all open source, this study provides a useful benchmarking tool to evaluate other CBCT cardiac segmentation methods. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the feasibility of an open‐source, semi‐automated, and reproducible vertex placement tool to improve the efficiency of lattice radiotherapy (LRT) planning. We used polymer gel dosimetry with a Cone Beam CT (CBCT) readout to commission this LRT technique. Material and methods: We generated a volumetric modulated arc therapy (VMAT)‐based LRT plan on a 2 L NIPAM polymer gel dosimeter using our Eclipse Acuros version 15.6 AcurosXB beam model, and also recalculated the plan with a pre‐clinical Acuros v18.0 dose calculation algorithm with the enhanced leaf modelling (ELM). With the assistance of the MAAS‐SFRThelper software, a lattice vertex diameter of 1.5 cm and center‐to‐center spacing of 3 cm were used to place the spheres in a hexagonal, closed packed structure. The verification plan included four gantry arcs with 15°, 345°, 75°, 105° collimator angles. The spheres were prescribed 20 Gy to 50% of their combined volume. The 6 MV Flattening Filter Free beam energy was used to deliver the verification plan. The dosimetric accuracy of the LRT delivery was evaluated with 1D dose profiles, 2D isodose maps, and a 3D global gamma analysis. Results: Qualitative comparisons between the 1D dose profiles of the Eclipse plan and measured gel showed good consistency at the prescription dose mark. The average diameter measured 13.3 ± 0.2 mm (gel for v15.6), 12.6 mm (v15.6 plan), 13.1 ± 0.2 mm (gel for v18.0), and 12.3 mm (v18.0 plan). 3D gamma analysis showed that all gamma pass percent were > 95% except at 1% and 2% at the 1 mm distance to agreement criteria. Conclusion: This study presents a novel application of gel dosimetry in verifying the dosimetric accuracy of LRT, achieving excellent 3D gamma results. The treatment planning was facilitated by publicly available software that automatically placed the vertices for consistency and efficiency. [ABSTRACT FROM AUTHOR]

Many scenarios in dental clinical diagnosis and treatment require the segmentation and identification of a specific tooth or the entire dentition in cone‐beam computed tomography (CBCT) images. However, traditional segmentation methods struggle to ensure accuracy. In recent years, there has been significant progress in segmentation algorithms based on deep learning, garnering considerable attention. Inspired by models from present neuro networks such as UCTransNet and DC‐Unet, this study proposes an MRCM‐UCTransNet for accurate three‐dimensional tooth segmentation from cone‐beam CT images. To enhance feature extraction while preserving the multi‐head attention mechanism, a multi‐scale residual convolution module (MRCM) is integrated into the UCTransNet architecture. This modification addresses the limitations of traditional segmentation methods and aims to improve accuracy in tooth segmentation from CBCT images. Comparative experiments indicate that, in the situation with a specific image size and small data volume, the proposed method exhibits certain advantages in segmentation accuracy and precision. Compared to traditional Unet approaches, MRCM‐UCTransNet's dice accuracy is improved by 7%, while its sensitivity is improved by about 10%. These findings highlight the efficacy of the proposed approach, particularly in scenarios with specific image size constraints and limited data availability. The proposed MRCM‐UCTransNet algorithm integrates the latest architectural advancements in the Unet model which achieves effective segmentation of six types of teeth within the tooth. It was proved to be efficient for image segmentation on small datasets, requiring less training time and fewer parameters. [ABSTRACT FROM AUTHOR]

Background: Cone‐beam CT (CBCT) has been extensively employed in industrial and medical applications, such as image‐guided radiotherapy and diagnostic imaging, with a growing demand for quantitative imaging using CBCT. However, conventional CBCT can be easily compromised by scatter and beam hardening artifacts, and the entanglement of scatter and spectral effects introduces additional complexity. Purpose: The intertwined scatter and spectral effects within CBCT pose significant challenges to the quantitative performance of spectral imaging. In this work, we present the first attempt to develop a stationary spectral modulator with flying focal spot (SMFFS) technology as a promising, low‐cost approach to accurately solving the x‐ray scattering problem and physically enabling spectral imaging in a unified framework, and with no significant misalignment in data sampling of spectral projections. Methods: To deal with the intertwined scatter‐spectral challenge, we propose a novel scatter‐decoupled material decomposition (SDMD) method for SMFFS, which consists of four steps in total, including (1) spatial resolution‐preserved and noise‐suppressed multi‐energy "residual" projection generation free from scatter, based on a hypothesis of scatter similarity; (2) first‐pass material decomposition from the generated multi‐energy residual projections in non‐penumbra regions, with a structure similarity constraint to overcome the increased noise and penumbra effect; (3) scatter estimation for complete data; and (4) second‐pass material decomposition for complete data by using a multi‐material spectral correction method. Monte Carlo simulations of a pure‐water cylinder phantom with different focal spot deflections are conducted to validate the scatter similarity hypothesis. Both numerical simulations using a clinical abdominal CT dataset, and physics experiments on a tabletop CBCT system using a Gammex multi‐energy CT phantom and an anthropomorphic chest phantom, are carried out to demonstrate the feasibility of CBCT spectral imaging with SMFFS and our proposed SDMD method. Results: Monte Carlo simulations show that focal spot deflections within a range of 2 mm share quite similar scatter distributions overall. Numerical simulations demonstrate that SMFFS with SDMD method can achieve better material decomposition and CT number accuracy with fewer artifacts. In physics experiments, for the Gammex phantom, the average error of the mean values (ERMSEROI$E^{\text{ROI}}_{\text{RMSE}}$) in selected regions of interest (ROIs) of virtual monochromatic image (VMI) at 70 keV is 8 HU in SMFFS cone‐beam (CB) scan, and 19 and 210 HU in sequential 80/120 kVp (dual kVp, DKV) CB scan with and without scatter correction, respectively. For the chest phantom, the ERMSEROI$E^{\text{ROI}}_{\text{RMSE}}$ in selected ROIs of VMIs is 12 HU for SMFFS CB scan, and 15 and 438 HU for sequential 80/140 kVp CB scan with and without scatter correction, respectively. Also, the non‐uniformity among selected regions of the chest phantom is 14 HU for SMFFS CB scan, and 59 and 184 HU for the DKV CB scan with and without a traditional scatter correction method, respectively. Conclusions: We propose a SDMD method for CBCT with SMFFS. Our preliminary results show that SMFFS can enable spectral imaging with simultaneous scatter correction for CBCT and effectively improve its quantitative imaging performance. [ABSTRACT FROM AUTHOR]

Background: Recent studies have shown a clear relationship between absorbed dose and tumor response to treatment after hepatic radioembolization. These findings help to create more personalized treatment planning and dosimetry. However, crucial to this goal is the ability to predict the dose distribution prior to treatment. The microsphere distribution is ultimately determined by (i) the hepatic vasculature and the resulting blood flow dynamics and (ii) the catheter position. Purpose: To show that pretreatment, intra‐procedural imaging of blood flow patterns, as quantified by catheter‐directed intra‐arterial contrast enhancement, correlate with posttreatment microsphere accumulation and, consequently, absorbed dose. Materials and methods: Patients who participated in a clinical trial (NCT01177007) and for whom both a pretreatment dual‐phase contrast‐enhanced cone‐beam CT (CBCT) and a posttreatment 90Y PET/CT scan were available were included in this retrospective study. Tumors and perfused volumes were manually delineated on the CBCT by an experienced radiologist. The mean, sum, and standard deviation of the voxels in each volume were recorded. The delineations were transferred to the PET‐based absorbed dose maps by coregistration of the corresponding CTs. Linear multiple regression was used to correlate pretreatment CBCT enhancement to posttreatment 90Y PET/CT‐based absorbed dose in each region. Leave‐one‐out cross‐validation and Bland–Altman analyses were performed on the predicted versus measured absorbed doses. Results: Nine patients, with a total of 23 tumors were included. All presented with hepatocellular carcinoma (HCC). Visually, all patients had a clear correspondence between CBCT enhancement and absorbed dose. The correlation between CBCT enhancement and posttherapy absorbed tumor dose based was strong (R2 = 0.91), and moderate for the non‐tumor liver tissue (R2 = 0.61). Limits of agreement were approximately ±55 Gray for tumor tissue. Conclusion: There is a linear relationship between pretreatment blood dynamics in HCC tumors and posttreatment absorbed dose, which, if shown to be generalizable, allows for pretreatment tumor absorbed dose prediction. [ABSTRACT FROM AUTHOR]

Background: Dual‐energy (DE) detection of bone marrow edema (BME) would be a valuable new diagnostic capability for the emerging orthopedic cone‐beam computed tomography (CBCT) systems. However, this imaging task is inherently challenging because of the narrow energy separation between water (edematous fluid) and fat (health yellow marrow), requiring precise artifact correction and dedicated material decomposition approaches. Purpose: We investigate the feasibility of BME assessment using kV‐switching DE CBCT with a comprehensive CBCT artifact correction framework and a two‐stage projection‐ and image‐domain three‐material decomposition algorithm. Methods: DE CBCT projections of quantitative BME phantoms (water containers 100–165 mm in size with inserts presenting various degrees of edema) and an animal cadaver model of BME were acquired on a CBCT test bench emulating the standard wrist imaging configuration of a Multitom Rax twin robotic x‐ray system. The slow kV‐switching scan protocol involved a 60 kV low energy (LE) beam and a 120 kV high energy (HE) beam switched every 0.5° over a 200° angular span. The DE CBCT data preprocessing and artifact correction framework consisted of (i) projection interpolation onto matched LE and HE projections views, (ii) lag and glare deconvolutions, and (iii) efficient Monte Carlo (MC)‐based scatter correction. Virtual non‐calcium (VNCa) images for BME detection were then generated by projection‐domain decomposition into an Aluminium (Al) and polyethylene basis set (to remove beam hardening) followed by three‐material image‐domain decomposition into water, Ca, and fat. Feasibility of BME detection was quantified in terms of VNCa image contrast and receiver operating characteristic (ROC) curves. Robustness to object size, position in the field of view (FOV) and beam collimation (varied 20–160 mm) was investigated. Results: The MC‐based scatter correction delivered > 69% reduction of cupping artifacts for moderate to wide collimations (> 80 mm beam width), which was essential to achieve accurate DE material decomposition. In a forearm‐sized object, a 20% increase in water concentration (edema) of a trabecular bone‐mimicking mixture presented as ∼15 HU VNCa contrast using 80–160 mm beam collimations. The variability with respect to object position in the FOV was modest (< 15% coefficient of variation). The areas under the ROC curve were > 0.9. A femur‐sized object presented a somewhat more challenging task, resulting in increased sensitivity to object positioning at 160 mm collimation. In animal cadaver specimens, areas of VNCa enhancement consistent with BME were observed in DE CBCT images in regions of MRI‐confirmed edema. Conclusion: Our results indicate that the proposed artifact correction and material decomposition pipeline can overcome the challenges of scatter and limited spectral separation to achieve relatively accurate and sensitive BME detection in DE CBCT. This study provides an important baseline for clinical translation of musculoskeletal DE CBCT to quantitative, point‐of‐care bone health assessment. [ABSTRACT FROM AUTHOR]

Cone‐beam computed tomography (CBCT) system can provide real‐time 3D images and fluoroscopy images of the region of interest during the operation. Some systems can even offer augmented fluoroscopy and puncture guidance. The use of CBCT for interventional pulmonary procedures has grown significantly in recent years, and numerous clinical studies have confirmed the technology's efficacy and safety in the diagnosis, localization, and treatment of pulmonary nodules. In order to optimize and standardize the technical specifications of CBCT and guide its application in clinical practice, the consensus statement has been organized and written in a collaborative effort by the Professional Committee on Interventional Pulmonology of China Association for Promotion of Health Science and Technology. [ABSTRACT FROM AUTHOR]

Background: The bowtie‐filter in cone‐beam CT (CBCT) causes spatially nonuniform x‐ray beam often leading to eclipse artifacts in the reconstructed image. The artifacts are further confounded by the patient scatter, which is therefore patient‐dependent as well as system‐specific. Purpose: In this study, we propose a dual‐domain network for reducing the bowtie‐filter‐induced artifacts in CBCT images. Methods: In the projection domain, the network compensates for the filter‐induced beam‐hardening that are highly related to the eclipse artifacts. The output of the projection‐domain network was used for image reconstruction and the reconstructed images were fed into the image‐domain network. In the image domain, the network further reduces the remaining cupping artifacts that are associated with the scatter. A single image‐domain‐only network was also implemented for comparison. Results: The proposed approach successfully enhanced soft‐tissue contrast with much‐reduced image artifacts. In the numerical study, the proposed method decreased perceptual loss and root‐mean‐square‐error (RMSE) of the images by 84.5% and 84.9%, respectively, and increased the structure similarity index measure (SSIM) by 0.26 compared to the original input images on average. In the experimental study, the proposed method decreased perceptual loss and RMSE of the images by 87.2% and 92.1%, respectively, and increased SSIM by 0.58 compared to the original input images on average. Conclusions: We have proposed a deep‐learning‐based dual‐domain framework to reduce the bowtie‐filter artifacts and to increase the soft‐tissue contrast in CBCT images. The performance of the proposed method has been successfully demonstrated in both numerical and experimental studies. [ABSTRACT FROM AUTHOR]

Background: Convolutional neural networks (CNNs) have shown promising results in image denoising tasks. While most existing CNN‐based methods depend on supervised learning by directly mapping noisy inputs to clean targets, high‐quality references are often unavailable for interventional radiology such as cone‐beam computed tomography (CBCT). Purpose: In this paper, we propose a novel self‐supervised learning method that reduces noise in projections acquired by ordinary CBCT scans. Methods: With a network that partially blinds input, we are able to train the denoising model by mapping the partially blinded projections to the original projections. Additionally, we incorporate noise‐to‐noise learning into the self‐supervised learning by mapping the adjacent projections to the original projections. With standard image reconstruction methods such as FDK‐type algorithms, we can reconstruct high‐quality CBCT images from the projections denoised by our projection‐domain denoising method. Results: In the head phantom study, we measure peak signal‐to‐noise ratio (PSNR) and structural similarity index measure (SSIM) values of the proposed method along with the other denoising methods and uncorrected low‐dose CBCT data for a quantitative comparison both in projection and image domains. The PSNR and SSIM values of our self‐supervised denoising approach are 27.08 and 0.839, whereas those of uncorrected CBCT images are 15.68 and 0.103, respectively. In the retrospective study, we assess the quality of interventional patient CBCT images to evaluate the projection‐domain and image‐domain denoising methods. Both qualitative and quantitative results indicate that our approach can effectively produce high‐quality CBCT images with low‐dose projections in the absence of duplicate clean or noisy references. Conclusions: Our self‐supervised learning strategy is capable of restoring anatomical information while efficiently removing noise in CBCT projection data. [ABSTRACT FROM AUTHOR]

Introduction: The ability to capture more anatomical detail in cone‐beam computed tomography (CBCT) imaging compared to kilovoltage (kV) and megavoltage (MV) imaging, has seen a documented shift towards CBCT image verification and staff adopting more extensive image analysis processes. The timeframe associated with assessing online CBCT images, termed the online decision analysis time, if drawn out, can affect treatment efficiency and accuracy. This study aimed to determine the current CBCT online decision analysis time at Radiation Oncology Princess Alexandra Ipswich Road (ROPAIR) and investigate the influence of isocentre shift magnitude and treatment site considerations on this timeframe. Methods: This retrospective clinical audit collected treatment parameters from 202 CBCT images over 2 treatment days. The online decision analysis time was calculated by subtracting the image acquisition timestamp from the image verification shift application timestamp. The quantitative data were analysed using mean, standard deviation, and range in the following categories: all CBCTs, CBCTs grouped by isocentre shift magnitude and CBCTs grouped by treatment site. Content analysis was performed on staff comments made during image analysis. Results: The average online decision analysis time was 2:37 ± 1:28 min. On average approximately, head and neck, spine and extremity treatment sites measured 1 min, pelvis, breast, and chest measured 2–3 min with abdomen measuring 4 min. Common categories reported in staff comments included anatomical changes, repositioning, and organs at risk size. Conclusion: The results provide baseline online decision analysis times. Further refinement is required to determine if the image match method, treatment site considerations, and rotational discrepancies influence this timeframe. [ABSTRACT FROM AUTHOR]

Image quality models based on cascaded systems analysis and task‐based imaging performance were an important aspect of the emergence of 2D and 3D digital x‐ray systems over the last 25 years. This perspective vignette offers cursory review of such developments and personal insights that may not be obvious within previously published scientific literature. The vignette traces such models to the mid‐1990s, when flat‐panel x‐ray detectors were emerging as a new base technology for digital radiography and benefited from the rigorous, objective characterization of imaging performance gained from such models. The connection of models for spatial resolution and noise to spatial‐frequency‐dependent descriptors of imaging task provided a useful framework for system optimization that helped to accelerate the development of new technologies to first clinical use. Extension of the models to new technologies and applications is also described, including dual‐energy imaging, photon‐counting detectors, phase contrast imaging, tomosynthesis, cone‐beam CT, 3D image reconstruction, and image registration. [ABSTRACT FROM AUTHOR]

Background: Image‐guided neurosurgery requires high localization and registration accuracy to enable effective treatment and avoid complications. However, accurate neuronavigation based on preoperative magnetic resonance (MR) or computed tomography (CT) images is challenged by brain deformation occurring during the surgical intervention. Purpose: To facilitate intraoperative visualization of brain tissues and deformable registration with preoperative images, a 3D deep learning (DL) reconstruction framework (termed DL‐Recon) was proposed for improved intraoperative cone‐beam CT (CBCT) image quality. Methods: The DL‐Recon framework combines physics‐based models with deep learning CT synthesis and leverages uncertainty information to promote robustness to unseen features. A 3D generative adversarial network (GAN) with a conditional loss function modulated by aleatoric uncertainty was developed for CBCT‐to‐CT synthesis. Epistemic uncertainty of the synthesis model was estimated via Monte Carlo (MC) dropout. Using spatially varying weights derived from epistemic uncertainty, the DL‐Recon image combines the synthetic CT with an artifact‐corrected filtered back‐projection (FBP) reconstruction. In regions of high epistemic uncertainty, DL‐Recon includes greater contribution from the FBP image. Twenty paired real CT and simulated CBCT images of the head were used for network training and validation, and experiments evaluated the performance of DL‐Recon on CBCT images containing simulated and real brain lesions not present in the training data. Performance among learning‐ and physics‐based methods was quantified in terms of structural similarity (SSIM) of the resulting image to diagnostic CT and Dice similarity metric (DSC) in lesion segmentation compared to ground truth. A pilot study was conducted involving seven subjects with CBCT images acquired during neurosurgery to assess the feasibility of DL‐Recon in clinical data. Results: CBCT images reconstructed via FBP with physics‐based corrections exhibited the usual challenges to soft‐tissue contrast resolution due to image non‐uniformity, noise, and residual artifacts. GAN synthesis improved image uniformity and soft‐tissue visibility but was subject to error in the shape and contrast of simulated lesions that were unseen in training. Incorporation of aleatoric uncertainty in synthesis loss improved estimation of epistemic uncertainty, with variable brain structures and unseen lesions exhibiting higher epistemic uncertainty. The DL‐Recon approach mitigated synthesis errors while maintaining improvement in image quality, yielding 15%–22% increase in SSIM (image appearance compared to diagnostic CT) and up to 25% increase in DSC in lesion segmentation compared to FBP. Clear gains in visual image quality were also observed in real brain lesions and in clinical CBCT images. Conclusions: DL‐Recon leveraged uncertainty estimation to combine the strengths of DL and physics‐based reconstruction and demonstrated substantial improvements in the accuracy and quality of intraoperative CBCT. The improved soft‐tissue contrast resolution could facilitate visualization of brain structures and support deformable registration with preoperative images, further extending the utility of intraoperative CBCT in image‐guided neurosurgery. [ABSTRACT FROM AUTHOR]

Background: Many dedicated cone‐beam CT (CBCT) systems have irregular scanning trajectories. Compared with the standard CBCT calibration, accurate calibration for CBCT systems with irregular trajectories is a more complex task, since the geometric parameters for each scanning view are variable. Most of the existing calibration methods assume that the intrinsic geometric relationship of the fiducials in the phantom is precisely known, and rarely delve deeper into the issue of whether the phantom accuracy is adapted to the calibration model. Purpose: A high‐precision phantom and a highly robust calibration model are interdependent and mutually supportive, and they are both important for calibration accuracy, especially for the high‐resolution CBCT. Therefore, we propose a calibration scheme that considers both accurate phantom measurement and robust geometric calibration. Methods: Our proposed scheme consists of two parts: (1) introducing a measurement model to acquire the accurate intrinsic geometric relationship of the fiducials in the phantom; (2) developing a highly noise‐robust nonconvex model‐based calibration method. The measurement model in the first part is achieved by extending our previous high‐precision geometric calibration model suitable for CBCT with circular trajectories. In the second part, a novel iterative method with optimization constraints based on a back‐projection model is developed to solve the geometric parameters of each view. Results: The simulations and real experiments show that the measurement errors of the fiducial ball bearings (BBs) are within the subpixel level. With the help of the geometric relationship of the BBs obtained by our measurement method, the classic calibration method can achieve good calibration based on far fewer BBs. All metrics obtained in simulated experiments as well as in real experiments on Micro CT systems with resolutions of 9 and 4.5 μm show that the proposed calibration method has higher calibration accuracy than the competing classic method. It is particularly worth noting that although our measurement model proves to be very accurate, the classic calibration method based on this measurement model can only achieve good calibration results when the resolution of the measurement system is close to that of the system to be calibrated, but our calibration scheme enables high‐accuracy calibration even when the resolution of the system to be calibrated is twice that of the measurement system. Conclusions: The proposed combined geometrical calibration scheme does not rely on a phantom with an intricate pattern of fiducials, so it is applicable in Micro CT with high resolution. The two parts of the scheme, the "measurement model" and the "calibration model," prove to be of high accuracy. The combination of these two models can effectively improve the calibration accuracy, especially in some extreme cases. [ABSTRACT FROM AUTHOR]

Background: In recent years, cone-beam computed tomography (CBCT) has played an important role in medical imaging.However, the applications of CBCT are limited due to the severe scatter contamination. Conventional Monte Carlo (MC) simulation can provide accurate scatter estimation for scatter correction, but the expensive computational cost has always been the bottleneck of MC method in clinical application. Purpose: In this work, an MC simulation method combined with a variance reduction technique called correlated sampling is proposed for fast iterative scatter correction. Methods: Correlated sampling exploits correlation between similar simulation systems to reduce the variance of interest quantities. Specifically, conventional MC simulation is first performed on the scatter-contaminated CBCT to generate the initial scatter signal. In the subsequent correction iterations, scatter estimation is then updated by applying correlated MC sampling to the latest corrected CBCT images by reusing the random number sequences of the task-related photons in conventional MC. Afterward, the corrected projections obtained by subtracting the scatter estimation from raw projections are utilized for FDK reconstruction. These steps are repeated until an adequate scatter correction is obtained. The performance of the proposed framework is evaluated by the accuracy of the scatter estimation, the quality of corrected CBCT images and efficiency. Results: Overall, the difference in mean absolute percentage error between scatter estimation with and without correlated sampling is 0.25% for full-fan case and 0.34% for half-fan case, respectively. In simulation studies, scatter artifacts are substantially eliminated, where the mean absolute error value is reduced from 15 to 2 HU in full-fan case and from 53 to 13 HU in half-fan case. Scatter-to-primary ratio is reduced to 0.02 for full-fan and 0.04 for half -fan, respectively. In phantom study, the contrast-to-noise ratio (CNR) is increased by a factor of 1.63, and the contrast is increased by a factor of 1.77. As for clinical studies, the CNR is improved by 11% and 14% for half-fan and full-fan, respectively. The contrast after correction is increased by 19% for half-fan and 44% for full-fan. Furthermore, root mean square error is also effectively reduced, especially from 78 to 4 HU for full-fan. Experimental results demonstrate that the figure of merit is improved between 23 and 43 folds when using correlated sampling.The proposed method takes less than 25 s for the whole iterative scatter correction process. Conclusions: The proposed correlated sampling-based MC simulation method can achieve fast and accurate scatter correction for CBCT,making it suitable for real-time clinical use. [ABSTRACT FROM AUTHOR]

Background: Cone‐beam computed tomography (CBCT) systems acquire volumetric data more efficiently than fan‐beam or multislice CT, particularly when the anatomy of interest resides within the axial field‐of‐view of the detector and data can be acquired in one rotation. For such systems, scattered radiation remains a source of image quality degradation leading to increased noise, image artifacts, and CT number inaccuracies. Purpose: Recent advances in metal additive manufacturing allow the production of highly focused antiscatter grids (2D‐ASGs) that can be used to reduce scatter intensity, while preserving primary radiation transmission. We present the first implementation of a large‐area, 2D‐ASG for flat‐panel CBCT, including grid‐line artifact removal and related improvements in image quality. Methods: A 245 × 194 × 10 mm 2D‐ASG was manufactured from chrome–cobalt alloy using laser powder‐bed fusion (LPBF) (AM‐400; Renishaw plc, New Mills Wotton‐under‐Edge, UK). The 2D‐ASG had a square profile with a pitch of 9.09 lines/cm and 10:1 grid‐ratio. The nominal 0.1 mm grid septa were focused to a 732 mm x‐ray source to optimize primary x‐ray transmission and reduce grid‐line shadowing at the detector. Powder‐bed fusion ensured the structural stability of the ASG with no need for additional interseptal support. The 2D‐ASG was coupled to a 0.139‐mm element pitch flat‐panel detector (DRX 3543, Carestream Health) and proper alignment was confirmed by consistent grid‐line shadow thickness across the whole detector array. A 154‐mm diameter CBCT image‐quality‐assurance phantom was imaged using a rotary stage and a ceiling‐mounted, x‐ray unit (Proteus XR/a, GE Medical Systems, 80kVp, 0.5mAs). Grid‐line artifacts were removed using a combination of exposure‐dependent gain correction and spatial‐frequency, Fourier filtering. Projections were reconstructed using a Parker‐weighted, FDK algorithm and voxels were spatially averaged to 357 × 357 × 595 µm to improve the signal‐to‐noise characteristics of the CBCT reconstruction. Finally, in order to compare image quality with and without scatter, the phantom was scanned again under the same CBCT conditions but with no 2D‐ASG. No additional antiscatter (i.e., air‐gap, bowtie filtration) strategies were used to evaluate the effects in image quality caused by the 2D‐ASG alone. Results: The large‐area, 2D‐ASG prototype was successfully designed and manufactured using LPBF. CBCT image‐quality improvements using the 2D‐ASG included: an overall 14.5% CNR increase across the volume; up to 48.8% CNR increase for low‐contrast inserts inside the contrast plate of the QA phantom; and a 65% reduction of cupping artifact in axial profiles of water‐filled cross sections of the phantom. Advanced image processing strategies to remove grid line artifacts did not affect the spatial resolution or geometric accuracy of the system. Conclusions: LPBF can be used to manufacture highly efficient, 2D‐focused ASGs that can be easily coupled to clinical, flat‐panel detectors. The implementation of ASGs in CBCT leads to reduced scatter‐related artifacts, improved CT number accuracy, and enhanced CNR with no increased equivalent dose to the patient. Further improvements to image quality might be achieved with a combination of scatter‐correction algorithms and iterative‐reconstruction strategies. Finally, clinical applications where other scatter removal strategies are unfeasible might now achieve superior soft‐tissue visualization and quantitative capabilities. [ABSTRACT FROM AUTHOR]

Purpose: Evaluate a cone‐beam computed tomography (CBCT)‐based daily adaptive platform in cervical cancer for multiple endpoints: (1) physics contouring accuracy of daily CTVs, (2) CTV coverage with adapted plans and reduced PTV margins versus non‐adapted plans with standard‐of‐care (SOC) margins, (3) dosimetric improvements to CTV and organs‐at‐risk (OARs), and (4) on‐couch time. Methods and materials: Using a Varian Ethos™ emulator and KV‐CBCT scans, we simulated the doses 15 retrospective cervical cancer patients would have received with/without online adaptation for five fractions. We compared contours and doses from SOC plans (5–15 mm CTV‐to‐PTV margins) to adapted plans (3 mm margins). Auto‐segmented CTVs and OARs were reviewed and edited by trained physicists. Physics‐edited targets were evaluated by an oncologist. Time spent reviewing and editing auto‐segmented structures was recorded. Metrics from the CTV (D99%), bowel (V45Gy, V40Gy), bladder (D50%), and rectum (D50%) were compared. Results: The physician approved the physics‐edited CTVs for 55/75 fractions; 16/75 required reductions, and 4/75 required CTV expansions. CTVs were encapsulated by unadapted, SOC PTVs for 56/75 (72%) fractions—representative of current clinical practice. CTVs were completely covered by adapted 3 mm PTVs for 71/75 (94.6%) fractions. CTV D99% values for adapted plans were comparable to non‐adapted SOC plans (average difference of −0.9%), while all OAR metrics improved with adaptation. Specifically, bowel V45Gy and V40Gy decreased on average by 87.6 and 109.4 cc, while bladder and rectum D50% decreased by 37.7% and 35.8%, respectively. The time required for contouring and calculating an adaptive plan for 65/75 fractions was less than 20 min (range: 1–29 min). Conclusions: Improved dose metrics with daily adaption could translate to reduced toxicity while maintaining tumor control. Training physicists to perform contouring edits could minimize the time physicians are required at adaptive sessions improving clinical efficiency. All emulated adaptive sessions were completed within 30 min however extra time will be required for patient setup, image acquisition, and treatment delivery. [ABSTRACT FROM AUTHOR]

Purpose: Treatment planning for head‐and‐neck (H&N) cancer, in particular oropharynx, nasopharynx, and paranasal sinus cases, at our center requires noncoplanar proton beams due to the complexity of the anatomy and target location. Targeting accuracy for all beams is carefully evaluated by using image guidance before delivering proton beam therapy (PBT). In this study, we analyzed couch shifts to evaluate whether imaging is required before delivering each field with different couch angles. Methods: After the Institutional Review Board approval, a retrospective analysis was performed on data from 28 H&N patients treated with PBT. Each plan was made with two‐to‐three noncoplanar and two‐to‐three coplanar fields. Cone‐beam computed tomography and orthogonal kilovoltage (kV) images were acquired for setup and before delivering each field, respectively. The Cartesian (longitudinal, vertical, and lateral) and angular (pitch and roll) shifts for each field were recorded from the treatment summary on the first two fractions and every subsequent fifth fraction. A net magnitude of the three‐dimensional (3D) shift in Cartesian coordinates was calculated, and a 3D vector was created from the 6 degrees of freedom coordinates for transforming couch shifts in the system coordinate to the beam's‐eye view. Results: A total of 3219 Cartesian and 2146 angular shift values were recorded for 28 patients. Of the Cartesian shifts, 2069 were zero (64.3%), and 1150 (35.7%) were nonzero (range, −7 to 11 mm). Of the angular shifts, 1034 (48.2%) were zero, and 1112 (51.8%) were nonzero (range, −3.0° to 3.2°). For 17 patients, the couch shifts increased toward the end of the treatment course. We also found that patients with higher body mass index (BMI) presented increased net couch shifts (p < 0.001). With BMI < 27, all overall net shift averages were <2 mm, and overall maximum net shifts were <6 mm. Conclusions: These results confirm the need for orthogonal kV imaging before delivering each field of H&N PBT at our center, where a couch rotation is involved. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the effects of subject motion and gantry rotation speed on computed tomography (CT) image quality over a range of image acquisition speeds for fan‐beam (FB) and cone‐beam (CB) CT scanners, and quantify the geometric and dosimetric errors introduced by FB and CB sampling in the context of adaptive radiotherapy. Methods: Images of motion phantoms were acquired using four CT scanners with gantry rotation speeds of 0.5 s/rotation (denoted FB‐0.5), 1.9 s/rotation (FB‐1.9), 16.6 s/rotation (CB‐16.6), and 60.0 s/rotation (CB‐60.0). A phantom presenting various tissue densities undergoing motion with 4‐s period and ranging in amplitude from ±0.5 to ±10.0 mm was used to characterize motion artifacts (streaks), motion blur (edge‐spread function, ESF), and geometric inaccuracy (excursion of insert centroids and distortion of known shape). An anthropomorphic abdomen phantom undergoing ±2.5‐mm motion with 4‐s period was used to simulate an adaptive radiotherapy workflow, and relative geometric and dosimetric errors were compared between scanners. Results: At ±2.5‐mm motion, phantom measurements demonstrated mean ± SD ESF widths of 0.6 ± 0.0, 1.3 ± 0.4, 2.0 ± 1.1, and 2.9 ± 2.0 mm and geometric inaccuracy (excursion) of 2.7 ± 0.4, 4.1 ± 1.2, 2.6 ± 0.7, and 2.0 ± 0.5 mm for the FB‐0.5, FB‐1.9, CB‐16.6, and CB‐60.0 scanners, respectively. The results demonstrated nonmonotonic trends with scanner speed for FB and CB geometries. Geometric and dosimetric errors in adaptive radiotherapy plans were largest for the slowest (CB‐60.0) scanner and similar for the three faster systems (CB‐16.6, FB‐1.9, and FB‐0.5). Conclusions: Clinically standard CB‐60.0 demonstrates strong image quality degradation in the presence of subject motion, which is mitigated through faster CBCT or FBCT. Although motion blur is minimized for FB‐0.5 and FB‐1.9, such systems suffer from increased geometric distortion compared to CB‐16.6. Each system reflects tradeoffs in image artifacts and geometric inaccuracies that affect treatment delivery/dosimetric error and should be considered in the design of next‐generation CT‐guided radiotherapy systems. [ABSTRACT FROM AUTHOR]

The use of deep learning (DL) to improve cone‐beam CT (CBCT) image quality has gained popularity as computational resources and algorithmic sophistication have advanced in tandem. CBCT imaging has the potential to facilitate online adaptive radiation therapy (ART) by utilizing up‐to‐date patient anatomy to modify treatment parameters before irradiation. Poor CBCT image quality has been an impediment to realizing ART due to the increased scatter conditions inherent to cone‐beam acquisitions. Given the recent interest in DL applications in radiation oncology, and specifically DL for CBCT correction, we provide a systematic theoretical and literature review for future stakeholders. The review encompasses DL approaches for synthetic CT generation, as well as projection domain methods employed in the CBCT correction literature. We review trends pertaining to publications from January 2018 to April 2022 and condense their major findings—with emphasis on study design and DL techniques. Clinically relevant endpoints relating to image quality and dosimetric accuracy are summarized, highlighting gaps in the literature. Finally, we make recommendations for both clinicians and DL practitioners based on literature trends and the current DL state‐of‐the‐art methods utilized in radiation oncology. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate dosimetric changes detected using synthetic computed tomography (sCT) derived from online cone‐beam CTs (CBCT) in pediatric patients treated using intensity‐modulated proton therapy (IMPT). Methods: Ten pediatric patients undergoing IMPT and aligned daily using proton gantry‐mounted CBCT were identified for retrospective analysis with treated anatomical sites fully encompassed in the CBCT field of view. Dates were identified when the patient received both a CBCT and a quality assurance CT (qCT) for routine dosimetric evaluation. sCTs were generated based on a deformable registration between the initial plan CT (pCT) and CBCT. The clinical IMPT plans were re‐computed on the same day qCT and sCT, and dosimetric changes due to tissue change or response from the initial plan were computed using each image. Linear regression analysis was performed to determine the correlation between dosimetric changes detected using the qCT and the sCT. Gamma analysis was also used to compare the dose distributions computed on the qCT and sCT. Results: The correlation coefficients (p‐values) between qCTs and sCTs for changes detected in target coverage, overall maximum dose, and organ at risk dose were 0.97 (<.001), 0.84 (.002) and 0.91 (<.001), respectively. Mean ± SD gamma pass rates of the sCT‐based dose compared to the qCT‐based dose at 3%/3 mm, 3%/2 mm, and 2%/2 mm criteria were 96.5%±4.5%, 93.2%±6.3%, and 91.3%±7.8%, respectively. Pass rates tended to be lower for targets near lung. Conclusion: While insufficient for re‐planning, sCTs provide approximate dosimetry without administering additional imaging dose in pediatric patients undergoing IMPT. Dosimetric changes detected using sCTs are correlated with changes detected using clinically‐standard qCTs; however, residual differences in dosimetry remain a limitation. Further improvements in sCT image quality may both improve online dosimetric evaluation and reduce imaging dose for pediatric patients by reducing the need for routine qCTs. [ABSTRACT FROM AUTHOR]

Background: Indirect detection flat‐panel detectors (FPDs) consisting of hydrogenated amorphous silicon (a‐Si:H) thin‐film transistors (TFTs) are a prevalent technology for digital x‐ray imaging. However, their performance is challenged in applications requiring low exposure levels, high spatial resolution, and high frame rate. Emerging FPD designs using metal oxide TFTs may offer potential performance improvements compared to FPDs based on a‐Si:H TFTs. Purpose: This work investigates the imaging performance of a new indium gallium zinc oxide (IGZO) TFT‐based detector in 2D fluoroscopy and 3D cone‐beam CT (CBCT). Methods: The new FPD consists of a sensor array combining IGZO TFTs with a‐Si:H photodiodes and a 0.7‐mm thick CsI:Tl scintillator. The FPD was implemented on an x‐ray imaging bench with system geometry emulating intraoperative CBCT. A conventional FPD with a‐Si:H TFTs and a 0.6‐mm thick CsI:Tl scintillator was similarly implemented as a basis of comparison. 2D imaging performance was characterized in terms of electronic noise, sensitivity, linearity, lag, spatial resolution (modulation transfer function, MTF), image noise (noise‐power spectrum, NPS), and detective quantum efficiency (DQE) with entrance air kerma (EAK) ranging from 0.3 to 1.2 μGy. 3D imaging performance was evaluated in terms of the 3D MTF and noise‐equivalent quanta (NEQ), soft‐tissue contrast‐to‐noise ratio (CNR), and image quality evident in anthropomorphic phantoms for a range of anatomical sites and dose, with weighted air kerma, Kw${K_w}$, ranging from 0.8 to 4.9 mGy. Results: The 2D imaging performance of the IGZO‐based FPD exhibited up to ∼1.7× lower electronic noise than the a‐Si:H FPD at matched pixel pitch. Furthermore, the IGZO FPD exhibited ∼27% increase in mid‐frequency DQE (1 mm−1) at matched pixel size and dose (EAK ≈ 1.0 μGy) and ∼11% increase after adjusting for differences in scintillator thickness. 2D spatial resolution was limited by the scintillator for each FPD. The IGZO‐based FPD demonstrated improved 3D NEQ at all spatial frequencies in both head (≥25% increase for all dose levels) and body (≥10% increase for Kw${K_w}$ ≤2 mGy) imaging scenarios. These characteristics translated to improved low‐contrast visualization in anthropomorphic phantoms, demonstrating ≥10% improvement in CNR and extension of the low‐dose range for which the detector is input‐quantum limited. Conclusion: The IGZO‐based FPD demonstrated improvements in electronic noise, image lag, and NEQ that translated to measurable improvements in 2D and 3D imaging performance compared to a conventional FPD based on a‐Si:H TFTs. The improvements are most beneficial for 2D or 3D imaging scenarios involving low‐dose and/or high‐frame rate. [ABSTRACT FROM AUTHOR]

Purpose: Image guidance is used to improve the accuracy of radiation therapy delivery but results in increased dose to patients. This is of particular concern in children who need be treated per Pediatric Image Gently Protocols due to long‐term risks from radiation exposure. The purpose of this study is to design a deep neural network architecture and loss function for improving soft‐tissue contrast and preserving small anatomical features in ultra‐low‐dose cone‐beam CTs (CBCT) of head and neck cancer (HNC) imaging. Methods: A 2D compound U‐Net architecture (modified U‐Net++) with different depths was proposed to enhance the network capability of capturing small‐volume structures. A mask weighted loss function (Mask‐Loss) was applied to enhance soft‐tissue contrast. Fifty‐five paired CBCT and CT images of HNC patients were retrospectively collected for network training and testing. The output enhanced CBCT images from the present study were evaluated with quantitative metrics including mean absolute error (MAE), signal‐to‐noise ratio (SNR), and structural similarity (SSIM), and compared with those from the previously proposed network architectures (U‐Net and wide U‐Net) using MAE loss functions. A visual assessment of ten selected structures in the enhanced CBCT images of each patient was performed to evaluate image quality improvement, blindly scored by an experienced radiation oncologist specialized in HN cancer. Results: All the enhanced CBCT images showed reduced artifactual distortion and image noise. U‐Net++ outperformed the U‐Net and wide U‐Net in terms of MAE, contrast near structure boundaries, and small structures. The proposed Mask‐Loss improved image contrast and accuracy of the soft‐tissue regions. The enhanced CBCT images predicted by U‐Net++ and Mask‐Loss demonstrated improvement compared to the U‐Net in terms of average MAE (52.41 vs 42.85 HU), SNR (14.14 vs 15.07 dB), and SSIM (0.84 vs 0.87), respectively (p<0.01$p < 0.01$, in all paired t‐tests). The visual assessment showed that the proposed U‐Net++ and Mask‐Loss significantly improved original CBCTs (p<0.01$p < 0.01$), compared to the U‐Net and MAE loss. Conclusions: The proposed network architecture and loss function effectively improved image quality in soft‐tissue contrast, organ boundary, and small structure preservation for ultra‐low‐dose CBCT following Image Gently Protocol. This method has potential to provide sufficient anatomical representation on the enhanced CBCT images for accurate treatment delivery and potentially fast online‐adaptive re‐planning for HN cancer patients. [ABSTRACT FROM AUTHOR]

Objectives: To determine the diagnostic performance of ultrasonography (US) for evaluation of the ankle joint osteoarthritic (OA) changes. Cone‐beam computed tomography (CT) was used as the gold standard and US performance was compared with conventional radiography (CR). As a secondary aim, associations between the imaging findings and ankle symptoms were assessed. Methods: US was performed to 51 patients with ankle OA. Every patient had prior ankle CR and underwent cone‐beam CT during the same day as US examination. On US, effusion/synovitis, osteophytes, talar cartilage damage, and tenosynovitis were evaluated. Comparison to respective imaging findings on CR and cone‐beam CT was then performed. Single radiologist blinded to other modalities assessed all the imaging studies. Symptoms questionnaire, the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), was available for 48 patients. Results: US detected effusion/synovitis of the talocrural joint with 45% sensitivity and 90% specificity. For the detection of anterior talocrural osteophytes, US sensitivity was 78% and specificity 79%. For the medial talocrural osteophytes, they were 39 and 83%, and for the lateral talocrural osteophytes 54 and 100%, respectively. Considering cartilage damage of the talus, US yielded a low sensitivity of 18% and high specificity of 97%. Overall, the performance of US was only moderate and comparable to CR. The imaging findings showed only weak associations with ankle symptoms. Conclusions: The ability of US to detect ankle OA is only moderate. Interestingly, performance of CR also remained moderate. The associations between imaging findings and WOMAC score seem to be weak in ankle OA. [ABSTRACT FROM AUTHOR]

Purpose: Cone‐beam computed tomography (CBCT) is a widely accessible low‐dose imaging approach compatible with on‐table patient anatomy observation for radiotherapy. However, its use in comprehensive anatomy monitoring is hindered by low contrast and low signal‐to‐noise ratio and a large presence of artifacts, resulting in difficulty in identifying organ and structure boundaries either manually or automatically. In this study, we propose and develop an ensemble deep‐learning model to segment post‐prostatectomy organs automatically. Methods: We utilize the ensemble logic in various modules during the segmentation process to alleviate the impact of low image quality of CBCT. Specifically, (1) semantic attention was obtained from an ensemble 2.5D You‐only‐look‐once detector to consistently define regions of interest, (2) multiple view‐specific two‐stream 2.5D segmentation networks were developed, using auxiliary high‐quality CT data to aid CBCT segmentation, and (3) a novel tensor‐regularized ensemble scheme was proposed to aggregate the estimates from multiple views and regularize the spatial integrity of the final segmentation. Results: A cross‐validation study achieved Dice similarity coefficient and mean surface distance of 0.779 ±$\pm$ 0.069 and 2.895 ±$\pm$ 1.496 mm for the rectum, and 0.915 ±$\pm$ 0.055 and 1.675 ±$\pm$ 1.311 mm for the bladder. Conclusions: The proposed ensemble scheme manages to enhance the geometric integrity and robustness of the contours derived from CBCT with light network components. The tensor regularization approach generates organ results conforming to anatomy and physiology, without compromising typical quantitative performance in Dice similarity coefficient and mean surface distance, to support further clinical interpretation and decision making. [ABSTRACT FROM AUTHOR]

Purpose: Cone‐beam computed tomography (CBCT) is frequently used for accurate image‐guided radiation therapy. However, the poor CBCT image quality prevents its further clinical use. Thus, it is important to improve the HU accuracy and structure preservation of CBCT images. Methods: In this study, we proposed a novel method to generate synthetic CT (sCT) images from CBCT images. A multiresolution residual deep neural network (RDNN) was adopted for image regression from CBCT images to planning CT (pCT) images. At the coarse level, RDNN was first trained with a large amount of lower resolution images, which can make the network focus on coarse information and prevent overfitting problems. More fine information was obtained gradually by fine‐tuning the coarse model using fewer number of higher resolution images. Our model was optimized by using aligned pCT and CBCT image pairs of a particular body region of 153 prostate cancer patients treated in our hospital (120 for training and 33 for testing). Five‐fold cross‐validation was used to tune the hyperparameters and the testing data were used to evaluate the performance of the final models. Results: The mean absolute error (MAE) between CBCT and pCT on the testing data was 352.56 HU, while the MAE between the sCT and pCT images was 52.18 HU for our proposed multiresolution RDNN model, which reduced the MAE by 85.20% (p < 0.01). In addition, the average structural similarity index measure between the sCT and CBCT was 19.64% (p = 0.01) higher than that of pCT and CBCT. Conclusions: The sCT images generated using our proposed multiresolution RDNN have higher HU accuracy and structural fidelity, which may promote the further applications of CBCT images in the clinic for structure segmentation, dose calculation, and adaptive radiotherapy planning. [ABSTRACT FROM AUTHOR]

Purpose: Cone‐beam computed tomography (CBCT) is widely used for daily anatomy monitoring and can be a potential source to support adaptation. However, low image quality and artifacts limit CBCT's clinical utility. Peristalsis and air bubbles can cause severe artifacts in pelvic CBCT. We have observed that severe air bubble‐induced Feldkamp artifacts in the rectum may contribute to low automatic segmentation accuracy. Materials and Methods: In this study, air bubbles within the rectum were extracted and automatic rectum segmentation performance was measured in Dice similarity coefficient (DSC). A Gaussian mixture model (GMM) was used to characterize their correlation, and an expectation‐maximization (EM) approach was used to solve the corresponding parameter estimation and decouple the impact from air bubbles versus other image attributes based on cluster memberships. Postprostatectomy patient data with high variability in air bubble size and shape were used in this study to reveal the regression relationship. Results: GMM identified two distinct correlative relations between the air‐bubble severity in the rectum and the rectum prediction DSC: one showed strong negative dependency of segmentation performance on air bubble presence, and the other one had mild‐to‐moderate dependency that suggested another group of contributing factors influencing rectum segmentation, such as the inconsistent presence of fiducial seeds and shape extremes. Conclusion: The presence of severe air bubbles contributes semilinearly to performance degradation in automatic rectum segmentation. A good correction mechanism may boost the accuracy and consistency of pelvic segmentation. [ABSTRACT FROM AUTHOR]

An in‐house hybrid deformable image registration (DIR) method, which combines free‐form deformation (FFD) and the viscous fluid registration method, is proposed. Its results on the planning computed tomography (CT) and the day 1 treatment cone‐beam CT (CBCT) image from 68 head and neck cancer patients are compared with the results of NiftyReg, which uses B‐spline FFD alone. Several similarity metrics, the target registration error (TRE) of annotated points, as well as the Dice similarity coefficient (DSC) and Hausdorff distance (HD) of the propagated organs at risk are employed to analyze their registration accuracy. According to quantitative analysis on mutual information, normalized cross‐correlation, and the absolute pixel value differences, the results of the proposed DIR are more similar to the CBCT images than the NiftyReg results. Smaller TRE of the annotated points is observed in the proposed method, and the overall mean TRE for the proposed method and NiftyReg was 2.34 and 2.98 mm, respectively (p < 0.001). The mean DSC in the larynx, spinal cord, oral cavity, mandible, and parotid given by the proposed method ranged from 0.78 to 0.91, significantly higher than the NiftyReg results (ranging from 0.77 to 0.90), and the HD was significantly lower compared to NiftyReg. Furthermore, the proposed method did not suffer from unrealistic deformations as the NiftyReg did in the visual evaluation. Meanwhile, the execution time of the proposed method was much higher than NiftyReg (96.98 ± 11.88 s vs. 4.60 ± 0.49 s). In conclusion, the in‐house hybrid method gave better accuracy and more stable performance than NiftyReg. [ABSTRACT FROM AUTHOR]

Background: Large antroliths and those located adjacent to the sinus floor can affect clinical interventions and increase the difficulty of implant placement performed simultaneously with osteotome sinus floor elevation surgery. Purpose: This retrospective study investigated the clinical outcomes of implants placed simultaneously with osteotome sinus floor elevation subjacent to maxillary antroliths. Material and methods: Twenty implants inserted subjacent to or intruding into the antrolith after sinus floor elevation were evaluated in 18 patients. Cone‐beam computed tomography (CBCT) was used to measure antrolith size and membrane thickness at sites of osteotome sinus floor elevation. Periapical radiographs were used to assess the height of grafted bone. Generalized estimating equation (GEE) analysis was performed to correlate the occurrence of antroliths with patient background characteristics and dental outcomes, based on a sample population of 239, among whom 33 presented antroliths. Results: The 20 implants remained clinically stable over a mean follow‐up period of 42.4 months. The mean thickness of the sinus membrane at osteotome sites was 5.4 ± 3.3 mm. None of the cases presented sinus membrane perforation or sinus symptoms following osteotome intervention. The mean gain in the height of grafted sinus bone was 4.0 ± 1.4 mm at the last follow‐up. The occurrence of antroliths was higher among females and the elderly (>49 years old). The multivariable GEE analysis showed that the adjusted odds ratio for the occurrence of antroliths with root canal fillings was significantly lower than those without root canal fillings (odds ratio = 0.33; 95% confidence interval = 0.11–0.96). Conclusion: Our findings indicate that osteotome sinus floor elevation is a surgical procedure with a risk <17%. Thorough planning based on CBCT and careful management during surgery can eliminate the negative effects of antroliths on implant performance. [ABSTRACT FROM AUTHOR]

Purpose: To characterize the 3D imaging performance and radiation dose for a prototype slot‐beam configuration on an intraoperative O‐arm™ Surgical Imaging System (Medtronic Inc., Littleton, MA) and identify potential improvements in soft‐tissue image quality for surgical interventions. Methods: A slot collimator was integrated with the O‐arm™ system for slot‐beam axial CT. The collimator can be automatically actuated to provide 1.2° slot‐beam longitudinal collimation. Cone‐beam and slot‐beam configurations were investigated with and without an antiscatter grid (12:1 grid ratio, 60 lines/cm). Dose, scatter, image noise, and soft‐tissue contrast resolution were evaluated in quantitative phantoms for head and body configurations over a range of exposure levels (beam energy and mAs), with reconstruction performed via filtered‐backprojection. Qualitative imaging performance across various anatomical sites and imaging tasks was assessed with anthropomorphic head, abdomen, and pelvis phantoms. Results: The dose for a slot‐beam scan varied from 0.02–0.06 mGy/mAs for head protocols to 0.01–0.03 mGy/mAs for body protocols, yielding dose reduction by ∼1/5 to 1/3 compared to cone‐beam, owing to beam collimation and reduced x‐ray scatter. The slot‐beam provided an ∼6–7× reduction in scatter‐to‐primary ratio (SPR) compared to the cone‐beam, yielding SPR ∼20–80% for head and body without the grid and ∼7–30% with the grid. Compared to cone‐beam scans at equivalent dose, slot‐beam images exhibited an ∼2.5× increase in soft‐tissue contrast‐to‐noise ratio (CNR) for both grid and gridless configurations. For slot‐beam scans, a further ∼10–30% improvement in CNR was achieved when the grid was removed. Slot‐beam imaging could benefit certain interventional scenarios in which improved visualization of soft tissues is required within a fairly narrow longitudinal region of interest (±7 mm in z)––for example, checking the completeness of tumor resection, preservation of adjacent anatomy, or detection of complications (e.g., hemorrhage). While preserving existing capabilities for fluoroscopy and cone‐beam CT, slot‐beam scanning could enhance the utility of intraoperative imaging and provide a useful mode for safety and validation checks in image‐guided surgery. Conclusions: The 3D imaging performance and dose of a prototype slot‐beam CT configuration on the O‐arm™ system was investigated. Substantial improvements in soft‐tissue image quality and reduction in radiation dose are evident with the slot‐beam configuration due to reduced x‐ray scatter. [ABSTRACT FROM AUTHOR]

Purpose: Image guidance for minimally invasive interventions is usually performed by acquiring fluoroscopic images using a monoplanar or a biplanar C‐arm system. However, the projective data provide only limited information about the spatial structure and position of interventional tools and devices such as stents, guide wires, or coils. In this work, we propose a deep learning‐based pipeline for real‐time tomographic (four‐dimensional [4D]) interventional guidance at conventional dose levels. Methods: Our pipeline is comprised of two steps. In the first one, interventional tools are extracted from four cone‐beam CT projections using a deep convolutional neural network. These projections are then Feldkamp reconstructed and fed into a second network, which is trained to segment the interventional tools and devices in this highly undersampled reconstruction. Both networks are trained using simulated CT data and evaluated on both simulated data and C‐arm cone‐beam CT measurements of stents, coils, and guide wires. Results: The pipeline is capable of reconstructing interventional tools from only four X‐ray projections without the need for a patient prior. At an isotropic voxel size of 100 μm, our methods achieve a precision/recall within a 100 μm environment of the ground truth of 93%/98%, 90%/71%, and 93%/76% for guide wires, stents, and coils, respectively. Conclusions: A deep learning‐based approach for 4D interventional guidance is able to overcome the drawbacks of today's interventional guidance by providing full spatiotemporal (4D) information about the interventional tools at dose levels comparable to conventional fluoroscopy. [ABSTRACT FROM AUTHOR]

Purpose: Metal artifact remains a challenge in cone‐beam CT images. Many image domain‐based segmentation methods have been proposed for metal artifact reduction (MAR), which require two‐pass reconstruction. Such methods first segment metal from a first‐pass reconstruction and then forward‐project the metal mask to identify them in projections. These methods work well in general but are limited when the metal is outside the scan field‐of‐view (FOV) or when the metal is moving during the scan. In the former, even reconstructing with a larger FOV does not guarantee a good estimate of metal location in the projections; and in the latter, the metal location in each projection is difficult to identify due to motion. Single‐pass methods that detect metal in single‐energy projections have also been developed, but often have imperfect metal detection that leads to residual artifacts. In this work, we develop a MAR method using a dual‐layer (DL) flat panel detector, which improves performance for single‐pass reconstruction. Methods: In this work, we directly detect metal objects in projections using dual‐energy (DE) imaging that generates material‐specific images (e.g., soft tissue and bone), where the metal stands out in bone images when nonuniform soft tissue background is removed. Metal is detected via simple thresholding, and entropy filtration is further applied to remove false‐positive detections. A DL detector provides DE images with superior temporal and spatial registration and was used to perform the task. Scatter correction was first performed on DE raw projections to improve the accuracy of material decomposition. One phantom mimicking a liver biopsy setup and a cadaver head were used to evaluate the metal reduction performance of the proposed method and compared with that of a standard two‐pass reconstruction, a previously published sinogram‐based method using a Markov random field (MRF) model, and a single‐pass projection‐domain method using single‐energy imaging. The phantom has a liver steering setup placed in a hollow chest phantom, with embedded metal and a biopsy needle crossing the phantom boundary. The cadaver head has dental fillings and a metal tag attached to its surface. The identified metal regions in each projection were corrected by interpolation using surrounding pixels, and the images were reconstructed using filtered backprojection. Results: Our current approach removes metal from the projections, which is robust to FOV truncation during imaging acquisition. In case of FOV truncation, the method outperformed the two‐pass reconstruction method. The proposed method using DE renders better accuracy in metal segmentation than the MRF method and single‐energy method, which were prone to false‐positive errors that cause additional streaks. For the liver steering phantom, the average spatial nonuniformity was reduced from 0.127 in uncorrected images to 0.086 using a standard two‐pass reconstruction and to 0.077 using the proposed method. For the cadaver head, the average standard deviation within selected soft tissue regions (σs) was reduced from 209.1 HU in uncorrected images to 69.1 HU using a standard two‐pass reconstruction and to 46.8 HU using our proposed method. The proposed method reduced the processing time by 31% as compared with the two‐pass method. Conclusions: We proposed a MAR method that directly detects metal in the projection domain using DE imaging, which is robust to truncation and superior to that of single‐energy imaging. The method requires only a single‐pass reconstruction that substantially reduces processing time compared with the standard two‐pass metal reduction method. [ABSTRACT FROM AUTHOR]

Purpose: Implementing new online adaptive radiation therapy technologies is challenging because extra clinical resources are required particularly expert contour review. Here, we provide the first assessment of Varian's Ethos™ adaptive platform for prostate cancer using no manual edits after auto‐segmentation to minimize this impact on clinical efficiency. Methods: Twenty‐five prostate patients previously treated at our clinic were re‐planned using an Ethos™ emulator. Clinical target volumes (CTV) included intact prostate and proximal seminal vesicles. The following clinical margins were used: 3 mm posterior, 5 mm left/right/anterior, and 7 mm superior/inferior. Adapted plans were calculated for 10 fractions per patient using Ethos's auto‐segmentation and auto‐planning workflow without manual contouring edits. Doses and auto‐segmented structures were exported to our clinical treatment planning system where contours were modified as needed for all 250 CTVs and organs‐at‐risk. Dose metrics from adapted plans were compared to unadapted plans to evaluate CTV and OAR dose changes. Results: Overall 96% of fractions required auto‐segmentation edits, although corrections were generally minor (<10% of the volume for 70% of CTVs, 88% of bladders, and 90% of rectums). However, for one patient the auto‐segmented CTV failed to include the superior portion of prostate that extended into the bladder at all 10 fractions resulting in under‐contouring of the CTV by 31.3% ± 6.7%. For the 24 patients with minor auto‐segmentation corrections, adaptation improved CTV D98% by 2.9% ± 5.3%. For non‐adapted fractions where bladder or rectum V90% exceeded clinical thresholds, adaptation reduced them by 13.1% ± 1.0% and 6.5% ± 7.3%, respectively. Conclusion: For most patients, Ethos's online adaptive radiation therapy workflow improved CTV D98% and reduced normal tissue dose when structures would otherwise exceed clinical thresholds, even without time‐consuming manual edits. However, for one in 25 patients, large contour edits were required and thus scrutiny of the daily auto‐segmentation is necessary and not all patients will be good candidates for adaptation. [ABSTRACT FROM AUTHOR]

Purpose: Flat‐panel detector (FPD) based dual‐energy cone‐beam computed tomography (DE‐CBCT) is a promising imaging technique for dedicated clinical applications. In this paper, we proposed a fully analytical method for fast and effective single‐scan DE‐CBCT image reconstruction and decomposition. Methods: A rotatable Mo filter was inserted between an x‐ray source and imaged object to alternately produce low and high‐energy x‐ray spectra. First, filtered‐backprojection (FBP) method was applied on down‐sampled projections to reconstruct low and high‐energy images. Then, the two images were converted into a vectorized form represented with an amplitude and an argument image. Using amplitude image as a guide, a joint bilateral filter was applied to denoise the argument image. Then, high‐quality dual‐energy images were recovered from the amplitude image and the denoised argument image. Finally, the recovered dual‐energy images were further used for low‐noise material decomposition and electron density synthesis. Imaging was conducted on a Catphan®600 phantom and an anthropomorphic head phantom. The proposed method was evaluated via comparison with the traditional two‐scan method and a commonly used filtering method (HYPR‐LR). Results: On the Catphan®600 phantom, the proposed method successfully reduced streaking artifacts and preserved spatial resolution and noise‐power‐spectrum (NPS) pattern. In the electron density image, the proposed method increased contrast‐to‐noise ratio (CNR) by more than 2.5 times and achieved <1.2% error for electron density values. On the anthropomorphic head phantom, the proposed method greatly improved the soft‐tissue contrast and the fine detail differentiation ability. In the selected ROIs on different human tissues, the differences between the CT number obtained by the proposed method and that by the two‐scan method were less than 4 HU. In the material images, the proposed method suppressed noise by over 75.5% compared with two‐scan results, and by over 40.4% compared with HYPR‐LR results. Implementation of the whole algorithm took 44.5 s for volumetric imaging, including projection preprocessing, FBP reconstruction, joint bilateral filtering, and material decomposition. Conclusions: Using down‐sampled projections in single‐scan DE‐CBCT, the proposed method could effectively and efficiently produce high‐quality DE‐CBCT images and low‐noise material decomposition images. This method demonstrated superior performance on spatial resolution enhancement, NPS preservation, noise reduction, and electron density accuracy, indicating better prospect in material differentiation and dose calculation. [ABSTRACT FROM AUTHOR]

Access to volumetric imaging modalities, such as magnetic resonance imaging (MRI) and computed tomography (CT), has increased over the past decade and has revolutionised the way clinicians evaluate equine anatomy. More recent advancements have resulted in the development of multiple commercially available cone‐beam CT (CBCT) scanners for equine use. CBCT scanners modify the traditional fan‐shaped beam of ionising radiation into a three‐dimensional pyramidal‐ or cone‐shaped beam of radiation. This modification enables the scanner to acquire sufficient data to create diagnostic images of a region of interest after a single rotation of the gantry. The rapid acquisition of data and divergent X‐ray beam causes some artifacts to be more prominent on CBCT images—as well as the unique cone‐beam artifact—resulting in decreased contrast resolution. While the use of CT for evaluation of the equine musculoskeletal anatomy is not new, there is a paucity of literature and scientific studies on the capabilities of CBCT for equine imaging. CBCT units do not require a specialised table for imaging and in some cases are portable for imaging in the standing or anaesthetised patient. This review article summarises the basic physics of CT technology, including how CBCT imaging differs, and provides objective information about the strengths and limitations of this modality. Finally, potential future applications and techniques for imaging with CT which will need to be explored in order to fully consider the capabilities of CT imaging in the horse are discussed. [ABSTRACT FROM AUTHOR]

Purpose: Accurate deformable registration between computed tomography (CT) and cone‐beam CT (CBCT) images of pancreatic cancer patients treated with high biologically effective radiation doses is essential to assess changes in organ‐at‐risk (OAR) locations and shapes and to compute delivered dose. This study describes the development and evaluation of a deep‐learning (DL) registration model to predict OAR segmentations on the CBCT derived from segmentations on the planning CT. Methods: The DL model is trained with CT‐CBCT image pairs of the same patient, on which OAR segmentations of the small bowel, stomach, and duodenum have been manually drawn. A transformation map is obtained, which serves to warp the CT image and segmentations. In addition to a regularity loss and an image similarity loss, an OAR segmentation similarity loss is also used during training, which penalizes the mismatch between warped CT segmentations and manually drawn CBCT segmentations. At test time, CBCT segmentations are not required as they are instead obtained from the warped CT segmentations. In an IRB‐approved retrospective study, a dataset consisting of 40 patients, each with one planning CT and two CBCT scans, was used in a fivefold cross‐validation to train and evaluate the model, using physician‐drawn segmentations as reference. Images were preprocessed to remove gas pockets. Network performance was compared to two intensity‐based deformable registration algorithms (large deformation diffeomorphic metric mapping [LDDMM] and multimodality free‐form [MMFF]) as baseline. Evaluated metrics were Dice similarity coefficient (DSC), change in OAR volume within a volume of interest (enclosing the low‐dose PTV plus 1 cm margin) from planning CT to CBCT, and maximum dose to 5 cm3 of the OAR [D(5cc)]. Results: Processing time for one CT‐CBCT registration with the DL model at test time was less than 5 seconds on a GPU‐based system, compared to an average of 30 minutes for LDDMM optimization. For both small bowel and stomach/duodenum, the DL model yielded larger median DSC and smaller interquartile variation than either MMFF (paired t‐test P < 10−4 for both type of OARs) or LDDMM (P < 10−3 and P = 0.03 respectively). Root‐mean‐square deviation (RMSD) of DL‐predicted change in small bowel volume relative to reference was 22% less than for MMFF (P = 0.007). RMSD of DL‐predicted stomach/duodenum volume change was 28% less than for LDDMM (P = 0.0001). RMSD of DL‐predicted D(5cc) in small bowel was 39% less than for MMFF (P = 0.001); in stomach/duodenum, RMSD of DL‐predicted D(5cc) was 18% less than for LDDMM (P < 10−3). Conclusions: The proposed deep network CT‐to‐CBCT deformable registration model shows improved segmentation accuracy compared to intensity‐based algorithms and achieves an order‐of‐magnitude reduction in processing time. [ABSTRACT FROM AUTHOR]

Purpose: The modulation transfer function (MTF) is widely used as an objective metric of spatial resolution of medical imaging systems. Despite advances in capability for three‐dimensional (3D) isotropic spatial resolution in computed tomography (CT) and cone‐beam CT (CBCT), MTF evaluation for such systems is typically reported only in the axial plane, and practical methodology for assessment of fully 3D spatial resolution characteristics is lacking. This work reviews fundamental theoretical relationships of two‐dimensional (2D) and 3D spread functions and reports practical methods and test tools for analysis of 3D MTF in CBCT. Methods: Fundamental aspects of 2D and 3D MTF measurement are reviewed within a common notational framework, and three MTF test tools with analysis code are reported and made available online (https://istar.jhu.edu/downloads/): (a) a multi‐wire tool for measurement of the axial plane MTF [denoted as MTF(fr;φ=0∘), where φis the measurement angle out of the axial plane] as a function of position in the axial plane; (b) a wedge tool for measurement of the MTF in any direction in the 3D Fourier domain [e.g., φ = 45°, denoted as MTF(fr;φ=45∘)]; and (c) a sphere tool for measurement of the MTF in any or all directions in the 3D Fourier domain. Experiments were performed on a mobile C‐arm with CBCT capability, showing that MTF(fr;φ=45∘) yields an informative one‐dimensional (1D) representation of the overall 3D spatial resolution characteristics, capturing important characteristics of the 3D MTF that might be missed in conventional analysis. The effects of anisotropic filters and detector readout mode were investigated, and the extent to which a system can be said to provide "isotropic" resolution was evaluated by quantitative comparison of MTF at various φ. Results: All three test tools provided consistent measurement of MTF(fr;φ=0∘), and the wedge and sphere tools demonstrated how measurement of the MTF in directions outside the axial plane (φ>0∘) can reveal spatial resolution characteristics to which conventional axial MTF measurement is blind. The wedge tool was shown to reduce statistical measurement error compared to the sphere tool due to improved sampling, and the sphere tool was shown to provide a basis for measurement of the MTF in any or all directions (outside the null cone) from a single scan. The C‐arm system exhibited non‐isotropic spatial resolution with conventional non‐isotropic 1D apodization filters (i.e., frequency cutoff filters) — which is common in CBCT — and implementation of isotropic 2D apodization yielded quantifiably isotropic MTF. Asymmetric pixel binning modes were similarly shown to impart non‐isotropic effects on the 3D MTF, and the overall 3D MTF characteristics were evident in each case with a single, 1D measurement of the 1D MTF(fr;φ=45∘). Conclusion: Three test tools and corresponding MTF analysis methods were presented within a consistent framework for analysis of 3D spatial resolution characteristics in a manner amenable to routine, practical measurements. Experiments on a CBCT C‐arm validated many intuitive aspects of 3D spatial resolution and quantified the extent to which a CBCT system may be considered to have isotropic resolution. Measurement of MTF(fr;φ=45∘) provided a practical 1D measure of the underlying 3D MTF characteristics and is extensible to other CT or CBCT systems offering high, isotropic spatial resolution. [ABSTRACT FROM AUTHOR]

Purpose: Radiomic features of cone‐beam CT (CBCT) images have potential as biomarkers to predict treatment response and prognosis for patients of prostate cancer. Previous studies of radiomic feature analysis for prostate cancer were assessed in a variety of imaging modalities, including MRI, PET, and CT, but usually limited to a pretreatment setting. However, CBCT images may provide an opportunity to capture early morphological changes to the tumor during treatment that could lead to timely treatment adaptation. This work investigated the quality of CBCT‐based radiomic features and their relationship with reconstruction methods applied to the CBCT projections and the preprocessing methods used in feature extraction. Moreover, CBCT features were correlated with planning CT (pCT) features to further assess the viability of CBCT radiomic features. Methods: The quality of 42 CBCT‐based radiomic features was assessed according to their repeatability and reproducibility. Repeatability was quantified by correlating radiomic features between 20 CBCT scans that also had repeated scans within 15 minutes. Reproducibility was quantified by correlating radiomic features between the planning CT (pCT) and the first fraction CBCT for 20 patients. Concordance correlation coefficients (CCC) of radiomic features were used to estimate the repeatability and reproducibility of radiomic features. The same patient dataset was assessed using different reconstruction methods applied to the CBCT projections. CBCT images were generated using 18 reconstruction methods using iterative (iCBCT) and standard (sCBCT) reconstructions, three convolution filters, and five noise suppression filters. Eighteen preprocessing settings were also considered. Results: Overall, CBCT radiomic features were more repeatable than reproducible. Five radiomic features are repeatable in > 97% of the reconstruction and preprocessing methods, and come from the gray‐level size zone matrix (GLSZM), neighborhood gray‐tone difference matrix (NGTDM), and gray‐level‐run length matrix (GLRLM) radiomic feature classes. These radiomic features were reproducible in > 9.8% of the reconstruction and preprocessing methods. Noise suppression and convolution filter smoothing increased radiomic features repeatability, but decreased reproducibility. The top‐repeatable iCBCT method (iCBCT‐Sharp‐VeryHigh) is more repeatable than the top‐repeatable sCBCT method (sCBCT‐Smooth) in 64% of the radiomic features. Conclusion: Methods for reconstruction and preprocessing that improve CBCT radiomic feature repeatability often decrease reproducibility. The best approach may be to use methods that strike a balance repeatability and reproducibility such as iCBCT‐Sharp‐VeryLow‐1‐Lloyd‐256 that has 17 repeatable and eight reproducible radiomic features. Previous radiomic studies that only used pCT radiomic features have generated prognostic models of prostate cancer outcome. Since our study indicates that CBCT radiomic features correlated well with a subset of pCT radiomic features, one may expect CBCT radiomics to also generate prognostic models for prostate cancer. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the accuracy of surface‐guided radiotherapy (SGRT) in cranial patient setup by direct comparison between optical surface imaging (OSI) and cone‐beam computed tomography (CBCT), before applying SGRT‐only setup for conventional radiotherapy of brain and nasopharynx cancer. Methods and Materials: Using CBCT as reference, SGRT setup accuracy was examined based on 269 patients (415 treatments) treated with frameless cranial stereotactic radiosurgery (SRS) during 2018‐2019. Patients were immobilized in customized head molds and open‐face masks and monitored using OSI during treatment. The facial skin area in planning CT was used as OSI region of interest (ROI) for automatic surface alignment and the skull was used as the landmark for automatic CBCT/CT registration. A 6 degrees of freedom (6DOF) couch was used. Immediately after CBCT setup, an OSI verification image was captured, recording the SGRT setup differences. These differences were analyzed in 6DOFs and as a function of isocenter positions away from the anterior surface to assess OSI‐ROI bias. The SGRT in‐room setup time was estimated and compared with CBCT and orthogonal 2D kilovoltage (2DkV) setups. Results: The SGRT setup difference (magnitude) is found to be 1.0 ± 2.5 mm and 0.1˚±1.4˚ on average among 415 treatments and within 5 mm/3˚ with greater than 95% confidence level (P < 0.001). Outliers were observed for very‐posterior isocenters: 15 differences (3.6%) are >5.0mm and 9 (2.2%) are >3.0˚. The setup differences show minor correlations (|r| < 0.45) between translational and rotational DOFs and a minor increasing trend (<1.0 mm) in the anterior‐to‐posterior direction. The SGRT setup time is 0.8 ± 0.3 min, much shorter than CBCT (5 ± 2 min) and 2DkV (2 ± 1 min) setups. Conclusion: This study demonstrates that SGRT has sufficient accuracy for fast in‐room patient setup and allows real‐time motion monitoring for beam holding during treatment, potentially useful to guide radiotherapy of brain and nasopharynx cancer with standard fractionation. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the dosimetric impact of daily positioning variations measured with cone‐beam computed tomography (CBCT) on whole‐breast radiotherapy patients treated in the prone position. Methods: Daily CBCT was prospectively acquired for 30 consecutive patients positioned prone. Treatment for early‐stage (≤II) breast cancer was prescribed with standard dose (50 Gy/25 fractions) or hypofractionation (42.56 Gy/16 fractions) for 13 and 17 patients, respectively. Systematic and random errors were calculated from the translational CBCT shifts and used to determine population‐based setup margins. Mean translations (±one standard deviation) for each patient were used to simulate the dosimetric impact on targets (PTV_eval and lumpectomy cavity), heart, and lung. Paired Student's t tests at α = 0.01 were used to compare dose metrics after correction for multiple testing (P < 0.002). Significant correlation coefficients were used to identify associations (P < 0.01). Results: Of 597 total fractions, 20 ± 13% required patient rotation. Mean translations were 0.29 ± 0.27 cm, 0.41 ± 0.34 cm, and 0.48 ± 0.33 cm in the anterior–posterior, superior–inferior, and lateral directions leading to calculated setup margins of 0.63, 0.88, and 1.10 cm, respectively. Average three‐dimensional (3D) shifts correlated with the maximum distance of breast tissue from the sternum (r = 0.62) but not with body‐mass index. Simulated shifts showed significant, but minor, changes in dose metrics for PTV_eval, lung, and heart. For left‐sided treatments (n = 18), mean heart dose increased from 109 ± 75 cGy to 148 ± 115 cGy. Shifts from the original plan caused PTV_eval hotspots (V105%) to increase by 5.2% ± 3.8%, which correlated with the total MU of wedged fields (r = 0.59). No significant change in V95% to the cavity was found. Conclusions: Large translational variations that occur when positioning prone breast patients had small but significant dosimetric effects on 3DCRT plans. Daily CBCT may still be necessary to correct for rotational variations that occur in 20% of treatments. To maintain planned dose metrics, unintended beam shifts toward the heart and the contribution of wedged fields should be minimized. [ABSTRACT FROM AUTHOR]

Objectives: To develop and evaluate an educational web‐based programme for temporomandibular joint (TMJ) assessment using cone‐beam computed tomography (CBCT). Methods: A web‐based educational programme was designed for TMJ assessment using CBCT images. Fifteen CBCT cases of the TMJ (three‐dimensional reconstructed volumes) and an assessment module based on image analysis criteria in the Diagnostic Criteria for Temporomandibular Disorders (DC/TMD) were added to the programme. Thirty‐six undergraduate dental students were invited to test the programme by assessing all cases twice: before and after 2 months of training. Participants submitted written subjective evaluations of the programme. Results: The web‐based programme can be accessed on Malmö University webpage (www.cbct‐tmj.mau.se). Despite limited CBCT learning in their undergraduate training and not using the programme in the 2‐month interval, the students were able to correctly diagnose 80% of the cases at the second assessment. Their diagnoses, however, did not differ significantly from the first assessment. Overall, the students were satisfied with the programme and considered it user‐friendly. Conclusion: The web‐based educational programme that was developed in the present study and tested by dental students could be a useful educational tool for TMJ assessment using CBCT. [ABSTRACT FROM AUTHOR]

Purpose: To assess whether alveolar ridge preservation (ARP) with 90% deproteinized bovine bone mineral in a 10% collagen matrix (DBBMC) and resorbable type I/III porcine collagen matrix (CM) maintains sufficient bone volume for early implant placement 8–10 weeks after extraction of maxillary central incisors. Materials and Methods: In this case series study of 10 consecutively enrolled patients, sockets of maxillary single central incisors requiring extraction and early implant placement were grafted with DBBMC/CM. Ridge dimensions were measured pre‐extraction and just prior to implant placement. Results: Alveolar ridge preservation maintained sufficient bone volume for implants to be placed in all sites. Compared to pre‐extraction, there was a significant reduction in the orofacial dimensions of the ridge (1.4 ± 1.07 mm; 13.2% reduction) and bone (0.7 ± 0.67 mm; 9.3%) at the coronal midfacial region. A significant reduction in apicocoronal height of the crestal bone at midfacial (1.2 ± 0.78 mm) and palatal aspects was observed. On CBCT, a statistically significant reduction in alveolar ridge area occurred (10.9 ± 13.42 mm2; 12.2% reduction). To optimize aesthetic outcomes, 9/10 sites required additional low volume grafting at the coronal region, whereas one site required more extensive grafting due to a facial bone dehiscence. At 1‐year, the implant survival rate was 100% and median Pink Esthetic Score (PES) was 10 (range 9–13). Conclusions: ARP using DBBMC/CM maintains sufficient bone volume for early implant placement 8.9 ± 0.97 weeks later, with a 100% survival rate 1 year after restoration. [ABSTRACT FROM AUTHOR]

Purpose: In intensity‐modulated proton therapy (IMPT), protons are used to deliver highly conformal dose distributions, targeting tumors, and sparing organs‐at‐risk. However, due to uncertainties in both patient setup and relative stopping power (RSP) calculation, margins are added to the treatment volume during treatment planning, leading to higher doses to normal tissues. Cone‐beam computed tomography (CBCT) images are taken daily before treatment; however, the poor image quality of CBCT limits the use of these images for online dose calculation. In this work, we use a deep‐learning‐based method to predict RSP maps from daily CBCT images, allowing for online dose calculation in a step toward adaptive radiation therapy. Methods: Twenty‐three head‐and‐neck cancer patients were simulated using a Siemens TwinBeam dual‐energy CT (DECT) scanner. Mixed‐energy scans (equivalent to a 120 kVp single‐energy CT scan) were converted to RSP maps for treatment planning. Cone‐beam computed tomography images were taken on the first day of treatment, and the planning RSP maps were registered to these images. A deep learning network based on a cycle‐GAN architecture, relying on a compound loss function designed for structural and contrast preservation, was then trained to create an RSP map from a CBCT image. Leave‐one‐out and holdout cross validations were used for evaluation, and mean absolute error (MAE), mean error (ME), peak signal‐to‐noise ratio (PSNR), and structural similarity (SSIM) were used to quantify the differences between the CT‐based and CBCT‐based RSP maps. The proposed method was compared to a deformable image registration‐based method which was taken as the ground truth and two other deep learning methods. For one patient who underwent resimulation, the new planning RSP maps and CBCT images were used for further evaluation and validation. Results: The CBCT‐based RSP generation method was evaluated on 23 head‐and‐neck cancer patients. From leave‐one‐out testing, the MAE between CT‐based and CBCT‐based RSP was 0.06 ± 0.01 and the ME was −0.01 ± 0.01. The proposed method statistically outperformed the comparison DL methods in terms of MAE and ME when compared to the planning CT. In terms of dose comparison, the mean gamma passing rate at 3%/3 mm was 94% when three‐dimensional (3D) gamma index was calculated per plan and 96% when gamma index was calculated per field. Conclusions: The proposed method provides sufficiently accurate RSP map generation from CBCT images, allowing for evaluation of daily dose based on CBCT and possibly allowing for CBCT‐guided adaptive treatment planning for IMPT. [ABSTRACT FROM AUTHOR]

Purpose: The recently introduced robotic x‐ray systems provide the flexibility to acquire cone‐beam computed tomography (CBCT) data using customized, application‐specific source‐detector trajectories. We exploit this capability to mitigate the effects of x‐ray scatter and noise in CBCT imaging of weight‐bearing foot and cervical spine (C‐spine) using scan orbits with a tilted rotation axis. Methods: We used an advanced CBCT simulator implementing accurate models of x‐ray scatter, primary attenuation, and noise to investigate the effects of the orbital tilt angle in upright foot and C‐spine imaging. The system model was parameterized using a laboratory version of a three‐dimensional (3D) robotic x‐ray system (Multitom RAX, Siemens Healthineers). We considered a generalized tilted axis scan configuration, where the detector remained parallel to patient's long body axis during the acquisition, but the elevation of source and detector was changing. A modified Feldkamp–Davis–Kress (FDK) algorithm was developed for reconstruction in this configuration, which departs from the FDK assumption of a detector that is perpendicular to the scan plane. The simulated foot scans involved source‐detector distance (SDD) of 1386 mm, orbital tilt angles ranging 10° to 40°, and 400 views at 1 mAs/view and 0.5° increment; the C‐spine scans involved −25° to −45° tilt angles, SDD of 1090 mm, and 202 views at 1.3 mAs and 1° increment The imaging performance was assessed by projection‐domain measurements of the scatter‐to‐primary ratio (SPR) and by reconstruction‐domain measurements of contrast, noise and generalized contrast‐to‐noise ratio (gCNR, accounting for both image noise and background nonuniformity) of the metatarsals (foot imaging) and cervical vertebrae (spine imaging). The effects of scatter correction were also compared for horizontal and tilted scans using an ideal Monte Carlo (MC)‐based scatter correction and a frame‐by‐frame mean scatter correction. Results: The proposed modified FDK, involving projection resampling, mitigated streak artifacts caused by the misalignment between the filtering direction and the detector rows. For foot imaging (no grids), an optimized 20° tilted orbit reduced the maximum SPR from ~1.5 in a horizontal scan to <0.5. The gCNR of the second metatarsal was enhanced twofold compared to a horizontal orbit. For the C‐spine (with vertical grids), imaging with a tilted orbit avoided highly attenuating x‐ray paths through the lower cervical vertebrae and shoulders. A −35° tilted orbit yielded improved image quality and visualization of the lower cervical spine: the SPR of lower cervical vertebrae was reduced from ~10 (horizontal orbit) to <6 (tilted orbit), and the gCNR for C5–C7 increased by a factor of 2. Furthermore, tilted orbits showed potential benefits over horizontal orbits by enabling scatter correction with a simple frame‐by‐frame mean correction without substantial increase in noise‐induced artifacts after the correction. Conclusions: Tilted scan trajectories, enabled by the emerging robotic x‐ray system technology, were optimized for CBCT imaging of foot and cervical spine using an advanced simulation framework. The results demonstrated the potential advantages of tilted axis orbits in mitigation of scatter artifacts and improving contrast‐to‐noise ratio in CBCT reconstructions. [ABSTRACT FROM AUTHOR]

Background and purpose: The purpose of this study is to develop a deep learning‐based approach to simultaneously segment five pelvic organs including prostate, bladder, rectum, left and right femoral heads on cone‐beam CT (CBCT), as required elements for prostate adaptive radiotherapy planning. Materials and methods: We propose to utilize both CBCT and CBCT‐based synthetic MRI (sMRI) for the segmentation of soft tissue and bony structures, as they provide complementary information for pelvic organ segmentation. CBCT images have superior bony structure contrast and sMRIs have superior soft tissue contrast. Prior to segmentation, sMRI was generated using a cycle‐consistent adversarial networks (CycleGAN), which was trained using paired CBCT‐MR images. To combine the advantages of both CBCT and sMRI, we developed a cross‐modality attention pyramid network with late feature fusion. Our method processes CBCT and sMRI inputs separately to extract CBCT‐specific and sMRI‐specific features prior to combining them in a late‐fusion network for final segmentation. The network was trained and tested using 100 patients' datasets, with each dataset including the CBCT and manual physician contours. For comparison, we trained another two networks with different network inputs and architectures. The segmentation results were compared to manual contours for evaluations. Results: For the proposed method, dice similarity coefficients and mean surface distances between the segmentation results and the ground truth were 0.96 ± 0.03, 0.65 ± 0.67 mm; 0.91 ± 0.08, 0.93 ± 0.96 mm; 0.93 ± 0.04, 0.72 ± 0.61 mm; 0.95 ± 0.05, 1.05 ± 1.40 mm; and 0.95 ± 0.05, 1.08 ± 1.48 mm for bladder, prostate, rectum, left and right femoral heads, respectively. As compared to the other two competing methods, our method has shown superior performance in terms of the segmentation accuracy. Conclusion: We developed a deep learning‐based segmentation method to rapidly and accurately segment five pelvic organs simultaneously from daily CBCTs. The proposed method could be used in the clinic to support rapid target and organs‐at‐risk contouring for prostate adaptive radiation therapy. [ABSTRACT FROM AUTHOR]

The key goal and main challenge of radiation therapy is the elimination of tumors without any concurring damages of the surrounding healthy tissues and organs. Radiation doses required to achieve sufficient cancer‐cell kill exceed in most clinical situations the dose that can be tolerated by the healthy tissues, especially when large parts of the affected organ are irradiated. High‐precision radiation oncology aims at optimizing tumor coverage, while sparing normal tissues. Medical imaging during the preparation phase, as well as in the treatment room for localization of the tumor and directing the beam, referred to as image‐guided radiotherapy (IGRT), is the cornerstone of precision radiation oncology. Sophisticated high‐resolution real‐time IGRT using X‐rays, computer tomography, magnetic resonance imaging, or ultrasound, enables delivery of high radiation doses to tumors without significant damage of healthy organs. IGRT is the most convincing success story of radiation oncology over the last decades, and it remains a major driving force of innovation, contributing to the development of personalized oncology, for example, through the use of real‐time imaging biomarkers for individualized dose delivery. [ABSTRACT FROM AUTHOR]

Purpose: Our aim was to develop a high‐quality, mobile cone‐beam computed tomography (CBCT) scanner for point‐of‐care detection and monitoring of low‐contrast, soft‐tissue abnormalities in the head/brain, such as acute intracranial hemorrhage (ICH). This work presents an integrated framework of hardware and algorithmic advances for improving soft‐tissue contrast resolution and evaluation of its technical performance with human subjects. Methods: Four configurations of a CBCT scanner prototype were designed and implemented to investigate key aspects of hardware (including system geometry, antiscatter grid, bowtie filter) and technique protocols. An integrated software pipeline (c.f., a serial cascade of algorithms) was developed for artifact correction (image lag, glare, beam hardening and x‐ray scatter), motion compensation, and three‐dimensional image (3D) reconstruction [penalized weighted least squares (PWLS), with a hardware‐specific statistical noise model]. The PWLS method was extended in this work to accommodate multiple, independently moving regions with different resolution (to address both motion compensation and image truncation). Imaging performance was evaluated quantitatively and qualitatively with 41 human subjects in the neurosciences critical care unit (NCCU) at our institution. Results: The progression of four scanner configurations exhibited systematic improvement in the quality of raw data by variations in system geometry (source‐detector distance), antiscatter grid, and bowtie filter. Quantitative assessment of CBCT images in 41 subjects demonstrated: ~70% reduction in image nonuniformity with artifact correction methods (lag, glare, beam hardening, and scatter); ~40% reduction in motion‐induced streak artifacts via the multi‐motion compensation method; and ~15% improvement in soft‐tissue contrast‐to‐noise ratio (CNR) for PWLS compared to filtered backprojection (FBP) at matched resolution. Each of these components was important to improve contrast resolution for point‐of‐care cranial imaging. Conclusions: This work presents the first application of a high‐quality, point‐of‐care CBCT system for imaging of the head/ brain in a neurological critical care setting. Hardware configuration iterations and an integrated software pipeline for artifacts correction and PWLS reconstruction mitigated artifacts and noise to achieve image quality that could be valuable for point‐of‐care detection and monitoring of a variety of intracranial abnormalities, including ICH and hydrocephalus. [ABSTRACT FROM AUTHOR]

Aim: To explore in a laboratory setting the feasibility of using Meglumine Diatrizoate (MD) to improve the accuracy of diagnosis of cracked teeth on cone‐beam CT (CBCT) images. Methodology: Twenty‐four teeth were cracked artificially by soaking them cyclically in liquid nitrogen and hot water. The number and position of crack lines were evaluated with a dental operating microscope and used as the gold standard. The artificially cracked teeth were then examined using routine scanning (RS) and enhanced scanning (ES) modes, respectively. For the ES mode, MD was painted on the surface of the crack lines, and then, CBCT scanning with the same parameters was performed after 10 min. A radiological graduate student and an experienced radiologist evaluated the presence or absence of crack lines, respectively. The differences between the RS and ES modes were determined and assessed using McNemar's test. Inter‐examiner agreement and intra‐examiner agreement were assessed using kappa analysis. Results: Fifty‐seven crack lines were found in the 24 cracked teeth. In the RS mode, the accuracy of detection of crack lines was 23% (radiological graduate student) and 32% (experienced radiologist), whereas in the ES mode, the accuracy was 61% (radiological graduate student) and 65% (experienced radiologist). The inter‐examiner agreement was 0.693 in RS mode and 0.849 in ES mode. The intra‐examiner agreement was 0.872 and 0.949 for the radiological graduate student in RS and ES mode respectively; and one for the experienced radiologist both in RS and ES mode. Conclusions: Compared with routine scanning mode, more crack lines could be detected in enhanced scanning mode using Meglumine Diatrizoate as a contrast medium. MD could be a potential contrast medium to improve the accuracy of detection of crack lines on CBCT images. [ABSTRACT FROM AUTHOR]

Background: Stereotactic body radiotherapy (SBRT) is the standard care for inoperable early stage non‐small cell lung cancer (NSCLC). The purpose of our study was to investigate whether a prediction model based on cone‐beam CT (CBCT) plus pretreatment CT radiomics features could improve the prediction of tumor control and lung toxicity after SBRT in comparison to a model based on pretreatment CT radiomics features alone. Methods: A total of 34 cases of stage I NSCLC patients who received SBRT were included in the study. The pretreatment planning CT and serial CBCT radiomics features were analyzed using the imaging biomarker explorer (IBEX) software platform. Multivariate logistic regression was conducted for the association between progression‐free survival (PFS), lung toxicity and features. The predictive capabilities of the models based on CBCT and CT features were compared using receiver operating characteristic (ROC) curves. Results: Five CBCT features and two planning CT features were correlated with disease progression. Six CBCT features and two planning CT features were related to lung injury. The ROC curves indicated that the model based on the CBCT plus planning CT features might be better than the model based on the planning CT features in predicting lung injury. The other ROC curves indicated that the model based on the planning CT features was similar to the model based on the CBCT plus planning CT features in predicting disease progression. Conclusions: Both pretreatment CT and CBCT radiomics features could predict disease progression and lung injury. A model with CBCT plus pretreatment CT radiomics features might improve the prediction of lung toxicity in comparison with a model with pretreatment CT features alone. Key points: Significant findings of the study: A model with cone‐beam CT radiomics features plus pre‐treatment CT radiomics features might improve the prediction of lung toxicity after SBRT in stage I NSCLC patients.What this study adds: In the prediction of PFS and lung toxicity in early‐stage NSCLC patients treated with SBRT, CBCT radiomics could be another effective method. [ABSTRACT FROM AUTHOR]

Purpose: To characterize the radiation dose and three‐dimensional (3D) imaging performance of a recently developed mobile, isocentric C‐arm equipped with a flat‐panel detector (FPD) for intraoperative cone‐beam computed tomography (CBCT) (Cios Spin 3D, Siemens Healthineers) and to identify potential improvements in 3D imaging protocols for pertinent imaging tasks. Methods: The C‐arm features a 30 × 30 cm2 FPD and isocentric gantry with computer‐controlled motorization of rotation (0–195°), angulation (±220°), and height (0–45 cm). Geometric calibration was assessed in terms of 9 degrees of freedom of the x‐ray source and detector in CBCT scans, and the reproducibility of geometric calibration was evaluated. Standard and custom scan protocols were evaluated, with variation in the number of projections (100–400) and mAs per view (0.05–1.65 mAs). Image reconstruction was based on 3D filtered backprojection using "smooth," "normal," and "sharp" reconstruction filters as well as a custom, two‐dimensional 2D isotropic filter. Imaging performance was evaluated in terms of uniformity, gray value correspondence with Hounsfield units (HU), contrast, noise (noise‐power spectrum, NPS), spatial resolution (modulation transfer function, MTF), and noise‐equivalent quanta (NEQ). Performance tradeoffs among protocols were visualized in anthropomorphic phantoms for various anatomical sites and imaging tasks. Results: Geometric calibration showed a high degree of reproducibility despite ~19 mm gantry flex over a nominal semicircular orbit. The dose for a CBCT scan varied from ~0.8–4.7 mGy for head protocols to ~6–38 mGy for body protocols. The MTF was consistent with sub‐mm spatial resolution, with f10 (frequency at which MTF = 10%) equal to 0.64 mm−1, 1.0 mm−1, and 1.5 mm−1 for smooth, standard, and sharp filters respectively. Implementation of a custom 2D isotropic filter improved CNR ~ 50–60% for both head and body protocols and provided more isotropic resolution and noise characteristics. The NPS and NEQ quantified the 3D noise performance and provided a guide to protocol selection, confirmed in images of anthropomorphic phantoms. Alternative scan protocols were identified according to body site and task — for example, lower‐dose body protocols (<3 mGy) sufficient for visualization of bone structures. Conclusion: The studies provided objective assessment of the dose and 3D imaging performance of a new C‐arm, offering an important basis for clinical deployment and a benchmark for quality assurance. Modifications to standard 3D imaging protocols were identified that may improve performance or reduce radiation dose for pertinent imaging tasks. [ABSTRACT FROM AUTHOR]