Artifacts In CT Images Research Articles

Statistical image reconstruction with beam-hardening compensation for X-ray CT by a calibration step (2DIterBH).

The beam-hardening effect due to the polychromatic nature of the X-ray spectra results in two main artifacts in CT images: cupping in homogeneous areas and dark bands between dense parts in heterogeneous samples. Post-processing methods have been proposed in the literature to compensate for these artifacts, but these methods may introduce additional noise in low-dose acquisitions. Iterative methods are an alternative to compensate noise and beam-hardening artifacts simultaneously. However, they usually rely on the knowledge of the spectrum or the selection of empirical parameters. We propose an iterative reconstruction method with beam hardening compensation for small animal scanners that is robust against low-dose acquisitions and that does not require knowledge of the spectrum, overcoming the limitations of current beam-hardening correction algorithms. The proposed method includes an empirical characterization of the beam-hardening function based on a simple phantom in a polychromatic statistical reconstruction method. Evaluation was carried out on simulated data with different noise levels and step angles and on limited-view rodent data acquired with the ARGUS/CT system. Results in small animal studies showed a proper correction of the beam-hardening artifacts in the whole sample, independently of the quantity of bone present on each slice. The proposed approach also reduced noise in the low-dose acquisitions and reduced streaks in the limited-view acquisitions. Using an empirical model for the beam-hardening effect, obtained through calibration, in an iterative reconstruction method enables a robust correction of beam-hardening artifacts in low-dose small animal studies independently of the bone distribution.

Quad-Net: Quad-Domain Network for CT Metal Artifact Reduction.

Metal implants and other high-density objects in patients introduce severe streaking artifacts in CT images, compromising image quality and diagnostic performance. Although various methods were developed for CT metal artifact reduction over the past decades, including the latest dual-domain deep networks, remaining metal artifacts are still clinically challenging in many cases. Here we extend the state-of-the-art dual-domain deep network approach into a quad-domain counterpart so that all the features in the sinogram, image, and their corresponding Fourier domains are synergized to eliminate metal artifacts optimally without compromising structural subtleties. Our proposed quad-domain network for MAR, referred to as Quad-Net, takes little additional computational cost since the Fourier transform is highly efficient, and works across the four receptive fields to learn both global and local features as well as their relations. Specifically, we first design a Sinogram-Fourier Restoration Network (SFR-Net) in the sinogram domain and its Fourier space to faithfully inpaint metal-corrupted traces. Then, we couple SFR-Net with an Image-Fourier Refinement Network (IFR-Net) which takes both an image and its Fourier spectrum to improve a CT image reconstructed from the SFR-Net output using cross-domain contextual information. Quad-Net is trained on clinical datasets to minimize a composite loss function. Quad-Net does not require precise metal masks, which is of great importance in clinical practice. Our experimental results demonstrate the superiority of Quad-Net over the state-of-the-art MAR methods quantitatively, visually, and statistically. The Quad-Net code is publicly available at https://github.com/longzilicart/Quad-Net.

Feature shared multi-decoder network using complementary learning for Photon counting CT ring artifact suppression.

Photon-counting computed tomography (Photon counting CT) utilizes photon-counting detectors to precisely count incident photons and measure their energy. These detectors, compared to traditional energy integration detectors, provide better image contrast and material differentiation. However, Photon counting CT tends to show more noticeable ring artifacts due to limited photon counts and detector response variations, unlike conventional spiral CT. To comprehensively address this issue, we propose a novel feature shared multi-decoder network (FSMDN) that utilizes complementary learning to suppress ring artifacts in Photon counting CT images. Specifically, we employ a feature-sharing encoder to extract context and ring artifact features, facilitating effective feature sharing. These shared features are also independently processed by separate decoders dedicated to the context and ring artifact channels, working in parallel. Through complementary learning, this approach achieves superior performance in terms of artifact suppression while preserving tissue details. We conducted numerous experiments on Photon counting CT images with three-intensity ring artifacts. Both qualitative and quantitative results demonstrate that our network model performs exceptionally well in correcting ring artifacts at different levels while exhibiting superior stability and robustness compared to the comparison methods. In this paper, we have introduced a novel deep learning network designed to mitigate ring artifacts in Photon counting CT images. The results illustrate the viability and efficacy of our proposed network model as a new deep learning-based method for suppressing ring artifacts.

Assessment of material identification and quantification in the presence of metals using spectral photon counting CT.

Spectral Photon Counting Computed Tomography (SPCCT), a ground-breaking development in CT technology, has immense potential to address the persistent problem of metal artefacts in CT images. This study aims to evaluate the potential of Mars photon-counting CT technology in reducing metal artefacts. It focuses on identifying and quantifying clinically significant materials in the presence of metal objects. A multi-material phantom was used, containing inserts of varying concentrations of hydroxyapatite (a mineral present in teeth, bones, and calcified plaque), iodine (used as a contrast agent), CT water (to mimic soft tissue), and adipose (as a fat substitute). Three sets of scans were acquired: with aluminium, with stainless steel, and without a metal insert as a reference dataset. Data acquisition was performed using a Mars SPCCT scanner (Microlab 5×120); operated at 118 kVp and 80 μA. The images were subsequently reconstructed into five energy bins: 7-40, 40-50, 50-60, 60-79, and 79-118 keV. Evaluation metrics including signal-to-noise ratio (SNR), linearity of attenuation profiles, root mean square error (RMSE), and area under the curve (AUC) were employed to assess the energy and material-density images with and without metal inserts. Results show decreased metal artefacts and a better signal-to-noise ratio (up to 25%) with increased energy bins as compared to reference data. The attenuation profile also demonstrated high linearity (R2 >0.95) and lower RMSE across all material concentrations, even in the presence of aluminium and steel. Material identification accuracy for iodine and hydroxyapatite (with and without metal inserts) remained consistent, minimally impacting AUC values. For demonstration purposes, the biological sample was also scanned with the stainless steel volar implant and cortical bone screw, and the images were objectively assessed to indicate the potential effectiveness of SPCCT in replicating real-world clinical scenarios.

Mitigating metal artifacts in cone-beam CT images through deep learning techniques

Objective: To develop and evaluate metal artifact removal systems (MARSs) based on deep learning to assess their effectiveness in removing artifacts caused by different thicknesses of metals in cone-beam CT (CBCT) images. Methods: A full-mouth standard model (60 mm×75 mm×110 mm) was three-dimensional (3D) printed using photosensitive resin. The model included a removable and replaceable target tooth position where cobalt-chromium alloy crowns with varying thicknesses were inserted to generate matched CBCT images. The artifacts resulting from cobalt-chromium alloys with different thicknesses were evaluated using the structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR). CNN-MARS and U-net-MARS were developed using a convolutional neural network and U-net architecture, respectively. The effectiveness of both MARSs were assessed through visualization and by measuring SSIM and PSNR values. The SSIM and PSNR values were statistically analyzed using one-way analysis of variance (α=0.05). Results: Significant differences were observed in the range of artifacts produced by different thicknesses of cobalt-chromium alloys (all P<0.05), with 1 mm resulting in the least artifacts. The SSIM values for specimens with thicknesses of 1.0 mm, 1.5 mm, and 2.0 mm were 0.916±0.019, 0.873±0.010, and 0.833±0.010, respectively (F=447.89, P<0.001). The corresponding PSNR values were 20.834±1.176, 17.002±0.427, and 14.673±0.429, respectively (F=796.51, P<0.001). After applying CNN-MARS and U-net-MARS to artifact removal, the SSIM and PSNR values significantly increased for images with the same thickness of metal (both P<0.05). When using the CNN-MARS for artifact removal, the SSIM values for 1.0, 1.5 and 2.0 mm were 0.938±0.023, 0.930±0.029, and 0.928±0.020 (F=2.22, P=0.112), while the PSNR values were 30.938±1.495, 30.578±2.154 and 30.553±2.355 (F=0.54, P=0.585). When using the U-net-MARS for artifact removal, the SSIM values for 1.0, 1.5 and 2.0 mm were 0.930±0.024, 0.932±0.017 and 0.930±0.012 (F=0.24, P=0.788), and the PSNR values were 30.291±0.934, 30.351±1.002 and 30.271±1.143 (F=0.07, P=0.929). No significant differences were found in SSIM and PSNR values after artifact removal using CNN-MARS and U-net-MARS for different thicknesses of cobalt-chromium alloys (all P>0.05). Visualization demonstrated a high degree of similarity between the images before and after artifact removal using both MARSs. However, CNN-MARS displayed clearer metal edges and preserved more tissue details when compared with U-net-MARS. Conclusions: Both the CNN-MARS and U-net-MARS models developed in this study effectively remove the metal artifacts and enhance the image quality. CNN-MARS exhibited an advantage in restoring tissue structure information around the artifacts compared to U-net-MARS.

Novel Pulmonary Nodule Position Detection Method Based on Multiscale Convolution

In order to improve the accuracy of the current pulmonary nodule location detection method based on CT images, reduce the problem of missed detection or false detection, and effectively assist imaging doctors in the diagnosis of pulmonary nodules. Propose a novel method for detecting the location of pulmonary nodules based on multiscale convolution. First, image preprocessing methods are used to eliminate the noise and artifacts in lung CT images. Second, multiple adjacent single-frame CT images are selected to be concatenate into multi-frame images, and the feature extraction is carried out through the artificial neural network model U-Net improved by multi-scale convolution to enhanced feature extraction capability for pulmonary nodules of different sizes and shapes, so as to improve the accuracy of feature extraction of pulmonary nodules. Finally, using point detection to improve the loss function of U-Net training process, the accuracy of pulmonary nodule location detection is improved. The accuracy of detecting pulmonary nodules equal or larger than 3 mm and smaller than 3 mm are 98.02% and 96.94% respectively. This method can effectively improve the detection accuracy of pulmonary nodules on CT image sequence, and can better meet the diagnostic needs of pulmonary nodules.

Sinogram interpolation combined with unsupervised image-to-image translation network for CT metal artifact correction

To propose a framework that combines sinogram interpolation with unsupervised image-to-image translation (UNIT) network to correct metal artifacts in CT images. The initially corrected CT image and the prior image without artifacts, which were considered as different elements in two different domains, were input into the image transformation network to obtain the corrected image. Verification experiments were carried out to assess the effectiveness of the proposed method using the simulation data, and PSNR and SSIM were calculated for quantitative evaluation of the performance of the method. The experiment using the simulation data showed that the proposed method achieved better results for improving image quality as compared with other methods, and the corrected images preserved more details and structures. Compared with ADN algorithm, the proposed algorithm improved the PSNR and SSIM by 2.4449 and 0.0023 when the metal was small, by 5.9942 and 8.8388 for images with large metals, and by 8.8388 and 0.0130 when both small and large metals were present, respectively. The proposed method for metal artifact correction can effectively remove metal artifacts, improve image quality, and preserve more details and structures on CT images.

Low pitch significantly reduces helical artifacts in abdominal CT

PurposeHigh helical pitch scanning minimizes scan times in CT imaging, and thus also minimizes motion artifact and mis-synchronization with contrast bolus. However, high pitch produces helical artifacts that may adversely affect diagnostic image quality. This study aims to determine the severity and incidence of helical artifacts in abdominal CT imaging and their relation to the helical pitch scan parameter. MethodsTo obtain a dataset with varying pitch values, we used CT exam data both internal and external to our center. A cohort of 59 consecutive adult patients receiving an abdomen CT examination at our center with an accompanying prior examination from an external center was selected for retrospective review. Two expert observers performed a blinded rating of helical artifact in each examination using a five-point Likert scale. The incidence of artifacts with respect to the helical pitch was assessed. A generalized linear mixed-effects regression (GLMER) model, with study arm (Internal or External to our center) and helical pitch as the fixed-effect predictor variables, was fit to the artifact ratings, and significance of the predictor variables was tested. ResultsFor a pitch of <0.75, the proportion of exams with mild or worse helical artifacts (Likert scores of 1–3) was <1%. The proportion increased to 16% for exams with pitch between 0.75 and 1.2, and further increased to 78% for exams with a pitch greater than 1.2. Pitch was significantly associated with helical artifact in the GLMER model (p = 2.8 × 10−9), while study arm was not a significant factor (p = 0.76). ConclusionThe incidence and severity of helical artifact increased with helical pitch. This difference persisted even after accounting for the potential confounding factor of the center where the study was performed.

Sparsier2Sparse: Self-supervised convolutional neural network-based streak artifacts reduction in sparse-view CT images.

Sparse-view computed tomography (CT) has attracted a lot of attention for reducing both scanning time and radiation dose. However, sparsely-sampled projection data generate severe streak artifacts in the reconstructed images. In recent decades, many sparse-view CT reconstruction techniques based on fully-supervised learning have been proposed and have shown promising results. However, it is not feasible to acquire pairs of full-view and sparse-view CT images in real clinical practice. In this study, we propose a novel self-supervised convolutional neural network (CNN) method to reduce streak artifacts in sparse-view CTimages. We generate the training dataset using only sparse-view CT data and train CNN based on self-supervised learning. Since the streak artifacts can be estimated using prior images under the same CT geometry system, we acquire prior images by iteratively applying the trained network to given sparse-view CT images. We then subtract the estimated steak artifacts from given sparse-view CT images to produce the finalresults. We validated the imaging performance of the proposed method using extended cardiac-torso (XCAT) and the 2016 AAPM Low-Dose CT Grand Challenge dataset from Mayo Clinic. From the results of visual inspection and modulation transfer function (MTF), the proposed method preserved the anatomical structures effectively and showed higher image resolution compared to the various streak artifacts reduction methods for all projection views. We propose a new framework for streak artifacts reduction when only the sparse-view CT data are given. Although we do not use any information of full-view CT data for CNN training, the proposed method achieved the highest performance in preserving fine details. By overcoming the limitation of dataset requirements on fully-supervised-based methods, we expect that our framework can be utilized in the medical imagingfield.

Metal artifact reduction and clinical verification in oral and maxillofacial region based on deep learning

Objective: To construct a kind of neural network for eliminating the metal artifacts in CT images by training the generative adversarial networks (GAN) model, so as to provide reference for clinical practice. Methods: The CT data of patients treated in the Department of Radiology, West China Hospital of Stomatology, Sichuan University from January 2017 to June 2022 were collected. A total of 1 000 cases of artifact-free CT data and 620 cases of metal artifact CT data were obtained, including 5 types of metal restorative materials, namely, fillings, crowns, titanium plates and screws, orthodontic brackets and metal foreign bodies. Four hundred metal artifact CT data and 1 000 artifact-free CT data were utilized for simulation synthesis, and 1 000 pairs of simulated artifacts and metal images and simulated metal images (200 pairs of each type) were constructed. Under the condition that the data of the five metal artifacts were equal, the entire data set was randomly (computer random) divided into a training set (800 pairs) and a test set (200 pairs). The former was used to train the GAN model, and the latter was used to evaluate the performance of the GAN model. The test set was evaluated quantitatively and the quantitative indexes were root-mean-square error (RMSE) and structural similarity index measure (SSIM). The trained GAN model was employed to eliminate the metal artifacts from the CT data of the remaining 220 clinical cases of metal artifact CT data, and the elimination results were evaluated by two senior attending doctors using the modified LiKert scale. Results: The RMSE values for artifact elimination of fillings, crowns, titanium plates and screws, orthodontic brackets and metal foreign bodies in test set were 0.018±0.004, 0.023±0.007, 0.015±0.003, 0.019±0.004, 0.024±0.008, respectively (F=1.29, P=0.274). The SSIM values were 0.963±0.023, 0.961±0.023, 0.965±0.013, 0.958±0.022, 0.957±0.026, respectively (F=2.22, P=0.069). The intra-group correlation coefficient of 2 evaluators was 0.972. For 220 clinical cases, the overall score of the modified LiKert scale was (3.73±1.13), indicating a satisfactory performance. The scores of modified LiKert scale for fillings, crowns, titanium plates and screws, orthodontic brackets and metal foreign bodies were (3.68±1.13), (3.67±1.16), (3.97±1.03), (3.83±1.14), (3.33±1.12), respectively (F=1.44, P=0.145). Conclusions: The metal artifact reduction GAN model constructed in this study can effectively remove the interference of metal artifacts and improve the image quality.

Ring Artifacts Removal &amp; Noise Reduction in X-Ray Computed Tomography Using Deep Learning

Analyzing data obtained from X-ray CT acquisitions may be challenging due to the presence of image artifacts. In recent years, deep learning has proven to be one of the fastest growing technologies in computer science and has become a confirmed trend in X-ray CT. In this study, we propose using deep learning-based algorithm to reduce noise and ring artifacts in CT images.

STEDNet: Swin transformer-based encoder-decoder network for noise reduction in low-dose CT.

Low-dose computed tomography (LDCT) can reduce the dose of X-ray radiation, making it increasingly significant for routine clinical diagnosis and treatment planning. However, the noise introduced by low-dose X-ray exposure degrades the quality of CT images, affecting the accuracy of clinical diagnosis. Purpose The noises, artifacts, and high-frequency components are similarly distributed in LDCT images. Transformer can capture global context information in an attentional manner to create distant dependencies on targets and extract more powerful features. In this paper, we reduce the impact of image errors on the ability to retain detailed information and improve the noise suppression performance by fully mining the distribution characteristics of imageinformation. This paper proposed an LDCT noise and artifact suppressing network based on Swin Transformer. The network includes a noise extraction sub-network and a noise removal sub-network. The noise extraction and removal capability are improved using a coarse extraction network of high-frequency features based on full convolution. The noise removal sub-network improves the network's ability to extract relevant image features by using a Swin Transformer with a shift window as an encoder-decoder and skip connections for global feature fusion. Also, the perceptual field is extended by extracting multi-scale features of the images to recover the spatial resolution of the feature maps. The network uses a loss constraint with a combination of L1 and MS-SSIM to improve and ensure the stability and denoising effect of thenetwork. The denoising ability and clinical applicability of the methods were tested using clinical datasets. Compared with DnCNN, RED-CNN, CBDNet and TSCN, the STEDNet method shows a better denoising effect on RMSE and PSNR. The STEDNet method effectively removes image noise and preserves the image structure to the maximum extent, making the reconstructed image closest to the NDCT image. The subjective and objective analysis of several sets of experiments shows that the method in this paper can effectively maintain the structure, edges, and textures of the denoised images while having good noise suppression performance. In the real data evaluation, the RMSE of this method is reduced by 18.82%, 15.15%, 2.25%, and 1.10% on average compared with DnCNN, RED-CNN, CBDNet, and TSCNN, respectively. The average improvement of PSNR is 9.53%, 7.33%, 2.65%, and 3.69%,respectively. This paper proposed a LDCT image denoising algorithm based on end-to-end training. The method in this paper can effectively improve the diagnostic performance of CT images by constraining the details of the images and restoring the LDCT image structure. The problem of increased noise and artifacts in CT images can be solved while maintaining the integrity of CT image tissue structure and pathological information. Compared with other algorithms, this method has better denoising effects both quantitatively andqualitatively.

Metallic Artifact Reduction in Midfacial CT Scans Using Patient-Specific Polymer Implants Enhances Image Quality.

Midfacial reconstruction after tumor resection surgery is commonly conducted by using autologous bone grafts or alloplastic implants. Titanium is the most frequently used osteosynthesis material in these cases but causes disturbing metallic artifacts in CT imaging. The purpose of this experimental study was to evaluate whether the use of midfacial polymer implants reduces metallic artifacts in CT imaging to improve image quality. Zygomatic titanium (n = 1) and polymer (n = 12) implants were successively implanted in a human skull specimen. Implants were analyzed for their effect on Hounsfield Unit values (streak artifacts) and virtual growth in CT images (blooming artifacts) as well as image quality. Multi-factorial ANOVA and Bonferroni's post hoc test were used. Titanium (173.7 HU; SD ± 5.1) and hydroxyapatite containing polymers (155.3 HU; SD ± 5.9) were associated with significantly more streak artifacts compared to all other polymer materials. There was no significant difference in blooming artifacts between materials. The metallic artifact reduction algorithm showed no significant difference. Image quality was slightly better for polymer implants compared to titanium. Personalized polymer implants for midfacial reconstruction significantly reduce metallic artifacts in CT imaging which improves image quality. Hence, postoperative radiation therapy planning and radiological tumor aftercare around the implants are facilitated.

Dual-Energy Computed Tomography Applications to Reduce Metal Artifacts in Hip Prostheses: A Phantom Study

Metal components of hip prostheses cause severe artifacts in CT images, influencing diagnostic accuracy. Metal artifact reduction (MAR) software and virtual monoenergetic reconstructions on dual-energy CT (DECT) systems are possible solutions that should be considered. In this study, we created a customized adjustable phantom to quantify the severity of artifacts on periprosthetic tissues (cortical and spongious bone, soft tissues) for hip prostheses. The severity of artifacts was classified by different thresholds of deviation from the CT numbers for reference objects not affected by artifacts. The in vitro setup was applied on four unilateral and three bilateral configurations of hip prostheses (made of titanium, cobalt, and stainless steel alloys) with a DECT system, changing the energy of virtual monoenergetic reconstructions, with and without MAR. The impact of these tools on the severity of artifacts was scored, looking for the best scan conditions for the different configurations. For titanium prostheses, the reconstruction at 110 keV, without MAR, always minimized the artifacts. For cobalt and stainless-steel prostheses, MAR should always be applied, while monoenergetic reconstruction alone did not show clear advantages. The available tools for reducing metal artifacts must therefore be applied depending on the examined prosthetic configuration.

Convolutional neural network-based metal and streak artifacts reduction in dental CT images with sparse-view sampling scheme.

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 novelframework. 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. 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. 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 CBCTimaging.

Radiation dose perturbation at the tissue interface with PEEK and Titanium bone implants: Monte Carlo simulation, treatment planning and film dosimetry

PurposeTitanium bone implants perturb the dose distribution in radiation therapy, creating additional considerations for planning and treatment. The relatively high scattering density of titanium gives rise to imaging artifacts in CT, making contouring difficult and necessitating post processing. While significant work has been undertaken to reduce the risk of implant failure through the introduction of porosity and the functionalization of the surface to improve osseointegration, the dose distribution within the voids of porous implant structures have not been investigated. Materials and methodsHere we demonstrate using Monte Carlo methods, a clinical treatment planning system (TPS) and film measurements that cavities within a titanium implant are associated with local spikes of dose (ranging from a 10–20% increase) which could impact the osseointegration of the implant. We propose an alternative material, poly-ether-ether-ketone (PEEK), which is emerging as a bone replacement material with attenuation properties closer to that of water. ResultsWe show that the reduced perturbation of both kV and MV beams by PEEK creates fewer artifacts in CT images and minimal enhancement of dose to tissue in voids. The significant dose enhancement at the entry interface and reduction of dose at the exit interface characteristic for titanium, are greatly reduced for PEEK. DiscussionThe dose perturbation associated with titanium on the exterior surface and within the voids, is of sufficient magnitude to warrant the surrounding tissue be considered an organ at risk (OAR).The reduced perturbation of the radiation beam caused by PEEK provides a far more hospitable environment for osseointegration and thereby, is likely to improve the success of implants for patients prescribed a course of radiation therapy.

Low-dimensional Manifold Constrained Disentanglement Network for Metal Artifact Reduction.

Deep neural network based methods have achieved promising results for CT metal artifact reduction (MAR), most of which use many synthesized paired images for supervised learning. As synthesized metal artifacts in CT images may not accurately reflect the clinical counterparts, an artifact disentanglement network (ADN) was proposed with unpaired clinical images directly, producing promising results on clinical datasets. However, as the discriminator can only judge if large regions semantically look artifact-free or artifact-affected, it is difficult for ADN to recover small structural details of artifact-affected CT images based on adversarial losses only without sufficient constraints. To overcome the illposedness of this problem, here we propose a low-dimensional manifold (LDM) constrained disentanglement network (DN), leveraging the image characteristics that the patch manifold of CT images is generally low-dimensional. Specifically, we design an LDM-DN learning algorithm to empower the disentanglement network through optimizing the synergistic loss functions used in ADN while constraining the recovered images to be on a low-dimensional patch manifold. Moreover, learning from both paired and unpaired data, an efficient hybrid optimization scheme is proposed to further improve the MAR performance on clinical datasets. Extensive experiments demonstrate that the proposed LDM-DN approach can consistently improve the MAR performance in paired and/or unpaired learning settings, outperforming competing methods on synthesized and clinical datasets.

The butterfly effect: improving brain cone-beam CT image artifacts for stroke assessment using a novel dual-axis trajectory

BackgroundCone-beam computed tomography (CBCT) imaging of the brain can be performed in the angiography suite to support various neurovascular procedures. Relying on CBCT brain imaging solely, however, still lacks full...

Reliability and benefits of single-energy projection-based metallic artifact reduction (SEMAR) in the different orthopedic hardware for the hip.

To evaluate the performance and reliability of the single-energy metal artifact reduction (SEMAR) algorithm in patients with different orthopedic hardware at the hips. A total of 153 patients with hip instrumentation who had undergone CT with adaptive iterative dose reduction (AIDR) 3D and SEMAR algorithms between February 2015 and October 2019 were included retrospectively. Patients were divided into 5 groups by the hardware type. Two readers with 21 and 13years of experience blindly reviewed all image sets and graded the extent of artifacts and imaging quality using 5-point scales. To evaluate reliability, the mean densities and image noises were measured at the urinary bladder, veins, and fat in images with artifacts and the reference images. No significant differences were found in the mean densities of the urinary bladder, veins, and fat between the SEMAR images with artifacts (7.57 ± 9.49, 40.29 ± 23.07, 86.78 ± 38.34) and the reference images (7.77 ± 6.2, 40.27 ± 8.66, 89.10 ± 20.70) (P = .860, .994, .392). Image noises of the urinary bladder in the SEMAR images with artifacts (14.25 ± 4.50) and the SEMAR reference images (9.69 ± 1.29) were significantly higher than those in the AIDR 3D reference images (9.11 ± 1.12) (P < .001; P < .001). All AIDR 3D images were non-diagnostic (overall quality ≤ 3) and less than a quarter of the SEMAR images were non-diagnostic (16.7-23.7%), mainly in patients with prostheses [reader 1: 91.7% (22/24); reader 2: 92.6% (25/27)]. The SEMAR algorithm significantly reduces metal artifacts in CT images, more in patients with internal fixations than in patients with prostheses, and provides reliable attenuation of soft tissues.

The Impact of Different Types of Orthodontic Appliances and Its Location in Producing CT Scan Artefacts

Fixed orthodontic appliances can produce metal artefacts in CT images which may degrade the diagnostic image quality. The study aimed to evaluate the artefacts based on the types and location of the metallic and non-metallic orthodontic brackets. This is an in-vitro cross-sectional study. Four different types of orthodontic brackets (stainless steel, titanium, monocrystalline, and polycrystalline ceramic bracket) were bonded consecutively in four different locations of the cadaveric skull. All scans were performed by a single operator using the same CT machine followed by a standard scanning protocol. Artefact intensity for all data sets was quantified by calculating the standard deviation (SD) of the grey values within the dataset by following a standard method. The One-way ANOVA Bonferroni test was used for the data analysis. The mean artefact score of the stainless steel bracket was significantly (p &lt; 0.001) high in comparison with other types of the orthodontic brackets. Besides, the mean artefact score was significantly (p=0.002) low when orthodontic brackets were placed unilaterally. Stainless steel brackets produced a significant amount of noise in CT images which can degrade the diagnostic image quality. Thus, the polycrystalline ceramic bracket can be a better alternative of stainless steel brackets for patient need frequent CT scan.