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Purpose: This study evaluates a deep learning-based denoising algorithm to improve the trade-off between radiation dose, image noise, and motion artifacts in TIPSS procedures, aiming for shorter acquisition times and reduced radiation with maintained diagnostic quality. Methods: In this retrospective study, TIPSS patients were divided based on CBCT acquisition times of 6 s and 3 s. Traditional weighted filtered back projection (Original) and an AI denoising algorithm (AID) were used for image reconstructions. Objective assessments of image quality included contrast, noise levels, and contrast-to-noise ratios (CNRs) through place-consistent region-of-interest (ROI) measurements across various critical areas pertinent to the TIPSS procedure. Subjective assessments were conducted by two blinded radiologists who evaluated the overall image quality, sharpness, contrast, and motion artifacts for each dataset combination. Statistical significance was determined using a mixed-effects model (p ≤ 0.05). Results: From an initial cohort of 60 TIPSS patients, 44 were selected and paired. The mean dose-area product (DAP) for the 6 s acquisitions was 5138.50 ± 1325.57 µGy·m2, significantly higher than the 2514.06 ± 691.59 µGym2 obtained for the 3 s series. CNR was highest in the 6 s-AID series (p < 0.05). Both denoised and original series showed consistent contrast for 6 s and 3 s acquisitions, with no significant noise differences between the 6 s Original and 3 s AID images (p > 0.9). Subjective assessments indicated superior quality in 6 s-AID images, with no significant overall quality difference between the 6 s-Original and 3 s-AID series (p > 0.9). Conclusions: The AI denoising algorithm enhances CBCT image quality in TIPSS procedures, allowing for shorter scans that reduce radiation exposure and minimize motion artifacts. [ABSTRACT FROM AUTHOR]

In low-dose cone beam computed tomography (CT), the insufficient number of photons inevitably results in noise, which reduces the accuracy of disease diagnosis. One approach to improving the image quality of CT images acquired using a low-dose protocol involves the utilization of a reconstruction algorithm that efficiently reduces noise. In this study, we modeled the Feldkamp–Davis–Kress (FDK) algorithm using various filters and projection angles and applied it to the reconstruction process using CT simulation. To quantitatively evaluate the quality of the reconstruction images, we measured the coefficient of variation (COV), and signal-to-noise ratio (SNR) in the air, brain, and bone regions to evaluate the noise level. Furthermore, we calculated root mean square error (RMSE), universal image quality index (UQI), and blind/referenceless image spatial quality evaluator (BRISQUE) as similarity and no-reference evaluation. The Hann filter of the FDK algorithm showed superior performance in terms of COV, SNR, RMSE, and UQI compared to the other filters. In addition, when analyzing the COV and SNR results, we observed that image quality increased significantly at projection angles smaller than approximately 2.8°. Moreover, based on BRISQUE results, we confirm that the Shepp–Logan filter exhibited the most superior performance. In conclusion, we believe that the application of the Hann filter in the FDK reconstruction process offers significant advantages in improving the image quality acquired under a low-dose protocol, and we expect that our study will be a preliminary study of no-reference evaluation of CT reconstruction images. [ABSTRACT FROM AUTHOR]

Cone‐beam CT (CBCT) is the most commonly used onboard imaging technique for target localization in radiation therapy. Conventional 3D CBCT acquires x‐ray cone‐beam projections at multiple angles around the patient to reconstruct 3D images of the patient in the treatment room. However, despite its wide usage, 3D CBCT is limited in imaging disease sites affected by respiratory motions or other dynamic changes within the body, as it lacks time‐resolved information. To overcome this limitation, 4D‐CBCT was developed to incorporate a time dimension in the imaging to account for the patient's motion during the acquisitions. For example, respiration‐correlated 4D‐CBCT divides the breathing cycles into different phase bins and reconstructs 3D images for each phase bin, ultimately generating a complete set of 4D images. 4D‐CBCT is valuable for localizing tumors in the thoracic and abdominal regions where the localization accuracy is affected by respiratory motions. This is especially important for hypofractionated stereotactic body radiation therapy (SBRT), which delivers much higher fractional doses in fewer fractions than conventional fractionated treatments. Nonetheless, 4D‐CBCT does face certain limitations, including long scanning times, high imaging doses, and compromised image quality due to the necessity of acquiring sufficient x‐ray projections for each respiratory phase. In order to address these challenges, numerous methods have been developed to achieve fast, low‐dose, and high‐quality 4D‐CBCT. This paper aims to review the technical developments surrounding 4D‐CBCT comprehensively. It will explore conventional algorithms and recent deep learning‐based approaches, delving into their capabilities and limitations. Additionally, the paper will discuss the potential clinical applications of 4D‐CBCT and outline a future roadmap, highlighting areas for further research and development. Through this exploration, the readers will better understand 4D‐CBCT's capabilities and potential to enhance radiation therapy. [ABSTRACT FROM AUTHOR]

Objectives Materials and Methods Results Conclusions To evaluate the influence of multiplanar reconstruction thickness on the detection of peri‐implant bone defects with a standalone zirconia implant and compare it to when another implant is in the vicinity using cone‐beam computed tomography (CBCT).Five dry human mandibles were used to create twenty implant sites in the second premolar and first molar regions. The OP300 Maxio was used to acquire CBCT images (90 kVp, 6.3 mA, 5 × 5 cm FOV, and 0.125 mm3 voxel size) before and after creating 3 mm peri‐implant bone defects in the buccal aspect of the premolar region. Half of the scans featured a single zirconia implant in the premolar region, while the others had two implants in the premolar and molar regions. Three reconstruction thicknesses (0.125 mm, 1 mm, and 2 mm) were considered for the multiplanar reconstruction analyses. Five oral and maxillofacial radiologists assessed the detection of peri‐implant bone defects using a 5‐point scale. Diagnostic parameters were calculated and compared using Two‐way ANOVA (α = .05).The studied factors showed no significant influence on the diagnosis of peri‐implant bone defects (p > .05). Diagnostic performance was notably higher with a single implant, especially with a 2‐mm reconstruction thickness (AUC = 0.88, sensitivity = 0.68, specificity = 0.94). Although the differences were not statistically significant, the results were more modest when two implants were present (AUC = 0.80, sensitivity = 0.58, specificity = 0.82).The presence of an adjacent zirconia implant and variations in reconstruction thickness did not influence the detection of 3 mm buccal peri‐implant bone defects on CBCT images. [ABSTRACT FROM AUTHOR]

Metal artifacts notoriously pose significant challenge in computed tomography (CT), leading to inaccuracies in image formation and interpretation. Artifact reduction tools have been designed to improve cone beam computed tomography (CBCT) image quality by reducing artifacts caused by certain high-density materials. Metal artifact reduction (MAR) tools are specific algorithms that are applied during image reconstruction to minimize or eliminate artifacts degrading CBCT images. The purpose of the study is to evaluate the effect of a MAR algorithm on image quality in CBCT performed for evaluating patients before transarterial radioembolization (TARE). We retrospectively included 40 consecutive patients (aged 65 ± 13 years; 23 males) who underwent 45 CBCT examinations (Allura FD 20, XperCT Roll protocol, Philips Healthcare, Best, The Netherlands) in the setting of evaluation for TARE between January 2017 and December 2018. Artifacts caused by coils, catheters, and surgical clips were scored subjectively by four readers on a 5-point scale (1 = artifacts affecting diagnostic information to 5 = no artifacts) using a side-by-side display of uncorrected and MAR-corrected images. In addition, readers scored tumor visibility and vessel discrimination. MAR-corrected images were assigned higher scores, indicating better image quality. The differences between the measurements with and without MAR were most impressive for coils with a mean improvement of 1.6 points (95%CI [1.5 1.8]) on the 5-point likert scale, followed by catheters 1.4 points (95%CI [1.3 1.5]) and clips 0.7 points (95%CI [0.3 1.1]). Improvements for other artifact sources were consistent but relatively small (below 0.25 points on average). Interrater agreement was good to perfect (Kendall's W coefficient = 0.68–0.95) and was higher for MAR-corrected images, indicating that MAR improves diagnostic accuracy. A metal artifact reduction algorithm can improve diagnostic and interventional accuracy of cone beam CT in patients undergoing radioembolization by reducing artifacts caused by diagnostic catheters and coils, lowering interference of metal artifacts with adjacent major structures, and improving tumor visibility. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusions Cone beam CT (CBCT) is widely utilized in clinics. However, the scatter artifact degrades the CBCT image quality, hampering the expansion of CBCT applications. Recently, beam‐blocker methods have been used for CBCT scatter correction and proved their high cost‐effectiveness.A rotating beam‐blocker (RBB) method for CBCT scatter correction was proposed to complete scatter correction and image reconstruction within a single scan in both full‐ and half‐fan scan scenarios.The RBB consisted of two open regions and two blocked regions, and was designed as a centrosymmetric structure. The open and blocked projections could be alternatively obtained within one single rotation. The open projections were corrected with the scatter signal calculated from the blocked projections, and then used to reconstruct the 3D image via the Feldkamp‐Davis‐Kress algorithm. The performance of the RBB method was evaluated on head and pelvis phantoms in scenarios with and without a bowtie filter. The images obtained from nine repeated scans in each scenario were used to calculate the evaluation metrics including the CT number error, spatial nonuniformity (SNU) and contrast‐to‐noise ratio (CNR).For the head phantom, the CT number error was decreased to <5 after scatter correction from >200 HU before correction when scanned without a bowtie filter, and to <4 from >160 HU when scanned with a full bowtie filter. For the pelvis phantom, the CT number error was reduced to <12 after scatter correction from >250 HU before correction when scanned without a bowtie filter, and to <10 from >190 HU when scanned with a half bowtie filter. After scatter correction, the uniformity and contrast were both improved, resulting in an SNU of >79% decrease and CNR of >2 times increase, respectively.High‐quality CBCT images could be obtained in a single scan after using the proposed RBB method for scatter correction, enabling more accurate image guidance for surgery and radiation therapy applications. With almost no time delay between the successive open and blocked projections, the RBB method could eliminate the motion‐induced anatomical mismatches between the corresponding open and blocked projections and could find particular usefulness in thoracic and abdominal imaging. [ABSTRACT FROM AUTHOR]

Background: Motion induced image artifacts have been the focus of many investigations for x‐ray computed tomography (CT). Methodologies of combating patient motion include the use of gating devices to optimize the data acquisition, reduction in patient scan time via faster gantry rotation and large detector coverage, and the development of advanced reconstruction and post‐processing algorithms to minimize motion artifacts. Purpose: Previously proposed approaches are generally "global" in nature in that motion is characterized for the entire image. It is well known, however, that the presence of motion artifact in a CT image is highly nonuniform. When there is a lack of automated and quantitative local measure indicating the presence and the severity of motion artifacts in a local region, the quality of the reconstructed images depends heavily on the CT operator's rigor and experience. Even when an operator is informed of the presence of motion, little information is provided about the nature of the motion artifact to understand its relevance to the clinical task at hand. In this paper, we propose an image‐space spatial‐ and temporal‐consistency metric (CM) to detect and characterize the local motion. Method: In a non‐rigid human organ, such as the lung, there are many small and rigid objects (target objects), such as blood vessels and nodules, distributed throughout the organ. If motion can be characterized for these target objects, we obtain a complete motion map for the organ. To accomplish this, a preliminary image reconstruction is carried out to identify the target objects and establish region‐of‐interests for consistency‐metric calculation. The CM is then obtained based on the backprojected intensity difference between the object region and its circular background. For a stationary object, the accumulation of this quantity over views is linear. When a target object moves, nonlinear behavior exhibits and a quantitative measure of linearity indicates the severity of motion. Results: Extensive computer simulation was utilized to confirm the validity of the theory. These tests stress the sensitivity of the proposed CM to the target object size, object shape, in‐plane motion, cross‐plane motion, cone‐beam effect, and complex background. Results confirm that the proposed approach is robust under different testing conditions. The proposed CM is further validated using a cardiac scan of a swine, and the proposed CM correlates well with the visual inspection of the artifact in the reconstructed images. Conclusions: In this paper, we have demonstrated the efficacy of the proposed CM for motion detection. Unlike previously proposed approaches where the consistency condition is derived for the entire image or the entire imaging volume, the proposed metric is well localized so that different zones in a patient anatomy can be individually characterized. In addition, the proposed CM provides a quantitative measure on a view‐by‐view basis so that the severity of motion is consistently estimated over time. Such information can be used to optimize the image reconstruction process and minimize the motion artifact. [ABSTRACT FROM AUTHOR]

Background: High-quality images with minimum radiation dose are considered a challenge in Computed Tomography (CT) scans. Objective: The current study aimed to assess the efficacy of the Iterative Reconstruction in Image Space (IRIS) algorithm combined with Automatic Tube Current Modulation (ATCM) compared to Filtered Back Projection (FBP) in brain CT scans. Material and Methods: In this cross-sectional study, 200 patients underwent to brain CT scan, and images were then reconstructed using both FBP and IRIS. The CT Number (CTN), noise, and Signal-to-Noise Ratio (SNR) were computed for different tissues from CT images. The performance of two algorithms under different exposure conditions was evaluated using a water phantom. Two experienced radiologists assessed the image quality. Volume CT Dose Index (CTDIvol) and Dose Length Product (DLP) were recorded for each scan. Results: FBP reconstruction exhibited higher noise and lower SNR compared to IRIS, both with and without ATCM. Noise levels significantly increased for FBP combined with ATCM. Subjective analysis showed higher performance for IRIS without ATCM compared to other approaches. The mean CTDIvol with and without ATCM was 20.04±3.33 and 36.37±4.65 mGy, respectively. In the phantom study, the noise with IRIS remained lower than that with FBP even with a 42% dose reduction. Conclusion: IRIS algorithm can preserve the image quality when radiation dose is significantly reduced by ATCM in brain CT scan. Implementation of IRIS combined with ATCM is recommended for brain CT examinations. [ABSTRACT FROM AUTHOR]

Ultrafast X-ray computed tomography is an advanced imaging technique for multiphase flows. It has been used with great success for studying gas–liquid as well as gas–solid flows. Here, we apply this technique to analyze density-driven particle segregation in a rotating drum as an exemplary use case for analyzing industrial particle mixing systems. As glass particles are used as the denser of two granular species to be mixed, beam hardening artefacts occur and hamper the data analysis. In the general case of a distribution of arbitrary materials, the inverse problem of image reconstruction with energy-dependent attenuation is often ill-posed. Consequently, commonly known beam hardening correction algorithms are often quite complex. In our case, however, the number of materials is limited. We therefore propose a correction algorithm simplified by taking advantage of the known material properties, and demonstrate its ability to improve image quality and subsequent analyses significantly. [ABSTRACT FROM AUTHOR]

Objective. The aim of this study was to reconstruct volumetric computed tomography (CT) images in real-time from ultra-sparse two-dimensional x-ray projections, facilitating easier navigation and positioning during image-guided radiation therapy. Approach. Our approach leverages a voxel-sapce-searching Transformer model to overcome the limitations of conventional CT reconstruction techniques, which require extensive x-ray projections and lead to high radiation doses and equipment constraints. Main results. The proposed XTransCT algorithm demonstrated superior performance in terms of image quality, structural accuracy, and generalizability across different datasets, including a hospital set of 50 patients, the large-scale public LIDC-IDRI dataset, and the LNDb dataset for cross-validation. Notably, the algorithm achieved an approximately 300% improvement in reconstruction speed, with a rate of 44 ms per 3D image reconstruction compared to former 3D convolution-based methods. Significance. The XTransCT architecture has the potential to impact clinical practice by providing high-quality CT images faster and with substantially reduced radiation exposure for patients. The model's generalizability suggests it has the potential applicable in various healthcare settings. [ABSTRACT FROM AUTHOR]

The Feldkamp, Davis and Kress algorithm is a computationally efficient reconstruction method for three-dimensional cone-beam computed tomography. However, it suffers from severe artefacts when the number of projections is insufficient. Although recent deep learning–based methods have succeeded in reconstructing such sparse-view projections, it is still challenging to reconstruct a large 3D volume efficiently because of heavy memory consumption and difficulty in obtaining sufficient training data. Therefore, we propose a deep learning method to overcome these drawbacks. Our method consecutively reconstructs short bars in 3D CT volume using the intensities of pixels on the moving trajectory projected on the detector of these bars. Then, we feed such pixel intensities into a neural network and train it to simulate the filtering and back-projection processes of the FDK algorithm. Since the reconstruction volume is separated into bars, the neural network can be used with only a small amount of memory. Furthermore, the network can be trained sufficiently with only a few training samples because plenty of bar data can be extracted even from a single CT image. We experimentally demonstrate that our approach works efficiently for both simulated and actual sparse-view CBCT data using training data extracted from only a single CT image. [ABSTRACT FROM AUTHOR]

Purpose: To prospectively evaluate whether Lung-RADS classification and volumetric nodule assessment were feasible with ultralow-dose (ULD) chest CT scans with deep learning image reconstruction (DLIR). Methods: The institutional review board approved this prospective study. This study included 40 patients (mean age, 66±12 years; 21 women). Participants sequentially underwent LDCT and ULDCT (CTDIvol, 0.96±0.15 mGy and 0.12±0.01 mGy) scans reconstructed with the adaptive statistical iterative reconstruction-V 50% (ASIR-V50) and DLIR. CT image quality was compared subjectively and objectively. The pulmonary nodules were assessed visually by two readers using the Lung-RADS 1.1 and automatically using a computerized assisted tool. Results: DLIR provided a significantly higher signal-to-noise ratio for LDCT and ULDCT images than ASIR-V50 (all P <.001). In general, DLIR showed superior subjective image quality for ULDCT images (P <.001) and comparable quality for LDCT images compared to ASIR-V50 (P =.01–1). The per-nodule sensitivities of observers for Lung-RADS category 3–4 nodules were 70.6–88.2% and 64.7–82.4% for DLIR-LDCT and DLIR-ULDCT images (P = 1) and categories were mostly concordant within observers. The per-nodule sensitivities of the computer-assisted detection for nodules ≥4 mm were 72.1% and 67.4% on DLIR-LDCT and ULDCT images (P =.50). The 95% limits of agreement for nodule volume differences between DLIR-LDCT and ULDCT images (-85.6 to 78.7 mm3) was similar to the within-scan nodule volume differences between DLIR- and ASIR-V50-LDCT images (-63.9 to 78.5 mm3), with volume differences smaller than 25% in 88.5% and 92.3% of nodules, respectively (P =.65). Conclusion: DLIR enabled comparable Lung-RADS and volumetric nodule assessments on ULDCT images to LDCT images. [ABSTRACT FROM AUTHOR]

Background: Improving imaging speed has always been the focus of research in CT technology, which is related to the radiation dose and imaging quality of moving organs, including heart and blood vessels. However, it is difficult to achieve further improvement by increasing the rotation speed of the gantry due to its structural strength limitation. Differing from the conventional CTs, the static CT employs dozens of ray sources to acquire projection data from different angular ranges, and each source only needs to be rotated in a small range to finish a full 360° scan, thus greatly increasing the scanning speed. Purpose: As sources of static CT need to be evenly distributed over 360°, the sources and detectors have to be arranged on two parallel rings independently. Such a geometry can be considered as a special case of CT systems with a significantly large cone angle, that is, a part of the detector is missing in the vicinity of the mid‐plane. Due to restriction of upper and lower bounds of the cone angle of the static CT, there are uneven projection data varying in each portion of the reconstruction volume, the conventional analytical or iterative reconstruction methods may introduce artifacts in the reconstructed outcomes. Methods: Following the weighting approach extended FDK (xFDK) by Grimmer et al., we propose an improved bilateral xFDK algorithm (bixFDK), which focuses on the reconstruction of the expanded volume. With the same philosophy as xFDK in terms of weighting function design, bixFDK takes the longitudinal offset of the detector with respect to the source into consideration, making our method applicable to a wide range of CT geometries, especially for the static CT. Based on the proposed bixFDK, a new iterative scheme bixFDK‐IR is also constructed to extend the applications to a wide range of scan protocols such as sparse‐view scan. Results: The proposed method has been validated with the simulated phantom data and the actual clinical data of the static CT, and demonstrates that it can ensure good image quality and enlarge the reconstruction volume in z‐direction of the static CT. Conclusions: The bixFDK algorithm is an ideal reconstruction approach for static CT geometry, and the iterative scheme of bixFDK‐IR is applicable to a wide range of CT geometries and scan protocols, thus providing a wide range of application scenarios. [ABSTRACT FROM AUTHOR]

This paper describes the mathematical structure of the ill-posed nonlinear inverse problem of low-dose dental cone-beam computed tomography (CBCT) and explains the advantages of a deep learning-based approach to the reconstruction of computed tomography images over conventional regularization methods. This paper explains the underlying reasons why dental CBCT is more ill-posed than standard computed tomography. Despite this severe ill-posedness, the demand for dental CBCT systems is rapidly growing because of their cost competitiveness and low radiation dose. We then describe the limitations of existing methods in the accurate restoration of the morphological structures of teeth using dental CBCT data severely damaged by metal implants. We further discuss the usefulness of panoramic images generated from CBCT data for accurate tooth segmentation. We also discuss the possibility of utilizing radiation-free intra-oral scan data as prior information in CBCT image reconstruction to compensate for the damage to data caused by metal implants. [ABSTRACT FROM AUTHOR]

Background: Quantitative accuracy is critical for expanding the role of cone beam CT (CBCT) imaging from target localization to quantitative treatment monitoring and plan adaptations in radiation therapy. Despite advances in CBCT image quality improvement methods, quantitative accuracy gap between CBCT and multi‐detector CT (MDCT) remains. Purpose: In this work, a physics‐driven approach was investigated that combined robust scatter rejection, raw data correction and iterative image reconstruction to further improve CBCT image quality and quantitative accuracy, referred to as quantitative CBCT (qCBCT). Methods: QCBCT approach includes tungsten 2D antiscatter grid hardware, residual scatter correction with grid‐based scatter sampling, image lag, and beam hardening correction for offset detector geometry linac‐mounted CBCT. Images were reconstructed with iterative image reconstruction to reduce image noise. qCBCT was evaluated using a variety of phantoms to investigate the effect of object size and its composition on image quality, and image quality was benchmarked against clinical CBCT and gold standard MDCT images used for treatment planning. Results: QCBCT provided statistically significant improvement in CT number accuracy and reduced image artifacts when compared to clinical CBCT images. When compared to gold standard MDCT, mean HU errors in qCBCT and clinical CBCT were 17 ± 9 and 38 ± 29 HU, respectively. Magnitude of phantom size dependent HU variations were comparable between MDCT and qCBCT images. With iterative reconstruction, contrast‐to‐noise ratio improved by 25% when compared to clinical CBCT protocols. Conclusions: Combination of novel scatter suppression techniques and other data correction methods in qCBCT provided CT number accuracy comparable to gold standard MDCT used for treatment planning. This approach may potentially improve CBCT's promise in fulfilling the tasks that demand high quantitative accuracy, such as online dose calculations and treatment response assessment, in image guided radiation therapy. [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]

Computed tomography (CT) is a method for synthesizing volumetric or cross-sectional images of an object from a collection of projections. Popular reconstruction methods for CT are based on the assumption that the projection geometry, which describes the relative location and orientation of the radiation source, object, and detector for each projection, is exactly known. However, in practice, these geometric parameters are estimated quantities with uncertainty. A failure to accurately estimate the geometry may lead to reconstructions with severe misalignment artifacts that significantly decrease their scientific or diagnostic value. We propose a novel reconstruction method that jointly estimates the reconstruction and the center-of-rotation offset for fan-beam tomography. The reconstruction method is based on a Bayesian approach that yields a point estimate for the reconstruction and center-of-rotation offset and, in addition, provides valuable information regarding their uncertainty. This is achieved by approximately sampling from the joint posterior distribution of the reconstruction and center-of-rotation offset using a hierarchical Gibbs sampler. Our methodology is highly flexible and can be adapted to other geometric parameters and/or scan modes. Through numerical experiments based on real tomographic data, we compare the proposed Bayesian method to two existing approaches to the problem of correcting the geometry and demonstrate that our method achieves comparable or better results under challenging conditions. [ABSTRACT FROM AUTHOR]

Computed tomography (CT) has been developed as a nondestructive technique for observing minute internal images in samples. It has been difficult to obtain photorealistic (clean or clear) CT images due to various unwanted artifacts generated during the CT scanning process, along with the limitations of back-projection algorithms. Recently, an iterative optimization algorithm has been developed that uses an entire sinogram to reduce errors caused by artifacts. In this paper, we introduce a new quantum algorithm for reconstructing CT images. This algorithm can be used with any type of light source as long as the projection is defined. Assuming an experimental sinogram produced by a Radon transform, to find the CT image of this sinogram, we express the CT image as a combination of qubits. After acquiring the Radon transform of the undetermined CT image, we combine the actual sinogram and the optimized qubits. The global energy optimization value used here can determine the value of qubits through a gate model quantum computer or quantum annealer. In particular, the new algorithm can also be used for cone-beam CT image reconstruction and for medical imaging. [ABSTRACT FROM AUTHOR]

Dental CBCT and panoramic imaging play a pivotal role in dental diagnosis and treatment planning, alongside the indispensable use of computed tomography (CT) and X-ray imaging in dentistry, particularly for surgical planning. Given the widespread adoption of dental cone beam CT in clinics today, we explore a novel approach in this study—utilizing CT's three-dimensional (3D) data to reconstruct a two-dimensional (2D) panoramic dental image. This method eliminates the requirement for an extra panoramic scan. In this work, we propose a novel framework to generate an enhanced and extended 2D panoramic view by using the dental arch extracted from 3D CBCT. Our method involves segmenting the patient's dental arch from their 3D CBCT image by identifying horizontal slices with above-average intensity, followed by morphological operations, including dilation, Gaussian filtering, and skeletonization, to delineate the dental arch line. Additionally, we extend the dental arch beyond the wisdom teeth using quadratic curve fitting. Finally, we employ Maximum Intensity Projection on rotated cubic segments aligned with the dental arch curve to produce captivating panoramic images. The panoramic view produced using our proposed method, when compared to the results obtained from BlueSky and OpenInventor, exhibited superior enhancements and greater accuracy in panoramic visualization. [ABSTRACT FROM AUTHOR]

Introduction: This study aimed to compare the image quality and volume dose index of head CT scan between two adaptive statistical iterative reconstructions (ASIR) and the filtered back projection (FBP) algorithms. Material and Methods: CT number, noise and signal to noise ratio(SNR) for white matter(WM), gray matter(WM), cerebrospinal fluid (CSF) and skull bone were investigated in brain CT scans of 60 patients. All images were reconstructed by FBP and ASIR 40% algorithms. A water phantom was also used to compare the average CT number; noise, signal-to-noise (SNR), and contrast-to-noise ratio (CNR) between algorithms under different acquisition parameters. Volume computed tomography dose index (CTDIvol) and (dose-length product) DLP were obtained from scanner software. Data were analyzed by T-test and Mann-Whitney statistical test with a significance level of less than 0.05. Results: Image noise of gray matter, CSF and skull bone was significantly lower for ASIR algorithm (P<0.05). The difference in SNR for white matter and gray matter was not significant between the two algorithms but it was higher for CSF and bone for ASIR. In phantom study, Image noise, CTDIvol and DLP in both axial and spiral scan modes were higher for FBP algorithm (P<0.05). In addition, there was no significant difference in SNR and CNR between the two algorithms (P>0.05). Conclusion: ASIR algorithm reduces the dose and image noise in head CT scan compared to the filtered back projection. In addition, using ASIR algorithm the image noise does not increase with lower mA. [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]

Refusal of autopsy occurs for various reasons, including religious beliefs and the risk of infectious diseases such as coronavirus disease 2019. Dental autopsy involves invasive procedures, including incision and access openings in the oral cavity. Radiographic techniques can be used as an alternative to the conventional autopsy process in the field of forensic odontology, providing a non-invasive approach that does not involve tissue damage. The current study aimed to analyse the current status of the applicability of oral and maxillofacial radiology for virtual forensic odontological autopsy. A systematic review was conducted in accord with the Preferred Reporting Items for Systematic Review and Meta-Analysis protocol. The literature search was conducted from December 2021 to October 2022 using the PubMed, ScienceDirect, and SAGE Journals databases. Article selection was carried out by eliminating duplication, screening titles and abstracts, and reading the entire content of the article. A thematic analysis method was used to identify themes in the collected data. A total of 15 articles were included, and several uses of oral and maxillofacial radiography in virtual autopsy and forensic odontology were identified. Four techniques were identified that can be used for virtual autopsy in forensic odontology. The use of computed tomography (CT) scanning for virtual autopsy was reported in six articles, cone beam CT was reported in five articles, magnetic resonance imaging was reported in two articles, and multidetector CT was reported in two articles. In the studies included in this review, the identified oral and maxillofacial radiograph techniques that are used as the fundamental methods of virtual autopsy in forensic odontology are CT scanning (including multidetector CT), cone beam CT, and magnetic resonance imaging. The different methods of oral and maxillofacial radiography for virtual autopsy were identified as having advantages as well as limitations in their use in forensic odontology. Most of the included studies indicated that the virtual autopsy approach cannot yet stand alone as an identification method, but provides a useful adjunct for gathering dental evidence. Key points Virtual autopsy is a valuable tool for non-invasive autopsies in the field of forensic odontology. CT scans (including MDCT), CBCT, and MRI can be used for virtual autopsy in the identification of individuals based on dental evidence. The use of oral and maxillofacial radiographs can shorten the time required for forensic odontology activities. Artefacts caused by amalgam and other metal restorations on radiographic images can raise doubts in the identification process. Although the use of oral and maxillofacial radiology has many advantages, it cannot yet stand alone as an identification method. [ABSTRACT FROM AUTHOR]

This report reviews the image acquisition and reconstruction characteristics of C‐arm Cone Beam Computed Tomography (C‐arm CBCT) systems and provides guidance on quality control of C‐arm systems with this volumetric imaging capability. The concepts of 3D image reconstruction, geometric calibration, image quality, and dosimetry covered in this report are also pertinent to CBCT for Image‐Guided Radiation Therapy (IGRT). However, IGRT systems introduce a number of additional considerations, such as geometric alignment of the imaging at treatment isocenter, which are beyond the scope of the charge to the task group and the report. Section 1 provides an introduction to C‐arm CBCT systems and reviews a variety of clinical applications. Section 2 briefly presents nomenclature specific or unique to these systems. A short review of C‐arm fluoroscopy quality control (QC) in relation to 3D C‐arm imaging is given in Section 3. Section 4 discusses system calibration, including geometric calibration and uniformity calibration. A review of the unique approaches and challenges to 3D reconstruction of data sets acquired by C‐arm CBCT systems is give in Section 5. Sections 6 and 7 go in greater depth to address the performance assessment of C‐arm CBCT units. First, Section 6 describes testing approaches and phantoms that may be used to evaluate image quality (spatial resolution and image noise and artifacts) and identifies several factors that affect image quality. Section 7 describes both free‐in‐air and in‐phantom approaches to evaluating radiation dose indices. The methodologies described for assessing image quality and radiation dose may be used for annual constancy assessment and comparisons among different systems to help medical physicists determine when a system is not operating as expected. Baseline measurements taken either at installation or after a full preventative maintenance service call can also provide valuable data to help determine whether the performance of the system is acceptable. Collecting image quality and radiation dose data on existing phantoms used for CT image quality and radiation dose assessment, or on newly developed phantoms, will inform the development of performance criteria and standards. Phantom images are also useful for identifying and evaluating artifacts. In particular, comparing baseline data with those from current phantom images can reveal the need for system calibration before image artifacts are detected in clinical practice. Examples of artifacts are provided in Sections 4, 5, and 6. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the efficacy of low-dose CT (LDCT) with deep learning image reconstruction (DLIR) for the surveillance of pancreatic cystic lesions (PCLs) compared with standard-dose CT (SDCT) with adaptive statistical iterative reconstruction (ASIR-V). Methods: The study enrolled 103 patients who underwent pancreatic CT for follow-up of incidentally detected PCLs. The CT protocol included LDCT in the pancreatic phase with 40% ASIR-V, DLIR at medium (DLIR-M) and high levels (DLIR-H), and SDCT in the portal-venous phase with 40% ASIR-V. The overall image quality and conspicuity of PCLs were qualitatively assessed using five-point scales by two radiologists. The size of PCLs, presence of thickened/enhancing walls, enhancing mural nodules, and main pancreatic duct dilatation were reviewed. CT noise and cyst-to-pancreas contrast-to-noise ratio (CNR) were measured. Qualitative and quantitative parameters were analyzed using the chi-squared test, one-way ANOVA, and t-test. Additionally, interobserver agreement was analyzed using the kappa and weighted-kappa statistics. Results: The volume CT dose-indexes in LDCT and SDCT were 3.0 ± 0.6 mGy and 8.4 ± 2.9 mGy, respectively. LDCT with DLIR-H showed the highest overall image quality, the lowest noise, and the highest CNR. The PCL conspicuity in LDCT with either DLIR-M or DLIR-H was not significantly different from that in SDCT with ASIR-V. Other findings depicting PCLs also revealed no significant differences between LDCT with DLIR and SDCT with ASIR-V. Moreover, the results revealed good or excellent interobserver agreement. Conclusion: LDCT with DLIR has a comparable performance with SDCT for the follow-up of incidentally detected PCLs. [ABSTRACT FROM AUTHOR]

Background: This paper describes the protocol for the Nano X Image Guidance (Nano X IG) trial, a single-institution, clinical imaging study. The Nano X is a prototype fixed-beam radiotherapy system developed to investigate the feasibility of a low-cost, compact radiotherapy system to increase global access to radiation therapy. This study aims to assess the feasibility of volumetric image guidance with cone beam computed tomography (CBCT) acquired during horizontal patient rotation on the Nano X radiotherapy system. Methods: In the Nano X IG study, we will determine whether radiotherapy image guidance can be performed with the Nano X radiotherapy system where the patient is horizontally rotated while scan projections are acquired. We will acquire both conventional CBCT scans and Nano X CBCT scans for 30 patients aged 18 and above and receiving radiotherapy for head/neck or upper abdomen cancers. For each patient, a panel of experts will assess the image quality of Nano X CBCT scans against conventional CBCT scans. Each patient will receive two Nano X CBCT scans to determine the image quality reproducibility, the extent and reproducibility of patient motion and assess patient tolerance. Discussion: Fixed-beam radiotherapy systems have the potential to help ease the current shortfall and increase global access to radiotherapy treatment. Advances in image guidance could facilitate fixed-beam radiotherapy using horizontal patient rotation. The efficacy of this radiotherapy approach is dependent on our ability to image and adapt to motion due to rotation and for patients to tolerate rotation during treatment. Trial registration: ClinicalTrials.gov, NCT04488224. Registered on 27 July 2020. [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]

Dental CBCT and panoramic images are important imaging modalities used in dental diagnosis and treatment planning. In order to acquire a panoramic image without an additional panoramic scan, in this study, we proposed a method of reconstructing a panoramic image by extracting panoramic projection data from dental CBCT projection data. After specifying the patient's dental arch from the patient's CBCT image, panoramic projection data are extracted from the CBCT projection data along the appropriate panoramic scan trajectory that fits the dental arch. A total of 40 clinical human datasets and one head phantom dataset were used to test the proposed method. The clinical human dataset used in this study includes cases in which it is difficult to reconstruct panoramic images from CBCT images, such as data with severe metal artifacts or data without teeth. As a result of applying the panoramic image reconstruction method proposed in this study, we were able to successfully acquire panoramic images from the CBCT projection data of various patients. The proposed method acquires a universally applicable panoramic image that is less affected by CBCT image quality and metal artifacts by extracting panoramic projection data from dental CBCT data and reconstructing a panoramic image. [ABSTRACT FROM AUTHOR]

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

Simple Summary: Cone-beam computed tomography (CBCT) not only plays an important role in image-guided radiation therapy (IGRT) but also has the potential for dose calculation. Because CBCT suffers from poor image quality and uncertainties in the Hounsfield unit (HU) values, the accuracy of dose calculation with CBCT is insufficient for clinical use. This study investigated deep learning approaches that utilize a generative adversarial network (GAN) with an additional registration network (RegNet) to generate synthetic CT (sCT) from CBCT. Our study addressed the limitation of having paired CT and CBCT with their anatomy perfectly aligned for supervised training. RegNet can dynamically estimate the correct labels, enabling supervised learning with noisy labels, whereas GAN learns the bidirectional mapping from CBCT to CT. The HU values for sCT were sufficiently accurate for dose calculation, while preserving the anatomy of CBCT with clear structural boundaries. Recently, deep learning with generative adversarial networks (GANs) has been applied in multi-domain image-to-image translation. This study aims to improve the image quality of cone-beam computed tomography (CBCT) by generating synthetic CT (sCT) that maintains the patient's anatomy as in CBCT, while having the image quality of CT. As CBCT and CT are acquired at different time points, it is challenging to obtain paired images with aligned anatomy for supervised training. To address this limitation, the study incorporated a registration network (RegNet) into GAN during training. RegNet can dynamically estimate the correct labels, allowing supervised learning with noisy labels. The study developed and evaluated the approach using imaging data from 146 patients with head and neck cancer. The results showed that GAN trained with RegNet performed better than those trained without RegNet. Specifically, in the UNIT model trained with RegNet, the mean absolute error (MAE) was reduced from 40.46 to 37.21, the root mean-square error (RMSE) was reduced from 119.45 to 108.86, the peak signal-to-noise ratio (PSNR) was increased from 28.67 to 29.55, and the structural similarity index (SSIM) was increased from 0.8630 to 0.8791. The sCT generated from the model had fewer artifacts and retained the anatomical information as in CBCT. [ABSTRACT FROM AUTHOR]

Purpose: The study aims to use cone beam computed tomography (CBCT) to (1) define the virtual valid length of pterygoid implants in maxillary atrophic patients from the prosthetic prioritized driven position and (2) measure the implant length engaged in the pterygoid process according to the HU difference of the pterygoid maxillary junction. Materials and methods: Virtual pterygoid implants were planned with CBCT of maxillary atrophic patients in the software. The entry and angulation of the implant were planned according to the prosthetic prioritized driven position in the 3D reconstruction image. The planned implant length and the valid length defined as the implant between the pterygoid maxillary junction and pterygoid fossa were recorded. The relationship between the implant and sinus cavity was also evaluated. Results: A total of 120 CBCT samples were enrolled and virtually planned. The mean age of the patients was 56.2 ± 13.2 years. One hundred and sixteen samples could successfully place virtual implants according to the criterion. The mean implant length and mean implant length beyond the pterygoid maxillary junction were 16.3 ± 4.2 mm (range, 11.5–18 mm) and 7.1 ± 3.3 mm (range, 1.5–11.4 mm), respectively. Ninety percent of virtually planned implants had a close relationship with the sinus cavity, and implants exhibited longer lengths when they had no relation with the sinus. Conclusion: From a prosthetic prioritized driven position with fixed entry and angulation, pterygoid implants achieve adequate bone anchorage length beyond the pterygoid maxillary junction. Due to the individual anatomy and the volume of the maxillary sinus, the implants presented a different positional relationship with the maxillary sinus. [ABSTRACT FROM AUTHOR]

In-house fabrication of three-dimensional (3D) models for medical use has become easier in recent years. Cone beam computed tomography (CBCT) images are increasingly used as source data for fabricating osseous 3D models. The creation of a 3D CAD model begins with the segmentation of hard and soft tissues of the DICOM images and the creation of an STL model; however, it can be difficult to determine the binarization threshold in CBCT images. In this study, how the different CBCT scanning and imaging conditions of two different CBCT scanners affect the determination of the binarization threshold was evaluated. The key to efficient STL creation through voxel intensity distribution analysis was then explored. It was found that determination of the binarization threshold is easy for image datasets with a large number of voxels, sharp peak shapes, and narrow intensity distributions. Although the intensity distribution of voxels varied greatly among the image datasets, it was difficult to find correlations between different X-ray tube currents or image reconstruction filters that explained the differences. The objective observation of voxel intensity distribution may contribute to the determination of the binarization threshold for 3D model creation. [ABSTRACT FROM AUTHOR]

Background: Rapid kV cone-beam computed tomography (CBCT) scans are achievable in under 20 s on select linear accelerator systems to generate volumetric images in three dimensions (3D). Daily pre-treatment four-dimensional CBCT (4DCBCT) is recommended in image-guided lung radiotherapy to mitigate the detrimental effects of respiratory motion on treatment quality. Purpose: To demonstrate the potential for thoracic 4DCBCT reconstruction using projection data that was simulated using a clinical rapid 3DCBCT acquisition protocol. Methods: We simulated conventional (1320 projections over 4 min) and rapid (491 projections over 16.6 s) CBCT acquisitions using 4D computed tomography (CT) volumes of 14 lung cancer patients. Conventional acquisition data were reconstructed using the 4D Feldkamp-Davis-Kress (FDK) algorithm. Rapid acquisition data were reconstructed using 3DFDK, 4DFDK, and MotionCompensated FDK (MCFDK). Image quality was evaluated using Contrastto-Noise Ratio (CNR), Tissue Interface Width (TIW), Root-Mean-Square Error (RMSE), and Structural SIMilarity (SSIM). Results: The conventional acquisition 4DFDK reconstructions had median phase averaged CNR, TIW, RMSE, and SSIM of 2.96, 8.02 mm, 83.5, and 0.54, respectively. The rapid acquisition 3DFDK reconstructions had median CNR, TIW, RMSE, and SSIM of 2.99, 13.6 mm, 112, and 0.44 respectively. The rapid acquisition MCFDK reconstructions had median phase averaged CNR, TIW, RMSE, and SSIM of 2.98, 10.2 mm, 103, and 0.46, respectively. Rapid acquisition 4DFDK reconstruction quality was insufficient for any practical use due to sparse angular projection sampling. Conclusions: Results suggest that 4D motion-compensated reconstruction of rapid acquisition thoracic CBCT data are feasible with image quality approaching conventional acquisition CBCT data reconstructed using standard 4DFDK. [ABSTRACT FROM AUTHOR]

Background: Children have a potential risk from radiation exposure because they are more sensitive to radiation than adults. Objective: The purpose of this work is to estimate image quality according to tube voltage (kV) and radiation dose in pediatric computed tomography (CT) using deep learning reconstruction (DLR). Methods: Phantom images of children and adults were obtained for kV, radiation dose, and image reconstruction methods. The CT emits a fan beam to the opposite detector, and the geometry of the detector was symmetrical. Phantom images of children and adults were acquired at a volume CT dose index (CTDIvol) from 0.5 to 10.0 mGy for tube voltages at 80, 100, and 120 kV. A DLR was used to reconstruct the phantom image, and filtered back projection (FBP) and iterative reconstruction (IR) were also performed for comparison with the DLR. Image quality was evaluated by measuring the contrast-to-noise ratio (CNR) and noise. Results: Under the same imaging conditions, the DLR images of pediatric and adult phantoms generally provided improved CNR and noise compared with the FBP and IR images. At a similar CNR and noise, the FBP, IR, and DLR of the pediatric images showed a dose reduction compared with the FBP, IR, and DLR of the adult images, respectively. In terms of the effect of tube voltage, the CNR of the 100 kV DLR images was higher than that of the 120 kV DLR images. Conclusion: According to the results, since pediatric CT images maintain the same image quality at lower doses compared with adult CT images, DLR can improve image quality while reducing the radiation dose in children's abdominal CT scans. [ABSTRACT FROM AUTHOR]

Featured Application: The development of this co-registration method that combines CBCT with photographic images and subsequent realistic image reconstruction can be used during the orthognathic surgical planning stage. Therefore, both orthodontists and maxillofacial surgeons will obtain more realistic and precise result predictions, allowing them to better clarify the patient's surgical prognosis. Orthognathic surgery is a procedure used to correct intermaxillary discrepancies, thus promoting significant improvements in chewing and breathing. During the surgical planning stage, orthodontists often use two-dimensional imaging techniques. The assessment is based on CBCT images and dental cast models to overcome these limitations; however, the evaluation of soft tissues remains complex. The aim of the present study was to develop a co-registration method of CBCT and photo images that would result in realistic facial image reconstruction. CBCT images were three-dimensionally rendered, and the soft tissues were subsequently segmented resulting in the cranial external surface. A co-registration between the obtained surface and a frontal photo of the subject was then carried out. From this mapping, a photorealistic model capable of replicating the features of the face was generated. To assess the quality of this procedure, seven orthodontists were asked to fill in a survey on the models obtained. The survey results showed that orthodontists consider the three-dimensional model obtained to be realistic and of high quality. This process can automatically obtain a three-dimensional model from CBCT images, which in turn may enhance the predictability of surgical-orthognathic planning. [ABSTRACT FROM AUTHOR]

Background: Cone‐beam computed tomography (CBCT) allows for patient setup and positioning, and potentially dose verification or adaptive replanning prior to each treatment delivery. Poor CBCT image quality due to scatter artifacts and patient motion has been a major limiting factor. A new image reconstruction algorithm was recently clinically implemented for improving image quality through iterative reconstruction (iCBCT). Purpose: This study aims to characterize iCBCT image quality, establish image value (HU)‐to‐relative electron density (RED) calibration curves for dose calculation, and assess the dosimetric accuracy for different anatomical sites. Material and methods: Both conventional CBCT and iCBCT scans were acquired from a Varian TrueBeam On‐Board Imager system. A Catphan 604 phantom was scanned to compare image quality between the traditional Feldkamp–Davis–Kress (FDK) and novel iterative reconstruction techniques. Computerized Imaging Reference Systems (CIRS) electron density phantom was used to construct site‐specific HU‐RED curves corresponding to various scan settings. The CIRS Dynamic Thorax phantom, Rando pelvis phantom, and BrainLab head phantom were used for assessing dosimetric accuracy calculated on iCBCT images, compared to that on traditional FDK‐based CBCT images. All phantoms were scanned on a computed tomography (CT) to obtain baseline HU values for comparison. Results: Test results obtained from Catphan showed statistically significant improvement with iCBCT, compared to FDK CBCT. Average HU differences from the baseline CT values were improved to within ±30 HU for iCBCT, compared to FDK CBCT for phantom studies. Dose calculated on iCBCT for both phantoms and patient cases directly using baseline HU‐RED calibration from CT showed 0.5%–2.0% accuracy from the baseline dose calculated on CT, which is comparable to doses calculated using site‐specific HU‐RED calibration curves. Conclusion: iCBCT provides improved image quality, improved HU accuracy compared to CT baseline, and has potential to provide online dose verification as part of the adaptive radiotherapy workflow directly using the baseline HU‐RED calibration curve from CT. [ABSTRACT FROM AUTHOR]

4D cone-beam computed tomography (CBCT) is an important imaging modality in image-guided radiation therapy to address the motion-induced artifacts caused by organ movements during the respiratory process. However, due to the extremely sparse projection data for each temporal phase, 4D CBCT reconstructions will suffer from severe streaking artifacts. Therefore, to tackle the streak artifacts and provide high-quality images, we proposed a framework termed Prior-Regularized Iterative Optimization Reconstruction (PRIOR) for 4D CBCT. The PRIOR framework combines the physics-based model and data-driven method simultaneously, with powerful feature extracting capacity, significantly promoting the image quality compared to single model-based or deep learning-based methods. Besides, we designed a specialized deep learning model named PRIOR-Net, which can effectively excavate the static information in the prior image reconstructed from the fully-sampled projections at the encoding stage to improve the reconstruction performance for individual phase-resolved images. Both the simulated and clinical 4D CBCT datasets were performed to evaluate the performance of the PRIOR-Net and the PRIOR framework. Compared with the advanced 4D CBCT reconstruction methods, the proposed methods achieve promising results quantitatively and qualitatively in streak artifact suppression, soft tissue restoration, and tiny detail preservation. [ABSTRACT FROM AUTHOR]

Background: Although four‐dimensional cone‐beam computed tomography (4D‐CBCT) is valuable to provide onboard image guidance for radiotherapy of moving targets, it requires a long acquisition time to achieve sufficient image quality for target localization. To improve the utility, it is highly desirable to reduce the 4D‐CBCT scanning time while maintaining high‐quality images. Current motion‐compensated methods are limited by slow speed and compensation errors due to the severe intraphase undersampling. Purpose: In this work, we aim to propose an alternative feature‐compensated method to realize the fast 4D‐CBCT with high‐quality images. Methods: We proposed a feature‐compensated deformable convolutional network (FeaCo‐DCN) to perform interphase compensation in the latent feature space, which has not been explored by previous studies. In FeaCo‐DCN, encoding networks extract features from each phase, and then, features of other phases are deformed to those of the target phase via deformable convolutional networks. Finally, a decoding network combines and decodes features from all phases to yield high‐quality images of the target phase. The proposed FeaCo‐DCN was evaluated using lung cancer patient data. Results: (1) FeaCo‐DCN generated high‐quality images with accurate and clear structures for a fast 4D‐CBCT scan; (2) 4D‐CBCT images reconstructed by FeaCo‐DCN achieved 3D tumor localization accuracy within 2.5 mm; (3) image reconstruction is nearly real time; and (4) FeaCo‐DCN achieved superior performance by all metrics compared to the top‐ranked techniques in the AAPM SPARE Challenge. Conclusion: The proposed FeaCo‐DCN is effective and efficient in reconstructing 4D‐CBCT while reducing about 90% of the scanning time, which can be highly valuable for moving target localization in image‐guided radiotherapy. [ABSTRACT FROM AUTHOR]

Accurate and automatic segmentation of three-dimensional (3D) individual teeth from cone-beam computerized tomography (CBCT) images is a challenging problem because of the difficulty in separating an individual tooth from adjacent teeth and its surrounding alveolar bone. Thus, this paper proposes a fully automated method of identifying and segmenting 3D individual teeth from dental CBCT images. The proposed method addresses the aforementioned difficulty by developing a deep learning-based hierarchical multi-step model. First, it automatically generates upper and lower jaws panoramic images to overcome the computational complexity caused by high-dimensional data and the curse of dimensionality associated with limited training dataset. The obtained 2D panoramic images are then used to identify 2D individual teeth and capture loose- and tight- regions of interest (ROIs) of 3D individual teeth. Finally, accurate 3D individual tooth segmentation is achieved using both loose and tight ROIs. Experimental results showed that the proposed method achieved an F1-score of 93.35 percent for tooth identification and a Dice similarity coefficient of 94.79 percent for individual 3D tooth segmentation. The results demonstrate that the proposed method provides an effective clinical and practical framework for digital dentistry. [ABSTRACT FROM AUTHOR]

Existing protocols for image-guided adaptive brachytherapy (BT) pose distinct limitations, which include: (1) the possible misalignment of the application position as a consequence of patient translocation between the treatment room and the computed tomography (CT)/magnetic resonance imaging (MRI) suite, (2) an inherent inability to continually monitor physiological and anatomical changes during the course of 3–10 fraction treatment regimens, and (3) insufficient tracing of the radioactive source movement within the patient's body. In response to these challenges, this study proposes an innovative deep-learning-based C-arm CT/SPECT imaging system tailored specifically for image-guided adaptive BT. This technological advancement aims to enable the execution of high-precision online adaptive BT, thereby amplifying the adaptability and effectiveness of oncological care. An image dataset of 66 non-contrast pelvic CT studies was acquired and limited-angle cone-beam CT (CBCT) images were generated by mathematically calculating sinograms for voxel phantoms based on these CT images. Using a Generative Adversarial Network (GAN), low-quality CBCT images obtained using a 110° limited-angle rotating C-arm system were transformed into high-quality diagnostic CT images. The performances of these networks were evaluated by comparing them with CBCT images reconstructed using an iterative reconstruction method and ground-truth images. Synthetic CT images were successfully transformed from low-quality CBCT images, considerably reducing streaking artifacts and preserving the anatomical structures. Our deep-learning-based image reconstruction technique could successfully improve the image quality of limited-angle CBCT, similar to the ground-truth image quality, with a faster speed than the latest iterative reconstruction algorithm. We expect that this will be effectively implemented in online image-guided adaptive BT and patient dose verification. [ABSTRACT FROM AUTHOR]

Background: Iterative reconstruction for cone-beam computed tomography (CBCT) has been applied to improve image quality and reduce radiation dose. In a case where an object's actual projection is larger than a flat panel detector, CBCT images contain truncated data or incomplete projections, which degrade image quality inside the field of view (FOV). In this work, we propose truncation effect reduction for fast iterative reconstruction in CBCT imaging. Methods: The volume matrix size of the FOV and the height of projection images were extrapolated to a suitable size. These extended projections were reconstructed by fast iterative reconstruction. Moreover, a smoothing parameter for noise regularization in iterative reconstruction was modified to reduce the accumulated error while processing. The proposed work was evaluated by image quality measurements and compared with conventional filtered backprojection (FBP). To validate the proposed method, we used a head phantom for evaluation and preliminarily tested on a human dataset. Results: In the experimental results, the reconstructed images from the head phantom showed enhanced image quality. In addition, fast iterative reconstruction can be run continuously while maintaining a consistent mean-percentage-error value for many iterations. The contrast-to-noise ratio of the soft-tissue images was improved. Visualization of low contrast in the ventricle and soft-tissue images was much improved compared to those from FBP using the same dose index of 5 mGy. Conclusions: Our proposed method showed satisfactory performance to reduce the truncation effect, especially inside the FOV with better image quality for soft-tissue imaging. The convergence of fast iterative reconstruction tends to be stable for many iterations. [ABSTRACT FROM AUTHOR]

Purpose: Sparse‐view sampling has attracted attention for reducing the scan time and radiation dose of dental cone‐beam computed tomography (CBCT). Recently, various deep learning–based image reconstruction techniques for sparse‐view CT have been employed to produce high‐quality image while effectively reducing streak artifacts caused by the lack of projection views. However, most of these methods do not fully consider the effects of metal implants. As sparse‐view sampling strengthens the artifacts caused by metal objects, simultaneously reducing both metal and streak artifacts in sparse‐view CT images has been challenging. To solve this problem, in this study, we propose a novel framework. Methods: The proposed method was based on the normalized metal artifact reduction (NMAR) method, and its performance was enhanced using two convolutional neural networks (CNNs). The first network reduced the initial artifacts while preserving the fine details to generate high‐quality priors for NMAR processing. Subsequently, the second network was employed to reduce the streak artifacts after NMAR processing of sparse‐view CT data. To validate the proposed method, we generated training and test data by computer simulations using both extended cardiac‐torso (XCAT) and clinical data sets. Results: Visual inspection and quantitative evaluations demonstrated that the proposed method effectively reduced both metal and streak artifacts while preserving the details of anatomical structures compared with the conventional metal artifact reduction methods. Conclusions: We propose a framework for reconstructing accurate CT images in metal‐inserted sparse‐view CT. The proposed method reduces streak artifacts from both metal objects and sparse‐view sampling while recovering the anatomical details, indicating the feasibility of fast‐scan dental CBCT imaging. [ABSTRACT FROM AUTHOR]

Panoramic images have been widely used in the diagnosis of dental diseases. In the process of panoramic image reconstruction, the position of the dental arch curve usually affects the quality of display content, especially the completion level of the panoramic image. In addition, the metal implants in the patient's mouth often lead the contrast of the panoramic image to decrease. This paper describes a method to automatically synthesize panoramic images from dental cone beam computed tomography (CBCT) data. The proposed method has two essential features: the first feature is that the method can detect the dental arch curve through axial maximum intensity projection images over different ranges, and the second feature is that our method is able to adjust the intensity distribution of the implant in critical areas, to reduce the impact of the implant on the contrast of the panoramic image. The proposed method was tested on 50 CBCT datasets; the panoramic images generated by this method were compared with images attained from three other commonly used approaches and then subjectively scored by three experienced dentists. In the comprehensive image contrast score, the method in this paper has the highest score of 11.16 ± 2.64 points. The results show that the panoramic images generated by this method have better image contrast. [ABSTRACT FROM AUTHOR]

Cone beam computed tomography (CBCT) is used extensively in image-guided surgery and radiotherapy, but it induces ionizing radiation to the patients. Sparse-view CBCT is a main method to lower the radiation dose; however, it introduces streak artifacts in the reconstructed images. We develop a dual convolutional neural network architecture (DualCNN) to eliminate streak artifacts from sparse-view CBCT images. In the first part, we develop an interpolation CNN in the projection domain to restore the full-view projections from sparse-view projections. The restored full-view projections are then input to the Feldkamp–Davis–Kress algorithm for reconstructing the CBCT images. In the second part, we develop an image domain CNN to further improve the quality of the CBCT images. DualCNN is evaluated using real CBCT X-ray projection data of walnuts. Experimental results show that, DualCNN reconstructs good CT images with only a quarter number of full-view projections, and it achieves significantly higher performance than other representative methods in terms of qualitative and quantitative evaluations. DualCNN achieves a mean root-mean-square error of 0.0369, a mean peak-signal-to-noise ratio of 26.93 dB and a mean structural similarity of 0.732 in 3800 reconstructed images. Therefore, our DualCNN can significantly lower the CBCT radiation dose while maintaining good quality of reconstructed images. [ABSTRACT FROM AUTHOR]

Industrial computed tomography (CT) has seen widespread adoption within certain areas of non-destructive testing (NDT), with many commercial systems capable of acquisition and reconstruction of cone-beam CT data. The majority of these systems utilise reconstruction algorithms based on the traditional filtered back-projection (FBP) methods, which are imperfect with respect to limited-angle cone-beam data. These techniques are also inherently restricted in the source trajectories that can be utilised due to the use of Fourier slice theorem. This restricts FBP-based techniques to a circular or helical trajectory. Iterative reconstruction algorithms provide a solution to these limitations as the volume reconstruction does not depend on the location or orientation of the source and detector, allowing the possibility of scanning trajectories that satisfy well-known CT data-sufficiency conditions. This paper proposes a method of reconstruction based on computationally efficient computer graphics algorithms with data collected from points in 3D space not restricted to a single circular trajectory, which is useful within NDT for automated robotic inspection. The algorithms developed allow for rapid processing of the algebraic reconstruction technique (ART) for use with X-ray transmission data for CT reconstruction. Experimental results are presented for reconstructions for circular trajectory and points on a sphere to demonstrate the suitability for NDT applications. [ABSTRACT FROM AUTHOR]

Bone tissue engineering strategies aimed at treating critical-sized craniofacial defects often utilize novel biomaterials and scaffolding. Rapid manufacturing of defect-matching geometries using 3D-printing strategies is a promising strategy to treat craniofacial bone loss to improve aesthetic and regenerative outcomes. To validate manufacturing quality, a robust, three-dimensional quality assurance pipeline is needed to provide an objective, quantitative metric of print quality if porous scaffolds are to be translated from laboratory to clinical settings. Previously published methods of assessing scaffold print quality utilized one- and two-dimensional measurements (e.g., strut widths, pore widths, and pore area) or, in some cases, the print quality of a single phantom is assumed to be representative of the quality of all subsequent prints. More robust volume correlation between anatomic shapes has been accomplished; however, it requires manual user correction in challenging cases such as porous objects like bone scaffolds. Here, we designed porous, anatomically-shaped scaffolds with homogenous or heterogenous porous structures. We 3D-printed the designs with acrylonitrile butadiene styrene (ABS) and used cone-beam computed tomography (CBCT) to obtain 3D image reconstructions. We applied the iterative closest point algorithm to superimpose the computational scaffold designs with the CBCT images to obtain a 3D volumetric overlap. In order to avoid false convergences while using an autonomous workflow for volumetric correlation, we developed an independent iterative closest point (I-ICP10) algorithm using MATLAB®, which applied ten initial conditions for the spatial orientation of the CBCT images relative to the original design. Following successful correlation, scaffold quality can be quantified and visualized on a sub-voxel scale for any part of the volume. [ABSTRACT FROM AUTHOR]

Recently, deep learning with generative adversarial networks (GANs) has been applied in multi-domain image-to-image translation. This study aims to improve the image quality of cone-beam computed tomography (CBCT) by generating synthetic CT (sCT) that maintains the patient’s anatomy as in CBCT, while having the image quality of CT. As CBCT and CT are acquired at different time points, it is challenging to obtain paired images with aligned anatomy for supervised training. To address this limitation, the study incorporated a registration network (RegNet) into GAN during training. RegNet can dynamically estimate the correct labels, allowing supervised learning with noisy labels. The study developed and evaluated the approach using imaging data from 146 patients with head and neck cancer. The results showed that GAN trained with RegNet performed better than those trained without RegNet. Specifically, in the UNIT model trained with RegNet, the mean absolute error (MAE) was reduced from 40.46 to 37.21, the root mean-square error (RMSE) was reduced from 119.45 to 108.86, the peak signal-to-noise ratio (PSNR) was increased from 28.67 to 29.55, and the structural similarity index (SSIM) was increased from 0.8630 to 0.8791. The sCT generated from the model had fewer artifacts and retained the anatomical information as in CBCT.

Micro cone-beam computed tomography (µCBCT) imaging is of utmost importance for carrying out extensive preclinical research in rodents. The imaging of animals is an essential step prior to preclinical precision irradiation, but also in the longitudinal assessment of treatment outcomes. However, imaging artifacts such as beam hardening will occur due to the low energetic nature of the X-ray imaging beam (i.e., 60 kVp). Beam hardening artifacts are especially difficult to resolve in a 'pancake' imaging geometry with stationary source and detector, where the animal is rotated around its sagittal axis, and the X-ray imaging beam crosses a wide range of thicknesses. In this study, a seven-layer U-Net based network architecture (vMonoCT) is adopted to predict virtual monoenergetic X-ray projections from polyenergetic X-ray projections. A Monte Carlo simulation model is developed to compose a training dataset of 1890 projection pairs. Here, a series of digital anthropomorphic mouse phantoms was derived from the reference DigiMouse phantom as simulation geometry. vMonoCT was trained on 1512 projection pairs (= 80%) and tested on 378 projection pairs (= 20%). The percentage error calculated for the test dataset was 1.7 ± 0.4%. Additionally, the vMonoCT model was evaluated on a retrospective projection dataset of five mice and one frozen cadaver. It was found that beam hardening artifacts were minimized after image reconstruction of the vMonoCT-corrected projections, and that anatomically incorrect gradient errors were corrected in the cranium up to 15%. Our results disclose the potential of Artificial Intelligence to enhance the µCBCT image quality in biomedical applications. vMonoCT is expected to contribute to the reproducibility of quantitative preclinical applications such as precision irradiations in X-ray cabinets, and to the evaluation of longitudinal imaging data in extensive preclinical studies. [ABSTRACT FROM AUTHOR]

Background: There is no clear evidence that immediate implant placement can be applied to cases with dehiscence in the facial alveolar bone prior to extraction. Purpose: To evaluate the results of immediate implant placement in the anterior maxilla with facial alveolar bone dehiscence. Materials and Methods: We super positioned pre‐ and post‐operative cone‐beam computed tomography (CBCT) three‐dimensional reconstruction images. A CBCT was taken before tooth extraction (T0), when the definitive restoration was placed (T1), and 1 year after placing the definitive restoration (T2). The depth and width of the dehiscence at T0, and the height and width of the facial hard and soft tissues are measured at the implant site at T1 and T2. We calculated the change in the amount of hard and soft tissues from T1 to T2 and determined the correlation between preoperative facial alveolar bone morphology and postoperative gingival recession. Results: 13 women and 7 men were recruited. A total of 20 implants were evaluated. The implant survival rate was 100%. The mean facial alveolar bone dehiscence width was 3.9 ± 1.6 mm, and the mean depth from platform level was 2.9 ± 1.7 mm. The mean implant body exposure on the buccal was 4.8 ± 1.7 mm, and the mean socket width gap was 2.1 ± 0.8 mm. At T1, the mean facial hard tissue width was 2.1 ± 0.7 mm, and the mean height was 2.0 ± 0.7 mm. The mean change in vertical gingival recession from T1 to T2 was 0.5 ± 0.5 mm. We found a positive correlation between facial alveolar bone dehiscence width and gingival recession (r = 0.46, p‐value = 0.04) and between dehiscence depth and gingival recession (r = 0.48, p‐value = 0.03). Conclusions: The results of our CBCT superposition method indicated that immediate implant placement can be considered in patients with facial alveolar bone dehiscence. However, there may be a higher risk of gingival recession with wide or deep dehiscence. [ABSTRACT FROM AUTHOR]