Cone Beam Computed Tomography Artifacts Research Articles

Improvement of metal artifact reduction in dental CBCT using a CdTe photon-counting detector: Simulation study

In dental cone-beam computed tomography (CBCT), the image quality often degrades when patients have metallic objects such as metal implants and/or metal prostheses. Metal artifacts typically appear as streaks and shadows in the reconstructed CT images, limiting their clinical usefulness. Although various metal artifact reduction (MAR) methods are used in dental CBCT, no algorithm that can robustly remove metal artifacts has been universally accepted. Recent research has explored the potential benefits of using of a photon-counting detector (PCD) to advance enhanced MAR. PCD can classify x-ray photons into several different energy bins, allowing for more precise measurements of x-ray attenuation. Therefore, in this study, we propose a new MAR method based on the use of a PCD, named the high-energy trace normalized-MAR (HT-NMAR) algorithm, to ensure improved image quality. The proposed MAR algorithm consists of three main steps: 1) Segmentation of the metal trace using a high-energy binned sinogram, 2) Generation of a residual artifact-reduced prior, 3) Sinogram completion with the segmented metal trace and the prior, followed by CBCT reconstruction. We conducted a simulation on a numerical head phantom with metal inserts using a PCD simulation toolkit to demonstrate its efficacy. Our simulation study indicates that the proposed MAR method considerably reduces metal artifacts in CBCT and shows a better image performance than other existing MAR methods in reducing streak artifacts without any contrast anomaly.

Impact of cone-beam computed tomography artifacts on dose calculation accuracy for lung cancer.

To implement image-guided adaptive radiotherapy (IGART), many studies investigated dose calculations on cone-beam computed tomography (CBCT). A high HU accuracy is crucial for a high dose calculation accuracy and many imaging sites showed satisfactory results. It has been shown that the dose calculation accuracy for lung cancer lags behind. To examine why the dose calculation accuracy for lung is insufficient, the relative effects of the field-of-view (FOV), breathing motion, and scatter on dose calculation accuracy were studied. A framework was built to simulate CBCT scans for lung cancer patients by forward projecting repeat CT (rCT) scans for two scan geometries: small (SFOV) and medium FOV (MFOV). Breathing motion was modeled by applying a 4D deformation vector field to the mid-position rCT. Scatter was modeled by Monte-Carlo simulations with/without an anti-scatter grid (ASG). Simulated projections were reconstructed using filtered back-projection with/without scatter correction. In case of the SFOV, the CBCT images were patched with the planning CT scan in axial direction. The treatment plan was recalculated on the rCT and simulated CBCT. The mean Hounsfield unit (HU) difference (ΔHUmean ), the structural similarity index measure (SSIM), and γ metrics were calculated for the CBCT datasets of various imaging settings. The differences in HU, SSIM and dose calculation accuracy for CBCTs with and without breathing motion were negligible (mean ΔHUmean =6.4vs. 13.7, mean SSIM=0.941vs. 0.957, mean γ (ref=MFOV)=0.75). The SFOV resulted in a lower HU (mean ΔHUmean =-9.2vs. 13.7) and SSIM (mean SSIM=0.912vs. 0.957), and therefore in dose differences compared to the MFOV (mean γ=1.22). Scatter led to considerable discrepancies in all metrics. Adding only the ASG improved the results more than only applying a scatter correction algorithm. Combining ASG and scatter correction algorithm resulted in an even higher dose calculation accuracy. Scatter and FOV are the main contributors to dose inaccuracies and motion has only a minor effect on dose calculation accuracy. Therefore, utilizing an appropriate scatter correction and FOV is important to achieve sufficient dose calculation accuracy to facilitate IGART for lung.

PDS-MAR: a fine-grained projection-domain segmentation-based metal artifact reduction method for intraoperative CBCT images with guidewires

Objective. Since the invention of modern Computed Tomography (CT) systems, metal artifacts have been a persistent problem. Due to increased scattering, amplified noise, and limited-angle projection data collection, it is more difficult to suppress metal artifacts in cone-beam CT, limiting its use in human- and robot-assisted spine surgeries where metallic guidewires and screws are commonly used. Approach. To solve this problem, we present a fine-grained projection-domain segmentation-based metal artifact reduction (MAR) method termed PDS-MAR, in which metal traces are augmented and segmented in the projection domain before being inpainted using triangular interpolation. In addition, a metal reconstruction phase is proposed to restore metal areas in the image domain. Main results. The proposed method is tested on both digital phantom data and real scanned cone-beam computed tomography (CBCT) data. It achieves much-improved quantitative results in both metal segmentation and artifact reduction in our phantom study. The results on real scanned data also show the superiority of this method. Significance. The concept of projection-domain metal segmentation would advance MAR techniques in CBCT and has the potential to push forward the use of intraoperative CBCT in human-handed and robotic-assisted minimal invasive spine surgeries.

Artifact removal for unpaired thorax CBCT images using a feature fusion residual network and contextual loss.

Cone-beam computed tomography (CBCT) has become a more and more active cutting-edge topic in the international computed tomography (CT) research due to its advantages of fast scanning speed, high ray utilization rate and high precision. However, scatter artifacts affect the imaging performance of CBCT, which hinders its application seriously. Therefore, our study aimed to propose a novel algorithm for scatter artifacts suppression in thorax CBCT based on a feature fusion residual network (FFRN), where the contextual loss was introduced to adapt the unpaired datasets better. In the method we proposed, a FFRN with contextual loss was used to reduce CBCT artifacts in the region of chest. Unlike L1 or L2 loss, the contextual loss function makes input images which are not aligned strictly in space available, so we performed it on our unpaired datasets. The algorithm aims to reduce artifacts via studying the mapping between CBCT and CT images, where the CBCT images were set as the beginning while planning CT images as the end. The proposed method effectively removes artifacts in thorax CBCT, including shadow artifacts and cup artifacts which can be collectively referred to as uneven grayscale artifacts, in the CBCT image, and perform well in preserving details and maintaining the original shape. In addition, the average PSNR number of our proposed method achieved 27.7, which was higher than the methods this paper referred which indicated the significance of our method. What is revealed by the results is that our method provides a highly effective, rapid, and robust solution for the removal of scatter artifacts in thorax CBCT images. Moreover, as is shown in Table 1, our method has demonstrated better artifact reduction capability than other methods.

Imaging of the human cochlea using micro-computed tomography before and after cochlear implantation: comparison with cone-beam computed tomography.

Analysis of cochlear structures and postoperative temporal bone (TB) imaging are gaining importance in the evaluation of cochlear implantation (CI°). Our aims were to explore the microarchitecture of human cochlea using micro-computed tomography (μCT), analyze electrode's placement inside cochlea after CI°, and compare pre-/post-implantation μCT scans with cone-beam CT (CBCT) scans of same TBs. Cadaveric TBs were scanned using μCT and CBCT then underwent CI° using straight electrodes. Thereafter, they underwent again μCT and CBCT-imaging. Ten TBs were studied. μCT allowed visualization of scala tympani, scala vestibuli, basilar membrane, osseous spiral lamina, crista fenestrae, and spiral ligament. CBCT showed same structures except spiral ligament and crista fenestrae. After CI°, μCT and CBCT displayed the scalar location and course of electrode array within the cochlea. There were 7 cases of atraumatic electrode insertion and 3 cases of insertion trauma: basilar membrane elevation, electrode foldover with limited migration into scala vestibuli, and electrode kinking with limited migration into scala vestibuli. Insertion trauma was not correlated with cochlea's size or crista's maximal height but with round window membrane diameter. Resolution of μCT was higher than CBCT but electrode artifacts were similar. μCT was accurate in visualizing cochlear structures, and course and scalar position of electrode array inside cochlea with any possible trauma to cochlea or array. CBCT offers a good alternative to μCT in clinical practice for cochlear imaging and evaluation of CI°, with lower radiation and higher resolution than multi-slice CT. Difficulties related to non-traumatic CI° are multifactorial.

Evaluation of the Frequency of Artifacts in CBCT Depending on the Different Size of Field of View

Objectives: Various artifacts can be encountered when examining Cone-beam computed tomography(CBCT) images. In recent years, no descriptive study has been conducted on CBCT artifacts in the literature. The aim of this study is to identify artifacts in CBCT images with different imaging fields (FOV) taken on the same device, to determine their frequency, and also to reveal artifacts that are little known in the literature. Materials and Methods: In the study, CBCT images of the Faculty of Dentistry between the years 2012-2021 were scanned retrospectively and a total of 359 Cone Beam Computed Tomography images with 50x55, 100x55, 100x90, 130x55, 130x90, 230x170, 230x270 imaging fields (FOV) in the database, which met the exclusion and inclusion criteria, were analyzed by three oral and maxillofacial radiology research assistants and the types of artifacts seen on these images were determined. The incidence of the identified artifact types in the specified imaging areas was evaluated. Results: When looking at all images, the most common errors, regardless of FOV, were inevitable artifacts. Aliasing and motion artifacts were seen at higher rates on CBCT images with a larger field of view. In addition, the ring artifact was encountered in CBCT images with high imaging fields such as 130x90, 230x170 and 230x270. Conclusion: To know the incidence and causes of artifacts in images; it will prevent the patient, the environment and the practitioner from receiving x-rays (radiation) unnecessarily, mixing these errors with different pathological conditions and repetition of the image.

The effects of different metal posts, cements, and exposure parameters on cone-beam computed tomography artifacts.

This study assessed the intensity of artifacts produced by 2 metal posts, 2 cements, and different exposure parameters using 2 cone-beam computed tomography (CBCT) units. The sample was composed of 20 single-rooted premolars, divided into 4 groups: Ni-Cr/zinc phosphate, Ni-Cr/resin cement, Ag-Pd/zinc phosphate, and Ag-Pd/resin cement. Samples were scanned before and after post insertion and cementation using a CS9000 3D scanner with 4 exposure parameters (85/90 kV and 6.3/10 mA) and an i-CAT scanner with 120 kV and 5 mA. The presence of artifacts was assessed subjectively by 2 observers and objectively by a trained observer using ImageJ software. The Mann-Whitney, Wilcoxon, weighted kappa, and chi-square tests were used to assess data at a 95% confidence level (α<0.05). In the subjective analyses, AgPd presented more hypodense and hyperdense lines than NiCr (P<0.05), and more hypodense halos were found using i-CAT (P<0.05) than using CS9000 3D. More hypodense halos, hypodense lines, and hyperdense lines were observed at 10 mA than at 6.3 mA (P<0.05). More hypodense halos were observed at 85 kV than at 90 kV (P<0.05). CS9000 3D presented more hypodense and hyperdense lines than i-CAT (P<0.05). In the objective analyses, AgPd presented higher percentages of hyperdense and hypodense artifacts than NiCr (P<0.05). Zinc phosphate cement presented higher hyperdense artifact percentages on CS9000 3D scans (P<0.05). CS9000 3D presented higher artifact percentages than i-CAT (P<0.05). High-atomic-number alloys, higher tube current, and lower tube voltage may increase the artifacts present in CBCT images.

A fidelity-embedded learning for metal artifact reduction in dental CBCT.

Dental cone-beam computed tomography (CBCT) has been increasingly used for dental and maxillofacial imaging. However, the presence of metallic inserts, such as implants, crowns, and dental braces, violates the CT model assumption, which leads to severe metal artifacts in the reconstructed CBCT image, resulting in the degradation of diagnostic performance. In this study, we used deep learning to reduce metalartifacts. The metal artifacts, appearing as streaks and shadows, are nonlocal and highly associated with various factors, including the geometry of metallic inserts, energy-dependent attenuation, and energy spectrum of the incident X-ray beam, making it difficult to learn their complicated structures directly. To provide a step-by-step environment in which deep learning can be trained, we propose an iterative learning approach in which the network at each iteration step learns the correction error caused by the previous network, while enforcing the data fidelity in the projection domain. To generate a realistic paired training dataset, metal-free CBCT scans were collected from patients without metallic inserts, and then simulated metal projection data were added to generate the corresponding metal-corrupted projectiondata. The feasibility of the proposed method was investigated in clinical metal-affected CBCT scans, as well as simulated metal-affected CBCT scans. The results show that the proposed method significantly reduces metal artifacts while preserving the morphological structures near metallic objects and outperforms direct image domainlearning. The proposed fidelity-embedded learning can effectively reduce metal artifacts in dental CBCT compared with direct image domainlearning.

Evaluation of three resin cements in the production of cone beam computed tomography artifacts in teeth with fiberglass posts

The present study analyzed the presence of artifacts in endodontically treated teeth restored with fiberglass posts (FP) cemented with different resin cements by means of cone beam computed tomography (CBCT) in order to evaluate the contrast-to-noise ratio (CNR). A total of 60 mandibular premolars were selected to assemble the phantoms in order to simulate a clinical situation. The teeth were allocated to 6 groups, in which G1, G2 and G3 were endodontically treated teeth restored with FP cemented with Nexus 3, Duo-Link, and Allcem Core resin cements, respectively; G4: endodontically treated teeth restored with FP; G5: teeth treated only endodontically; and G6: healthy teeth. Mean gray scale values were analyzed in the axial images of the cervical, middle and apical thirds of the post length. CNR analysis was conducted on all groups except G5 and G6. There was a statistically significant difference in the mean values of the middle third region regarding the groups analyzed (p=0.026). However, artifacts were observed in all studied groups. The statistical difference observed between the cervical and apical thirds when the groups were evaluated together did not characterize the absence of artifacts between the resin cements, even when only the FP was present. Consequently, the choice of a resin cement might be based on ease of handling, better working time, cost-effectiveness, activation modes, substrate conditions, and clinical aspects. In summary, image quality was found to be compromised by artifacts in the presence of FP through CBCT with or without the resin cements in the root canal.

Quantitative Analysis of Cone-Beam Computed Tomography Artifacts Induced by Nonmetallic Root Canal Filling Materials Using Different Fields of View: In Vitro Study.

Cone-beam computed tomography (CBCT) imaging artifacts can hinder accurate diagnosis of several conditions. The aim of this study was to quantify CBCT artifacts created by nonmetallic root canal filling materials using two fields of view (FOV). Root canals of twenty extracted maxillary central incisors (n = 20) were instrumented and randomly divided into four equal groups: canals in Group 1 were filled with gutta-percha, canals in Group 2 with mineral trioxide aggregate (MTA) and gutta-percha, canals in Group 3 with gutta-percha and glass-fiber posts, and canals in Group 4 with MTA and glass-fiber posts. Each tooth was mounted on a silicon block and scanned three times using a CBCT machine, first with a prefilling scan (control) and then with postfilling scans, using two different FOV (40∗50 mm and 80∗50 mm). Imaging software was used to measure grayscale pixel values of canal cross sections. Data were analyzed using t-test and paired t-test statistical tests, with statistical significance set at p ≤ 0.05. Dentin at the apical and middle third of the teeth showed a significant decrease in minimum grayscale values in association with gutta-percha (p = 0.027, p = 0.034). However, a wide 80∗50 FOV showed a significant increase in maximum grayscale values of coronal (p = 0.048) and apical dentin (p = 0.049). Glass-fiber posts in middle third cross sections also corresponded to an increase in maximum grayscale values (p = 0.018). Gutta-percha produced dark band artifacts in the root's middle and apical thirds, whereas white streak artifacts were produced by gutta-percha in the coronal third and glass-fiber posts in the coronal and middle thirds when imaged with a wider FOV. A smaller FOV is therefore recommended for CBCT imaging, as it produces fewer artifacts.

Technical note: Air bubble-induced performance degradation in automatic rectum segmentation from cone-beam CT.

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. 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 regressionrelationship. 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 shapeextremes. 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 pelvicsegmentation.

Evaluation of Cone-beam Computed Tomography Artifacts Produced by Metal Objects Located Within and Outside the Field of View

IntroductionArtifacts created by the presence of metal objects in the jaw negatively affect the cone-beam computed tomographic image quality. This study compares artifacts produced by metal objects outside of the field of view (FOV) in a small FOV CBCT image with those produced in a large FOV image in which the metal object is within the FOV. MethodsWe methodically placed 4 titanium implant–sized rods and 4 zirconium crown–sized disks on 1 side of a human cadaver mandible. Using the Accuitomo 170 CBCT machine (J Morita, Irvine, CA), a total of 18 scans (9 with a small FOV and 9 with a large FOV) were made. Ten axial slices from each scan were transferred to ImageJ software (National Institutes of Health, Bethesda, MD) for analysis. The mean standard deviation of all voxel values of a fixed region of interest (ie, uniform air located lingual to tooth #30) was compared between small and large FOV slices. Two blinded observers subjectively rated the images for diagnostic quality and the presence of artifacts. ResultsThe Wilcoxon signed rank test showed that the standard deviation for both small and large FOV slices increases as the number of metal objects increases. The mean of the standard deviation for small and large FOVs is 3.6 and 2.5, respectively (P = .0000). Fifty-three percent of the small FOV slices had more artifacts in the subjective analysis. One hundred percent of the small FOV slices were rated as higher quality. ConclusionMetal objects outside of the FOV in the contralateral quadrant do affect the quality of small FOV images. However, small FOV images have a higher resolution compared with large FOV images.

The impact of software-based metal artifact reduction on the liquid embolic agent Onyx in cone-beam CT: a systematic in vitro and in vivo study

BackgroundOnyx is frequently used for endovascular embolization of intracranial arteriovenous malformations (AVMs) and dural arteriovenous fistulas (dAVFs). One drawback of using Onyx is the generation of artifacts in cone-beam CT...

INFLUENCE OF THE DISTANCE OF A METAL FROM THE FIELD OF VIEW PERIPHERY ON CONE-BEAM COMPUTED TOMOGRAPHY ARTIFACTS

<h3>BACKGROUND</h3> The presence of metal in the exomass (zone between the x-ray source and the image receptor) negatively affects image quality in cone beam computed tomography (CBCT), which cannot be effectively corrected by metal artifact reduction algorithms. However, considering that a metallic object can assume different positions in the exomass, the distance from the field of view (FOV) periphery could affect this condition. <h3>OBJECTIVE(S)</h3> The aim of this study was to evaluate the influence of the distance of metallic materials from the periphery of the FOV on the behavior of the CBCT artifacts. <h3>STUDY DESIGN</h3> An imaging phantom was custom made with 16 polypropylene tubes filled with a homogeneous radiopaque solution. CBCT scans were obtained by using a Picasso Trio unit adjusted to 90 kVp, 3 mA, and voxel size of 0.2 mm. The phantom was centered on a 5 × 5 cm FOV under protocols varying the composition (titanium or cobalt-chromium), quantity (0, 1, 2, or 3), and distance from the FOV periphery (2 cm, 3.5 cm, and 5.5 cm) of an object in the exomass. Means of voxel values were obtained from the 16 tubes of the phantom and the standard deviation was calculated as a way of measuring the variability values of each acquisition. Analysis of variance and Tukey's test compared separately the mean voxel and variability values between the different protocols (α = .05). <h3>RESULTS</h3> The mean voxel values were significantly lower (<i>P</i> < .05) for titanium at 5.5 cm and for 2 or 3 cobalt-chromium objects at 2 cm. In most conditions, the variability was significantly higher (<i>P</i> < .05) when the metallic object was at 2 cm. <h3>DISCUSSION/CONCLUSIONS</h3> The shortest distance of metallic materials from the FOV periphery had a greater impact on the CBCT artifacts.

Comparison of Supervised and Unsupervised Approaches for the Generation of Synthetic CT from Cone-Beam CT.

Due to major artifacts and uncalibrated Hounsfield units (HU), cone-beam computed tomography (CBCT) cannot be used readily for diagnostics and therapy planning purposes. This study addresses image-to-image translation by convolutional neural networks (CNNs) to convert CBCT to CT-like scans, comparing supervised to unsupervised training techniques, exploiting a pelvic CT/CBCT publicly available dataset. Interestingly, quantitative results were in favor of supervised against unsupervised approach showing improvements in the HU accuracy (62% vs. 50%), structural similarity index (2.5% vs. 1.1%) and peak signal-to-noise ratio (15% vs. 8%). Qualitative results conversely showcased higher anatomical artifacts in the synthetic CBCT generated by the supervised techniques. This was motivated by the higher sensitivity of the supervised training technique to the pixel-wise correspondence contained in the loss function. The unsupervised technique does not require correspondence and mitigates this drawback as it combines adversarial, cycle consistency, and identity loss functions. Overall, two main impacts qualify the paper: (a) the feasibility of CNN to generate accurate synthetic CT from CBCT images, which is fast and easy to use compared to traditional techniques applied in clinics; (b) the proposal of guidelines to drive the selection of the better training technique, which can be shifted to more general image-to-image translation.

Sinogram + image domain neural network approach for metal artifact reduction in low-dose cone-beam computed tomography

Purpose: Cone-beam computed tomography (CBCT) is commonly used in the operating room to evaluate the placement of surgical implants in relation to critical anatomical structures. A particularly problematic setting, however, is the imaging of metallic implants, where strong artifacts can obscure visualization of both the implant and surrounding anatomy. Such artifacts are compounded when combined with low-dose imaging techniques such as sparse-view acquisition. Approach: This work presents a dual convolutional neural network approach, one operating in the sinogram domain and one in the reconstructed image domain, that is specifically designed for the physics and setting of intraoperative CBCT to address the sources of beam hardening and sparse view sampling that contribute to metal artifacts. The networks were trained with images from cadaver scans with simulated metal hardware. Results: The trained networks were tested on images of cadavers with surgically implanted metal hardware, and performance was compared with a method operating in the image domain alone. While both methods removed most image artifacts, superior performance was observed for the dual-convolutional neural network (CNN) approach in which beam-hardening and view sampling effects were addressed in both the sinogram and image domain. Conclusion: The work demonstrates an innovative approach for eliminating metal and sparsity artifacts in CBCT using a dual-CNN framework which does not require a metal segmentation.

Deformable motion compensation for interventional cone-beam CT

Image-guided therapies in the abdomen and pelvis are often hindered by motion artifacts in cone-beam CT (CBCT) arising from complex, non-periodic, deformable organ motion during long scan times (5–30 s). We propose a deformable image-based motion compensation method to address these challenges and improve CBCT guidance. Motion compensation is achieved by selecting a set of small regions of interest in the uncompensated image to minimize a cost function consisting of an autofocus objective and spatiotemporal regularization penalties. Motion trajectories are estimated using an iterative optimization algorithm (CMA-ES) and used to interpolate a 4D spatiotemporal motion vector field. The motion-compensated image is reconstructed using a modified filtered backprojection approach. Being image-based, the method does not require additional input besides the raw CBCT projection data and system geometry that are used for image reconstruction. Experimental studies investigated: (1) various autofocus objective functions, analyzed using a digital phantom with a range of sinusoidal motion magnitude (4, 8, 12, 16, 20 mm); (2) spatiotemporal regularization, studied using a CT dataset from The Cancer Imaging Archive with deformable sinusoidal motion of variable magnitude (10, 15, 20, 25 mm); and (3) performance in complex anatomy, evaluated in cadavers undergoing simple and complex motion imaged on a CBCT-capable mobile C-arm system (Cios Spin 3D, Siemens Healthineers, Forchheim, Germany). Gradient entropy was found to be the best autofocus objective for soft-tissue CBCT, increasing structural similarity (SSIM) by 42%–92% over the range of motion magnitudes investigated. The optimal temporal regularization strength was found to vary widely (0.5–5 mm−2) over the range of motion magnitudes investigated, whereas optimal spatial regularization strength was relatively constant (0.1). In cadaver studies, deformable motion compensation was shown to improve local SSIM by ∼17% for simple motion and ∼21% for complex motion and provided strong visual improvement of motion artifacts (reduction of blurring and streaks and improved visibility of soft-tissue edges). The studies demonstrate the robustness of deformable motion compensation to a range of motion magnitudes, frequencies, and other factors (e.g. truncation and scatter).

Influence of voxel size on cone beam computed tomography artifacts arising from the exomass

The objective of this study was to evaluate the influence of voxel size on artifacts arising from the exomass in cone beam computed tomography (CBCT). An imaging phantom was scanned using 2 CBCT units, each adjusted to 2 voxel sizes: 0.2 and 0.3 mm. From 1 to 3 metal inserts of titanium, cobalt-chromium, or amalgam were placed in the exomass and additional CBCT scans were acquired. Mean voxel gray values were obtained from 16 homogeneous areas of the phantom and averaged, and the standard deviation was calculated to obtain voxel gray value variability. The data were analyzed using 2-way analysis of variance, Tukey, and Dunnett tests (α =.05). Overall, mean voxel gray values and voxel gray value variability did not differ significantly between CBCT scans obtained with voxel sizes of 0.2 and 0.3 mm for either CBCT model tested (P > .05). Despite some exceptions in which significant differences were observed between the 2 voxel sizes (P < .05), the mean voxel gray values and voxel gray value variability resulting from different metal compositions and, in most situations, for different numbers of metal inserts in the exomass were not affected. Voxel size has little influence on exomass-related CBCT artifacts.

Cone-beam computed tomography artifacts in the presence of dental implants and associated factors: an integrative review.

PurposeThis study was conducted to review the literature regarding the types of cone-beam computed tomography (CBCT) artifacts around dental implants and the factors that influence their formation.Materials and MethodsA search strategy was carried out in the PubMed, Embase, and Scopus databases to identify published between 2010 and 2020, and 9 studies were selected. The implants included 306 titanium, titanium-zirconium, and zirconia implants, as well as 5 titanium cylinders.ResultsThe artifacts around the implants were the beam-hardening artifact, the streaking artifact, and band-like radiolucent areas. Some factors that influenced the formation of artifacts were the implant material, bone type, evaluated regions, distance, type of CBCT, field of view (FOV) size, milliamperage, peak kilovoltage (kVp), and voxel size. The beam-hardening artifact was the most widely reported, and it was minimized in protocols with a smaller FOV, larger voxels, and higher kVp.ConclusionThe risk and benefit of these protocols in individuals with dental implants must be considered, and clinical examinations and complementary radiographs play an essential role in implantology.

Contextual loss based artifact removal method on CBCT image.

PurposeCone beam computed tomography (CBCT) offers advantages such as high ray utilization rate, the same spatial resolution within and between slices, and high precision. It is one of the most actively studied topics in international computed tomography (CT) research. However, its application is hindered owing to scatter artifacts. This paper proposes a novel scatter artifact removal algorithm that is based on a convolutional neural network (CNN), where contextual loss is employed as the loss function.MethodsIn the proposed method, contextual loss is added to a simple CNN network to correct the CBCT artifacts in the pelvic region. The algorithm aims to learn the mapping from CBCT images to planning CT images. The 627 CBCT‐CT pairs of 11 patients were used to train the network, and the proposed algorithm was evaluated in terms of the mean absolute error (MAE), average peak signal‐to‐noise ratio (PSNR) and so on. The proposed method was compared with other methods to illustrate its effectiveness.ResultsThe proposed method can remove artifacts (including streaking, shadowing, and cupping) in the CBCT image. Furthermore, key details such as the internal contours and texture information of the pelvic region are well preserved. Analysis of the average CT number, average MAE, and average PSNR indicated that the proposed method improved the image quality. The test results obtained with the chest data also indicated that the proposed method could be applied to other anatomies.ConclusionsAlthough the CBCT‐CT image pairs are not completely matched at the pixel level, the method proposed in this paper can effectively correct the artifacts in the CBCT slices and improve the image quality. The average CT number of the regions of interest (including bones, skin) also exhibited a significant improvement. Furthermore, the proposed method can be applied to enhance the performance on such applications as dose estimation and segmentation.