Computed Tomography Artifacts Research Articles

Meta-analysis study on anesthetic sedation recovery and onset times in pediatric and elderly patients undergoing CT and MRI.

Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are crucial diagnostic modalities that require patients to remain immobile for extended periods, with anesthesia sometimes used for comfort and image quality enhancement. The study compares dexmedetomidine and propofol in reducing recovery time and sedation onset in pediatric and elderly patients undergoing CT and MRI procedures. A meta-analysis of fifteen studies assessing recovery time, sedation onset, and failed sedation between dexmedetomidine and propofol in pediatric and elderly patients during CT and MRI was conducted. The study indicated that the administration of anaesthesia markedly improved patient compliance and reduced motion artefacts in both CT and MRI (P<0.00001, I2=94%). The meta-analysis indicated that the mean difference (MD) in the onset of sedation was significantly faster in the control group (P<0.00001, I2=96%). The study reveals that dexmedetomidine and propofol anesthesia can improve patient image quality during CT and MRI procedures by reducing motion artefacts. Dexmedetomidine sedated people more quickly than propofol, but no significant differences in sedation duration were observed.

Prediction of anemia with thoracic computed tomography findings.

This study explored the potential of non-contrast thoracic computed tomography (CT) to predict anemia by correlating CT parameters with hemoglobin (Hb) levels in patients who underwent non-contrast thoracic CT for various indications. This retrospective study included 150 patients who underwent non-contrast thoracic CT scans and complete blood counts within 24 hours at our center between January and June 2023. Exclusion criteria included acute bleeding, iron accumulation disorders, recent transfusions, pregnancy, and certain thoracic CT artifacts. Hounsfield Unit (HU) measurements were obtained from the ascending aorta, left ventricular cavity, and descending aorta, and compared with Hb and hematocrit (Htc) values. Anemia indicators such as the 'Aortic Ring Sign (ARS)' and the 'Hyperdense Septum Sign (HSS)' were also evaluated. Anemic patients (48%) exhibited significantly lower HU measurements at all three CT scan locations and higher instances of ARS and HSS compared to non-anemic patients. Notably, the presence of HSS and ARS was strongly associated with anemia. Thresholds for HU measurements corresponding to anemia were determined using receiver operating characteristic curve analysis, which also revealed strong positive correlations between HU measurements and Hb/Htc levels. The study concludes that non-contrast thoracic CT parameters, particularly HU measurements and the presence of ARS and HSS, are significantly associated with anemia. These CT indicators could serve as reliable, non-invasive markers for predicting anemia in patients, potentially aiding in the early diagnosis and management of the condition.

Streak artefact removal in x-ray dark-field computed tomography using a convolutional neural network.

Computed tomography (CT) relies on the attenuation of x-rays, and is, hence, of limited use for weakly attenuating organs of the body, such as the lung. X-ray dark-field (DF) imaging is a recently developed technology that utilizes x-ray optical gratings to enable small-angle scattering as an alternative contrast mechanism. The DF signal provides structural information about the micromorphology of an object, complementary to the conventional attenuation signal. A first human-scale x-ray DF CT has been developed by our group. Despite specialized processing algorithms, reconstructed images remain affected by streaking artifacts, which often hinder image interpretation. In recent years, convolutional neural networks have gained popularity in the field of CT reconstruction, amongst others for streak artefactremoval. Reducing streak artifacts is essential for the optimization of image quality in DF CT, and artefact free images are a prerequisite for potential future clinical application. The purpose of this paper is to demonstrate the feasibility of CNN post-processing for artefact reduction in x-ray DF CT and how multi-rotation scans can serve as a pathway for trainingdata. We employed a supervised deep-learning approach using a three-dimensional dual-frame UNet in order to remove streak artifacts. Required training data were obtained from the experimental x-ray DF CT prototype at our institute. Two different operating modes were used to generate input and corresponding ground truth data sets. Clinically relevant scans at dose-compatible radiation levels were used as input data, and extended scans with substantially fewer artifacts were used as ground truth data. The latter is neither dose-, nor time-compatible and, therefore, unfeasible for clinical imaging ofpatients. The trained CNN was able to greatly reduce streak artifacts in DF CT images. The network was tested against images with entirely different, previously unseen image characteristics. In all cases, CNN processing substantially increased the image quality, which was quantitatively confirmed by increased image quality metrics. Fine details are preserved during processing, despite the output images appearing smoother than the ground truthimages. Our results showcase the potential of a neural network to reduce streak artifacts in x-ray DF CT. The image quality is successfully enhanced in dose-compatible x-ray DF CT, which plays an essential role for the adoption of x-ray DF CT into modern clinicalradiology.

512 Evaluation of Computed Tomography Artefacts of Carbon-Fiber and Titanium Implants in Patients with Spinal Oligometastatic Disease Undergoing Stereotactic Ablative Radiotherapy

512 Evaluation of Computed Tomography Artefacts of Carbon-Fiber and Titanium Implants in Patients with Spinal Oligometastatic Disease Undergoing Stereotactic Ablative Radiotherapy

Reducing Metal Artifacts in X-ray Computed Tomography Based on Metal Effect Estimation and Multiple CT Volume Fusion

In recent years, X-ray Computed Tomography (CT) has been widely used for investigating industrial parts. Many industrial parts often include metals, which cause various CT artifacts such as beam hardening artifacts, and metal artifacts. These CT artifacts lower the quality of X-ray CT images, prevent precise measurements. In order to reduce metal artifacts, here we propose using multiple X-ray CT scanning. Our multiple X-ray CT scanning method includes local artifacts estimation, proposing adequate multiple scanning angles, multiple scanned data registration and multiple CT data fusion. Based on local artifacts estimation, optimal multiple scan angles for data fusion are selected. Data registration and fusion are also based on local artifacts estimation, excluding effects of artifacts during the process. In order to demonstrate our method, we measured industrial parts which includes metals and resins, proposed optimal multiple scanning angles and conducted volume fusion. As a result, the metal artifacts in the CT volume reduced significantly compared to single X-ray CT scanning. Experiments also showed that metal artifacts in different regions are reduced by additional scans, which suggest that our scan angle selection method successfully selected optimal multiple scan angles.

Metal Artifact Correction in Industrial CT Images Based on a Dual-Domain Joint Deep Learning Framework

Industrial computed tomography (CT) images reconstructed directly from projection data using the filtered back projection (FBP) method exhibit strong metal artifacts due to factors such as beam hardening, scatter, statistical noise, and deficiencies in the reconstruction algorithms. Traditional correction approaches, confined to either the projection domain or the image domain, fail to fully utilize the rich information embedded in the data. To leverage information from both domains, we propose a joint deep learning framework that integrates UNet and ResNet architectures for the correction of metal artifacts in CT images. Initially, the UNet network is employed to correct the imperfect projection data (sinograms), the output of which serves as the input for the CT image reconstruction unit. Subsequently, the reconstructed CT images are fed into the ResNet, with both networks undergoing a joint training process to optimize image quality. We take the projection data obtained by analytical simulation as the data set. The resulting optimized industrial CT images show a significant reduction in metal artifacts, with the average Peak Signal-to-Noise Ratio (PSNR) reaching 36.13 and the average Structural Similarity Index (SSIM) achieving 0.953. By conducting simultaneous correction in both the projection and image domains, our method effectively harnesses the complementary information from both, exhibiting a marked improvement in correction results over the deep learning-based single-domain corrections. The generalization capability of our proposed method is further verified in ablation experiments and multi-material phantom CT artifact correction.

Synchrotron radiation sparse-view CT artifact correction through deep learning neural networks

ABSTRACT Synchrotron radiation X-ray Computed Tomography(CT) imaging is an effective means of non-destructively revealing the internal structure of samples. To obtain high-quality reconstructed slices, the number of projections should be at least π 2 N detector ( N detector is the number of detector pixels occupied by the sample diameter). Acquiring excessive projections leads to high radiation doses and long scanning time. Insufficient projections, falling below the Nyquist sampling criterion, result in significant streaking artifacts. This paper proposes a method that fuses the structural similarity index and the peak signal-to-noise ratio as a loss function of the Attention U-Net network to correct streaking artifacts. Moreover, a new deep learning framework based on the Transformer and convolutional neural network is also proposed to solve the artifact problem. At 200 and 400 views, the two proposed methods significantly improve the reconstructed slices’ quality, qualitatively and quantitatively outperforming the classical streaking artifact correction method. The optimal sparsity ratio for sparse-view CT imaging is investigated: the Trans-attunet method corrects streaking artifacts at a 1/12 sparsity ratio to visualize large sample features, whereas ratios of 1/3 or 1/2 more effectively recover fine structures. The optimal sparsity ratio is contingent upon the trade-off between desired image quality and imaging efficiency.

Evaluation of computed tomography artefacts of carbon-fiber and titanium implants in patients with spinal oligometastatic disease undergoing stereotactic ablative radiotherapy

This study evaluated artefacts on computed tomography (CT) images using Hounsfield units (HU) in patients with spinal oligometastatic disease who received carbon-fiber (CF; n = 11) or titanium (n = 11) spine implants and underwent stereotactic ablative radiotherapy (SABR). Pre- and postoperative HU were measured at the vertebral body, pedicle, and spinal cord at three different levels: the lower instrumented vertebra, the level of metastatic spinal cord compression, and an uninvolved level. Areas measured at each level were delicately matched pre- and postoperatively. Significant differences in HU were observed at the vertebral body, the pedicle, and the spinal cord at the lowest instrumented vertebra level for both CF and titanium (average increase 1.54-fold and 5.11-fold respectively). At the metastatic spinal cord compression level, a trend towards a higher HU-increase was observed in titanium compared with CF treated patients (average increase 2.51-fold and 1.43-fold respectively). The relatively high postoperative HU-increase after insertion of titanium implants indicated CT artefacts, while the relatively low HU-increase of CF implants was not associated with artefacts. Less CT artefacts could facilitate an easier contouring phase in radiotherapy planning. In addition, we propose a CT artefact grading system based on postoperative HU-increase. This system could serve as a valuable tool in future research to assess if less CT artefacts lead to time savings during radiotherapy treatment planning and, potentially, to better tumoricidal effects and less adverse effects if particle therapy would be administered.

Improving the quality of computed tomography brain images in the presence of cochlear implant induced metal artefacts through the additional use of tissue mimicking materials alongside metal artefact reduction software

IntroductionMetal artefact reduction software (MAR) can be used to improve Computed Tomography (CT) image quality in the presence of implanted metalwork; however, this software is not effective for superficial metallic structures such as cochlear implants (CI).This study aimed to investigate whether the effectiveness of MAR software could be improved for brain scans with CI present through the use of tissue mimicking materials (TMM) placed exteriorly to the implant. MethodsIn this two-part study, a CI was positioned on the surface of water and anthropomorphic phantoms and imaged using a helical CT brain protocol. Three TMM, Superflab, Sure Thermal heat packs, and Bart's Bolus, were utilised and images were acquired to assess the resulting artefact reduction in terms of CT numbers, noise and artefact index (Aind). Changes in CTDIvol were assessed for the anthropomorphic phantom scans. ResultsIn the water phantom, statistically significant reductions in CT number (p = 0.038) and noise (p = 0.033) were observed for Superflab, whilst the heat packs produced similar significant reductions in CT number (p < 0.001) and noise (p = 0.001) for the anthropomorphic phantom images. Aind values were significantly reduced through the use of Superflab (p = 0.009) and the heat packs (p < 0.001). No significant effects were observed for Bart's Bolus. CTDIvol increases of generally less than 5% were observed for scans with TMM in place. ConclusionThe additional use of TMM alongside MAR software yielded statistically significant reductions in CI induced metal artefacts on both water and anthropomorphic phantom scans with minimal dose increases. Implications for practiceThe extent of metal artefacts in clinical head scans with CI in place could be significantly reduced through combined use of TMM and MAR software, consequently providing greater diagnostic confidence in the images.

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.

Reducing image artifacts in sparse projection CT using conditional generative adversarial networks

Reducing the amount of projection data in computed tomography (CT), specifically sparse-view CT, can reduce exposure dose; however, image artifacts can occur. We quantitatively evaluated the effects of conditional generative adversarial networks (CGAN) on image quality restoration for sparse-view CT using simulated sparse projection images and compared them with autoencoder (AE) and U-Net models. The AE, U-Net, and CGAN models were trained using pairs of artifacts and original images; 90% of patient cases were used for training and the remaining for evaluation. Restoration of CT values was evaluated using mean error (ME) and mean absolute error (MAE). The image quality was evaluated using structural image similarity (SSIM) and peak signal-to-noise ratio (PSNR). Image quality improved in all sparse projection data; however, slight deformation in tumor and spine regions was observed, with a dispersed projection of over 5°. Some hallucination regions were observed in the CGAN results. Image resolution decreased, and blurring occurred in AE and U-Net; therefore, large deviations in ME and MAE were observed in lung and air regions, and the SSIM and PSNR results were degraded. The CGAN model achieved accurate CT value restoration and improved SSIM and PSNR compared to AE and U-Net models.

Impact of the spatial orientation of the patient's head, metal artifact reduction, and tube current on cone-beam computed tomography artifact expression adjacent to a dental implant: A laboratory study using a simulated surgical guide.

The aim of this study was to evaluate image artifacts in the vicinity of dental implants in cone-beam computed tomography (CBCT) scans obtained with different spatial orientations, tube current levels, and metal artifact reduction algorithm (MAR) conditions. One dental implant and 2 tubes filled with a radiopaque solution were placed in the posterior region of a mandible using a surgical guide to ensure parallel alignment. CBCT scans were acquired with the mandible in 2 spatial orientations in relation to the X-ray projection plane (standard and modified) at 3 tube current levels: 5, 8, and 11 mA. CBCT scans were repeated without the implant and were reconstructed with and without MAR. The mean voxel and noise values of each tube were obtained and compared using multi-way analysis of variance and the Tukey test (α=0.05). Mean voxel values were significantly higher and noise values were significantly lower in the modified orientation than in the standard orientation (P<0.05). MAR activation and tube current levels did not show significant differences in most cases of the modified spatial orientation and in the absence of the dental implant (P>0.05). Modifying the spatial orientation of the head increased brightness and reduced spatial orientation noise in adjacent regions of a dental implant, with no influence from the tube current level and MAR.

Computed Tomography Artifact Reduction Employing a Convolutional Neural Network Within the Context of Dimensional Metrology

Abstract Utilizing accurate, nondestructive testing methods to improve quality control and reduce manufacturing errors has gained prominence in light of industry development in various fields. Industrial computed tomography (CT) scanning carries considerable weight among all conventional methods because of their unique features, such as providing a three-dimensional specimen model. Due to the prevalence of metals with high linear attenuation coefficients in industrial applications, beam hardening and scatter artifacts are two of the most prevalent artifacts in any reconstructed volume. Other notable artifacts include those with a nonideal focal spot and conical beam radiation. These artifacts may manifest as a distortion of gray value peaks, systematic discrepancies, blurring-like cupping, and streaking in reconstructed images, degrading volume reconstruction quality. In this paper, the effect of these artifacts is illustrated and mitigated by adopting our proposed method, a combination of conventional and contemporary techniques, including the use of a pretrained convolutional neural network (CNN). Five tests are replicated in different geometric parameters to perform a geometric configuration analysis, indicating how effective the proposed approach is at encountering different geometric situations. The results demonstrate that the proposed method has substantially achieved its goal of improving the accuracy of dimensional metrology performed on our phantom.

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.

Deep-learning-based methods of attenuation correction for SPECT and PET

Attenuation correction (AC) is essential for quantitative analysis and clinical diagnosis of single-photon emission computed tomography (SPECT) and positron emission tomography (PET). In clinical practice, computed tomography (CT) is utilized to generate attenuation maps (μ-maps) for AC of hybrid SPECT/CT and PET/CT scanners. However, CT-based AC methods frequently produce artifacts due to CT artifacts and misregistration of SPECT-CT and PET-CT scans. Segmentation-based AC methods using magnetic resonance imaging (MRI) for PET/MRI scanners are inaccurate and complicated since MRI does not contain direct information of photon attenuation. Computational AC methods for SPECT and PET estimate attenuation coefficients directly from raw emission data, but suffer from low accuracy, cross-talk artifacts, high computational complexity, and high noise level. The recently evolving deep-learning-based methods have shown promising results in AC of SPECT and PET, which can be generally divided into two categories: indirect and direct strategies. Indirect AC strategies apply neural networks to transform emission, transmission, or MR images into synthetic μ-maps or CT images which are then incorporated into AC reconstruction. Direct AC strategies skip the intermediate steps of generating μ-maps or CT images and predict AC SPECT or PET images from non-attenuation-correction (NAC) SPECT or PET images directly. These deep-learning-based AC methods show comparable and even superior performance to non-deep-learning methods. In this article, we first discussed the principles and limitations of non-deep-learning AC methods, and then reviewed the status and prospects of deep-learning-based methods for AC of SPECT and PET.

Revealing the microstructures and seepage characteristics in the uncured rubber-cord composites using micro-computed tomography and lattice Boltzmann approach

The internal microstructure distribution of cord-rubber-air during the processing of uncured rubber-cord composites (URCs) determines the finished product's performance. For the first time, we used computed tomography (CT) and the lattice Boltzmann method (LBM) to establish a geometrical representation model of the real microscopic pore-fracture structures of URCs and investigate the seepage law of fluid in porous URCs, where the reinforced rubber formula was originally designed to reduce CT artifacts of cord. The results showed that the average porosity and pore radius of the original cord (0.2711 and 15.53 μm, respectively) were considerably larger than those of the URCs (0.0509 and 4.46 μm, respectively); the pore number of the cord was the largest when the pore radius was 5–10 μm, accounting for 29.36% of the total number; however, the pore number accounted for 31.36% of the total number of the URCs when the pore radius was 2–3 μm. Moreover, the characteristics of the pore/throat surface area and pore volume/throat length exhibited perfect consistent patterns with those of the pore radius. Furthermore, the fluid flow velocity increased in both cord and URCs as the displacement differential pressure increased, but decreased dramatically as the fluid kinematic viscosity increased. The critical values of displacement differential pressure and kinematic viscosity were different in the cord and URCs samples, presenting 11.1209 Pa/1.3696 × 10−3 m2/s and 14.2984 Pa/2.8869 × 10−4 m2/s, respectively. This phenomenon should be attributed that when the uncured rubber was injected into the original cord sample, its porosity decreased, its pore radius decreased, the number of micro-scale pores increased, and flow resistance became larger, resulting in a higher critical value of displacement differential pressure and a lower critical value of kinematic viscosity.

Neural network guided sinogram-domain iterative algorithm for artifact reduction.

Artifact reduction or removal is a challenging task when the artifact creation physics are not well modeled mathematically. One of such situations is metal artifacts in x-ray CT when the metallic material is unknown, and the x-ray spectrum is wide. A neural network is used to act as the objective function for iterative artifact reduction when the artifact model is unknown. A hypothetical unpredictable projection data distortion model is used to illustrate the proposed approach. The model is unpredictable, because it is controlled by a random variable. A convolutional neural network is trained to recognize the artifacts. The trained network is then used to compute the objective function for an iterative algorithm, which tries to reduce the artifacts in a computed tomography (CT) task. The objective function is evaluated in the image domain. The iterative algorithm for artifact reduction is in the projection domain. A gradient descent algorithm is used for the objective function optimization. The associated gradient is calculated with the chain rule. The learning curves illustrate the decreasing treads of the objective function as the number of iterations increases. The images after the iterative treatment show the reduction of artifacts. A quantitative metric, the Sum Square Difference (SSD), also indicates the effectiveness of the proposed method. The methodology of using a neural network as an objective function has potential value for situations where a human developed model is difficult to describe the underlying physics. Real-world applications are expected to be benefit from this methodology.

A nonlinear scaling-based normalized metal artifact reduction to reduce low-frequency artifacts in energy-integrating and photon-counting CT.

Metal within the scan plane can cause severe artifacts when reconstructing X-ray computed tomography (CT) scans. Both in clinical use and recent research, normalized metal artifact reduction (NMAR) has established as the reference method for correcting metal artifacts, but NMAR introduces inconsistencies within the sinogram, which can cause additional low-frequency artifacts after image reconstruction. This paper introduces an extension to NMAR by applying a nonlinear scaling function (NLS-NMAR) to reduce low-frequency artifacts, which get introduced by the reconstruction of interpolation-edge-related sinogram inconsistencies in the normalized sinogram domain. After linear interpolation of the metal trace, an NLS function is applied in the prior-normalized sinogram domain to reduce the impact of the interpolation edges during filtered backprojection. After sinogram denormalization and image reconstruction, the low frequencies of the NLS image are combined with different high frequencies to restore anatomic details. An anthropomorphic dental phantom with removable metal inserts was utilized on two different CT systems to quantitatively assess the artifact reduction performance in terms of HU deviations and the root-mean-square-error within relevant regions of interest. Clinical dental examples were assessed to qualitatively demonstrate the problem of the interpolation-related blooming as well as to demonstrate the performance of the NLS function to reduce respective artifacts. To quantitatively prove HU consistency, HU values were assessed in central ROIs in the clinical cases. In addition, single clinical cases of a hip replacement and pedicle screws in the spine are shown to demonstrate the method's results in other body regions. The NLS-NMAR can minimize the effect of interpolation-related sinogram inconsistencies and thus reduce resulting hyperdense blooming artifacts. In the phantom results, the reconstructions with the NLS-NMAR-corrected low frequencies demonstrate the lowest error. In the qualitative assessment of the clinical data, the NLS-NMAR shows a tremendous enhancement in image quality, also performing best within all assessed images series. The NLS-NMAR provides a small yet effective extension to conventional NMAR by reducing low-frequency hyperdense metal trace-interpolation-related artifacts in computed tomography.

Iterative Metal Artifact Reduction in Head and Neck CT Facilitates Tumor Visualization of Oral and Oropharyngeal Cancer Obscured by Artifacts From Dental Hardware

The purpose of this study was to evaluate the diagnostic utility of iterative metal artifact reduction (iMAR) in computed tomography (CT)-imaging of oral and oropharyngeal cancers when obscured by dental hardware artifacts and to determine the most appropriate iMAR settings for this purpose. The study retrospectively enrolled 27 patients (8 female, 19 male; mean age 64±12.7years) with histologically confirmed oral or oropharyngeal cancer obscured by dental artifacts in contrast-enhanced CT. Raw CT data were reconstructed with ascending iMAR strengths (levels 1/2/3/4/5) and one reconstruction without iMAR (level 0). For subjective analysis, two blinded radiologists rated tumor visualization and artifact severity on a five-point Likert scale. For objective analysis, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and artifact index (AI) were determined. iMAR reconstructions improved the subjective image quality of tumor edge and contrast, and the objective parameters of tumor SNR and CNR, reaching their optimum at iMAR levels 4 and 5 (P<.001). AI decreased with iMAR reconstructions reaching its minimum at iMAR level 5 (P<.001). Tumor detection rates increased 2.4-fold with iMAR 5, 2.1-fold with iMAR 4, and 1.9-fold with iMAR 3 compared to reconstructions without iMAR. Disadvantages such as algorithm-induced artifacts increased significantly with higher iMAR strengths (P<.05), reaching a maximum with iMAR 5. iMAR significantly improves CT imaging of oral and oropharyngeal cancers, as confirmed by both subjective and objective measures, with best results at highest iMAR strengths.

Feature shape inspection of metal parts by matching X-ray projection images with CAD model projections

X-ray computed tomography (CT) provides a three-dimensional (3D) volume image of the scanned object and can be applied for nondestructive inspection of industrial materials. The object surface extracted from the X-ray CT volume is examined by comparing it with the pre-obtained computer-aided design (CAD) mesh model of the scanned object. However, industrial materials often contain heavy metals that significantly attenuate X-rays and cause CT artifacts in CT images, preventing an accurate shape extraction. This study proposes a method that extracts the geometric features of two-dimensional (2D) X-ray projection images and matches them with pre-obtained 3D CAD meshes. The proposed method is based on the principle that most of the geometric features of the object, such as edges, in the X-ray 2D projection images are captured and can be directly compared with CAD meshes. We first extract the features in each of the 2D projection images as X-ray feature projections. Subsequently, CAD mesh contours are projected onto 2D mesh feature projections and matched with the X-ray feature projections. Finally, 3D feature shapes of the CAD mesh are obtained by back-projecting the matching results. The proposed method requires only a small number of projection images, thus reducing computational time while maintaining shape accuracy.