Metal Artifact Reduction Algorithm Research Articles

Multidetector computed tomography with single energy metal artifact reduction algorithm in the complications following hip and knee replacement. Preliminary experience

Multidetector computed tomography with single energy metal artifact reduction algorithm in the complications following hip and knee replacement. Preliminary experience

Dosimetric assessment of a single-energy metal artifact reduction algorithm for computed tomography images in radiation therapy.

This study aimed to evaluate the performance of a single-energy metal artifact reduction (SEMAR) algorithm for radiation therapy treatment using phantom cases with metal inserts, assess improvements in computed tomography (CT) number accuracy, and investigate its effects on treatment planning dosimetry. A standard electron density phantom was scanned with and without metal inserts. The numbers of tissue-equivalent materials on both uncorrected and SEMAR-corrected CT images were compared. Treatment planning accuracy was evaluated by comparing dose distributions computed using true density images (without metal inserts), uncorrected images (with metal inserts), and SEMAR-corrected images (with metal inserts) using three-dimensional gamma analysis. The numbers of the true density and uncorrected and SEMAR-corrected CT images in a muscle plug with unilateral inserts were 25.9HU, - 281.8HU, and 26.1HU, respectively. A similar tendency was obtained for other tissue-equivalent materials, and the numbers on CT images were improved with the SEMAR algorithm. In cases involving 1 portal irradiation, 10-MV X-ray, and the Acuros XB algorithm, the pass ratio between the true density and uncorrected images was 89.89%, while that between the true density and SEMAR-corrected images was 95.03%. Improvements in dose distribution were evident using the SEMAR algorithm. Similar trends were found for different irradiation methods and dose calculation algorithms. The SEMAR algorithm can significantly reduce metal artifacts on CT images used for radiation treatment planning. This aspect influenced dosimetry in the region of the artifact and dose distribution was significantly improved with use of the SEMAR-corrected images.

Feasibility of Smart Metal Artifact Reduction Algorithm on Computed Tomography Angiography for Clipping of Recurrent Aneurysms After Coil Embolization

The number of patients with a history of clipping of recurrent aneurysms after coil embolization has increased. The aim of this article was to report the feasibility of CT angiography using a commercial metal artifact reduction algorithm (Smart Metal Artifact Reduction [MAR]) for patients who underwent clipping of recurrent aneurysms after coil embolization. Six cases of clipping of recurrent aneurysms after coil embolization were examined with CT angiography using MAR between 2015 and 2018 at a single institution. Conventional CT angiography and three-dimensional digital subtraction angiography data were compared, and depiction of the status of treated aneurysms using MAR was estimated. Conventional CT angiography was unable to depict the status of treated aneurysms in the patients with a history of clipping of recurrent aneurysms after coil embolization because of metal artifacts. With MAR, metal artifacts were greatly reduced, and the status of treated aneurysms was able to be depicted, although depiction was inferior to three-dimensional digital subtraction angiography. For patients with a history of clipping of recurrent aneurysms after coil embolization, CT angiography using MAR is feasible, although further development of imaging techniques is needed.

Model Image-Based Metal Artifact Reduction for Computed Tomography.

Metal implants often produce severe artifacts in the reconstructed computed tomography (CT) images, causing information and image detail loss and making the CT images diagnostically unusable. In order to eliminate the metal artifacts and enhance the diagnostic value of the reconstructed CT images, a post-processing metal artifact reduction algorithm, based on a tissue-class model segmented by thresholding and k-means clustering with spatial information, is proposed. The image inpainting technique is incorporated into the algorithm to improve the segmentation accuracy for CT images severely corrupted by metal artifacts. A study of a water phantom and of two sets of clinical CT images was performed to test the algorithm performance. The proposed method effectively eliminates typical metal artifacts, restores the average CT numbers of different tissues to the proper levels, and preserves the edge and contrast information, thus allowing the accurate reconstruction of the tissue attenuation map. The quality of the artifact-corrected CT images allows them to be subsequently used in other clinical applications, such as three-dimensional rendering, dose estimation for radiotherapy, attenuation correction for PET and SPECT, etc. The algorithm does not rely on the use of the raw sinogram and so is not limited by the proprietary format restrictions.

Gradient image smoothing for metal artifact reduction (GISMAR) in computed tomography

Metal artifacts can impair accurate diagnosis, and degrade the image quality and diagnostic value of CT-slices. In this work we propose a novel gradient image smoothing for metal artifact reduction (GISMAR) algorithm for image quality improvement in patients with hip implants, dental fillings, DBS implants and permanent seed implants. Using Image Smoothing via L0 Gradient Minimization method, a global thresholding method, and the principle of NMAR method, the authors developed a new MAR method that does not depend on access to raw projection data. To validate the authors’ approach, 2D-CT data from twenty-two patients with different metal implants were used and processed by GISMAR and three more well- known algorithms. In order to evaluate metal artifact reduction, mean CT number (HU and SD) was calculated as well as a subjective analysis with three expert observers. Image quality on images was compared using the non-parametric Friedman-ANOVA test. We conclude that GISMAR method can efficiently reduce metal artifacts using CT-slice, does not introduce new artifacts, while preserving anatomical structures.

A Curvelet based Sinogram Correction Method for Metal Artifact Reduction

In the X-ray computed tomography (CT) imaging, metal artifact occurs often due to the non linear attenuation effect of the metallic subject with respect to the photon energy. Especially high attenuated material, such as plumbum (Pb.) can easily distort the sinogram due to the few remaining of the information on the metal traces. The remedy of such a circumstance, an image inpainting algorithm is useful to fill the missing information in the sinogram. Most metal artifact reduction (MAR) algorithm based on inpainting consist of the filling the missing values using only informations around the matal traces. Without the consideration of the continuity of the sinogram, the inpainting method can generate addition artifact as a side effect. In this paper, we propose a curvelet based inpainting method to promote the continuity of the sinogram, induced by the rotation of the X-ray scan system. Numerical simulation and phantom experiment are provided to support the our assertion.

Effect of PET Image Reconstruction Techniques on Unexpected Aorta Uptake

Objectives:To determine if unexpected aorta uptake seen in some patients is influenced by popular modern reconstruction algorithms using semi-quantitative and qualitative analysis.Methods:Twenty-five consecutive patients without suspected vascular disease were selected for 18F-FDG positron emission tomography/ computed tomography (PET/CT) scanning and images of the aorta were created using iterative reconstruction (IT), IT + time of flight (TOF), IT + TOF + point spread function correction (referred collectively as UHD) with and without metal artefact reduction (MAR) algorithms. An experienced radiologist created aorta and blood pool (BP) regions of interests then copied these to all reconstructions for accurate positioning before recording target aorta standardized-uptake-values (SUVmax) and background BP SUVmean. Furthermore, target-to-background ratio (TBRmax) was defined by aorta SUVmax-to-BP SUVmean ratio for more analysis.Results:For aorta SUVmax with IT, IT + TOF, UHD, UHD + MAR reconstructions the mean ± standard deviation recorded were 2.15±0.43, 2.25±0.51, 2.25±0.45 and 2.09±0.4, respectively. Values for BP SUVmean were 1.61±0.31, 1.58±0.28, 1.58±0.28 and 1.47±0.25, respectively. Likewise, for TBRmax these were 1.35±0.19, 1.43±0.21, 1.43±0.19, 1.43±0.18, respectively. ANOVA analysis revealed no significant differences for aorta SUVmax (F(0.86) p=0.46), BP SUVmean (F(1.22) p=0.31) or TBRmax (F(0.99) p=0.4). However, the qualitative visual analysis revealed significant differences between IT + TOF with UHD (p=0.02) or UHD + MAR (p=0.02).Conclusion:Reconstruction algorithm effect on aorta SUVmax or BP SUVmean or TBRmax was not statistically significant. However, qualitative visual analysis showed significant differences between IT + TOF as compared with UHD or UHD + MAR reconstructions. Harmonization of techniques with a larger patient cohort is recommended in future clinical trials.

Are metal artefact reduction algorithms effective to correct cone beam CT artefacts arising from the exomass?

The aim of this study was to evaluate the effectiveness of metal artefact reduction (MAR) in cone beam CT (CBCT) artefacts arising from metallic objects in the exomass. A radiographic phantom composed of 16 polypropylene tubes filled with a homogeneous radiopaque solution was created. CBCT scans were obtained with two units: Picasso Trio (Vatech, South Korea) and ProMax (Planmeca, Finland). The phantom was centred in a 5 × 5 cm field-of-view (FOV) with titanium and CoCr inserts in the exomass. All scans were repeated after enabling MAR. Mean voxel values were obtained from the 16 tubes and standard deviation was calculated as a way of measuring voxel value variability. Mean values and voxel value variability were compared individually in the presence and absence of MAR by means of analysis of variance, followed by Tukey's test (α = 0.05). In the Picasso Trio, MAR significantly decreased mean voxel values (p ≤ 0.05) and increased voxel value variability (p > 0.05) in the presence of titanium. When CoCr was present, no statistical difference (p > 0.05) was observed. In the ProMax, MAR increased significantly mean voxel values (p ≤ 0.05) in the presence of titanium, and presented no significant difference (p > 0.05) for CoCr. Voxel value variability did not differ significantly (p > 0.05) for both materials. In conclusion, MAR was not effective to correct CBCT artefacts arising from metallic objects in the exomass in the two CBCT units used.

Metal artifacts in patients with large dental implants and bridges: combination of metal artifact reduction algorithms and virtual monoenergetic images provides an approach to handle even strongest artifacts

This study compares reduction of strong metal artifacts from large dental implants/bridges using spectral detector CT-derived virtual monoenergetic images (VMI), metal artifact reduction algorithms/reconstructions (MAR), and a combination of both methods (VMIMAR) to conventional CT images (CI). Forty-one spectral detector CT (SDCT) datasets of patients that obtained additional MAR reconstructions due to strongest artifacts from large oral implants were included. CI, VMI, MAR, and VMIMAR ranging from 70 to 200keV (10keV increment) were reconstructed. Objective image analyses were performed ROI-based by measurement of attenuation (HU) and standard deviation in most pronounced hypo-/hyperdense artifacts as well as artifact impaired soft tissue (mouth floor/soft palate). Extent of artifact reduction, diagnostic assessment of soft tissue, and appearance of new artifacts were rated visually by two radiologists. The hypo-/hyperattenuating artifacts showed an increase and decrease of HU values in MAR and VMIMAR (CI/MAR/VMIMAR-200keV: - 369.8 ± 239.6/- 37.3 ± 109.6/- 46.2 ± 71.0 HU, p < 0.001 and 274.8 ± 170.2/51.3 ± 150.8/36.6 ± 56.0, p < 0.001, respectively). Higher keV values in hyperdense artifacts allowed for additional artifact reduction; however, this trend was not significant. Artifacts in soft tissue were reduced significantly by MAR and VMIMAR. Visually, high-keV VMI, MAR, and VMIMAR reduced artifacts and improved diagnostic assessment of soft tissue. Overcorrection/new artifacts were reported that mostly did not hamper diagnostic assessment. Overall interrater agreement was excellent (ICC = 0.85). In the presence of strong artifacts due to large oral implants, MAR is a powerful mean for artifact reduction. For hyperdense artifacts, MAR should be supplemented by VMI ranging from 140 to 200keV. This combination yields optimal artifact reduction and improves the diagnostic image assessment in imaging of the head and neck. • Large oral implants can cause strong artifacts. • MAR is a powerful tool for artifact reduction considering such strong artifacts. • Hyperdense artifact reduction is supplemented by VMI of 140-200keV from SDCT.

Effect of applying metal artifact reduction algorithm in cone beam computed tomography in detection of vertical root fractures of teeth with metallic post versus digital intraoral radiography

Background: Conventional radiographs are not an efficient diagnostic imaging modality to detect vertical root fracture. Cone-beam computed tomography (CBCT) is a three-dimensional imaging modality that overcomes the limitations of conventional radiography in the detection of vertical root fractures (VRFS), although metallic structures produce artifacts in the produced images and since most of the teeth with VRF are endodontically treated and have metallic posts in root canal so fracture detection may be difficult. Aim: This study aimed to determine the diagnostic accuracy of CBCT using metal artifact reduction algorithm in detection of VRF of teeth with metallic posts compared to digital intraoral radiography. Materials and Methods: One hundred and twenty single-rooted extracted human teeth were endodontically treated then placed in an acrylic block and metallic posts were inserted. The teeth roots were divided into two groups; one with induced VRF and the other having intact roots. Then, each tooth was coded and imaged three times using CBCT with and without metal artifact reduction algorithm and digital periapical radiography. Data were collected and statistically analyzed. Results: Digital periapical radiography showed statistically significantly lower diagnostic accuracy than CBCT modalities (P ≤ 0.05). The use of metal artifact reduction algorithm improved the diagnostic accuracy from CBCT1 to CBCT2 but with no statistically significant difference (P ≥ 0.05). Conclusion: From the study, we can conclude that in case of presence of metallic posts, CBCT with metal artifact reduction algorithm can improve detection of VRF.

Application of a multi-material artifact reduction algorithm in a wide-detector CT in the evaluation of the portal venous angiography of postoperative TIPS and embolization

Objective: To assess the effect of monochromatic images and metal artifact reduction (MAR) on the image quality of spectral CT portal venous angiography in patients with operation of after the performing transjugular intrahepatic portosystemic stent shunt(TIPS) and embolization. Methods: From December 2017 to April 2018, the examination data of 28 patients with portal hypertension due to cirrhosis who underwent portal vein angiography 1 month after TIPS and embolization were prospectively collected. After spectral CT scanning in revolution CT, the monochromatic energy levels(60 keV, 120 keV), 60 keV + 120 keV, 120kV-like + 120 keV fused images combined with MAR algorithm were reconstructed. Quantitative parameters such as image artifact index (AI) and qualitative visual evaluation scores were recorded and compared. Results: The 120 keV monochromatic images showed the lowest AI value(30.8±8.5, 18.2±4.3) and highest metal artifacts reduction effect. The 60 keV monochromatic images showed the highest AI value (57.3±15.7, 32.1±7.9) and the lowest metal artifacts reduction effect. The AI value of 60 keV + 120 keV fused images was lower than that of 60 keV images(26.2%, 24.7%). The difference of AI value between each group was statistically significant(all P<0.05). The interobserver agreement in the subjective image scores was moderate with kappa value of 0.824. The overall image quality score of 60 keV + 120 keV fused image and the noise score of 120 kV-like+120 keV were higher than the remaining groups. The differences of the subjective scores among each group were statistically significant(all P<0.05). Conclusion: The spectral CT with MAR algorithm can effectively improve the image quality of portal vein angiography after the TIPS and embolization therapy and the 60 keV + 120 keV fused images can eliminate artifacts and ensure a clear display of blood vessels.

The value of iterative metal artifact reduction algorithms during antenna positioning for CT-guided microwave ablation

Objectives: To compare image quality between filtered back projection (FBP) and iterative reconstruction algorithm and dedicated metal artifact reduction (iMAR) algorithms during antenna positioning for computed tomography-guided microwave ablation (MWA).Materials and methods: An MWA antenna was positioned in the liver of five pigs under CT guidance. Different exposure settings (120kVp/200mAs-120kVp/50mAs) and image reconstruction techniques (FBP, iterative reconstruction with and without iMAR) were applied. Quantitative image analysis included density measurements in six positions (e.g., liver in extension of the antenna [ANTENNA] and liver >3 cm away from the antenna [LIVER-1]). Qualitative image analysis included assessment of overall quality, image noise, artifacts at the antenna tip, artifacts in liver parenchyma bordering antenna tip and newly generated artifacts. Two independent observers performed the analyses twice and interreader agreement was compared with Bland–Altman analysis.Results: For all exposure and reconstruction settings, density measurements for ANTENNA were significantly higher for the I30-1 iMAR compared with FBP and I30-1 (e.g., 8.3−17.2HU vs. −104.5 to 155.1HU; p ≤ 0.01, respectively). In contrast, for all exposure settings, density measurements for LIVER-1 were comparable between FBP and I30-1 iMAR (e.g., 49.4−50.4HU vs. 50.1−52.5U, respectively). For all exposure and reconstruction settings, subjective image quality for LIVER-1 was better for the I30-1 iMAR algorithm compared with FBP and I30-1. Bland–Altman interobserver agreement was from −0.2 to 0.2 for FBP and iMAR, and Cohen’s kappa was 0.74.Conclusion: Iterative algorithms I30-1 with iMAR algorithm improves image quality during antenna positioning and placement for CT-guided MWA and is applicable over a range of exposure settings.

19 Assessment of Smart MAR (Metal Artifact Reduction) algorithm for metallic artifact correction in external radiotherapy planning

Introduction When metal prosthesis was present in CT acquisition, like hip or spinal prostheses, image quality was deteriorated and the Hounsfield Units (HU) became corrupted by metal artifacts. They may obscure visualization of anatomical structures and affect dose calculation accuracy. Usually, these artifacts are manually corrected by delineation and by assigning electron density. This correction was time consuming and operator-dependent. The aim of this study was to evaluate the capability of MAR (Metal Artifact Reduction) algorithm1 [1] to restore real HU and avoid manual correction. Methods The phantom study was conducted using a CT electron density phantom CIRS (model 062). The phantom contained inserts made of various tissue-simulating materials. The phantom was scanned on a Optima 580 CT scanner (GE Healthcare) (120 kv, mAs auto) without metal (reference CT scan) and with titanium hip prosthesis (4.2 g.cm−3) and steel hip prosthesis (8 g.cm−3). These scans were reconstructed with and without MAR algorithm. In the presence of the metal prosthesis, the mean HU and corresponding standard deviations were determined for each rod with and without MAR correction and compare with the reference CT scan. Results Without MAR correction, HU were significantly impacted by metal artefact. HU errors were close to 20% for titanium hip prosthesis and 50% for steel hip prosthesis. With MAR correction, the mean HU were in good agreement with reference CT image (less 2%). However, residual artifacts were still visually observable. Conclusions Using MAR correction algorithm can potentially improve the global efficiency of RT treatment in the presence of metallic implants. In further works, the dosimetric impact of MAR will be investigated for various clinical ballistics.

Metal Artifact Reduction of Dental Fillings in Head and Neck CT Images

Introduction: The issue of metal artifact and its reduction is as old as the clinical use of computed tomography itself. When metal objects such as dental fillings, hip prostheses or surgical clips are present in the computed tomography (CT) field of view (FOV), make severe artifacts that reduce the image quality and accuracy of CT numbers. They can lead to unreliable clinical results due to inability in true tumor volume delineation and uncertainty in dose calculation of treatment planning software. So far, no generally accepted solution to this problem, especially for small size dental fillings, has been found. Herein, an approach is presented to reduce the dental filling artifacts in CT images of head and neck patients with multiple dental fillings. The proposed approach is compared with the commercial orthopedic metal artifact reduction (O-MAR) algorithm by Philips company. Materials and Methods: Our algorithm consists of six steps: 1) metallic object segmentation by thresholding, 2) obtaining the prior image by multi thresholding of the initial image, 3) normalization of the original image sinogram by prior image, 4) interpolation (replacing the affected projection data by previous slice unaffected projection data, 5) denormalization of the corrected sinogram and 6) application of a two dimensional adaptive filter to the final image. The forth step can be implemented by substitution of affected projections with opposite view projections. Both methods were tested and previous slice substitution was superior. Quantitative evaluation of the commercial and proposed algorithms was done in terms of peak signal-to-noise ratio (PSNR), signal-to- noise ratio (SNR) and structural similarity index (SSIM) on a CT image of a head and neck patient with four dental fillings. The posterior part of the head in original image was considered as the reference image. Also two uniform regions of interest (ROI) were considered in the image. ROI1 was posterior region of the head away from dental fillings, while ROI2 was restricted to the tongue region near to the dental fillings. Results: The PSNR, SNR, SSIM values obtained by the proposed method and O-MAR algorithm were 37.05, 36.13, 22.29, 21.37, 0.93 and 0.84, respectively. Using our approach, the standard deviation of CT numbers in ROI1 was 27 times less than the original image and 7% better than the commercial competitor. However, for ROI2 the commercial algorithm was 36% more successful in reduction of variations due to metal artifact. Conclusion: The proposed approach can be applied successfully for dental filling artifact reduction in head and neck patients. Although the performance of the commercial method was superior to ours in near dental regions, for the farther regions with more critical organs was vice versa. However, weighting the interpolation and adaptive filtering steps may result in even better results.

17 Impact of a CT artifact reduction algorithm in radiotherapy: preliminary qualitative and quantitative aspects

Introduction In radiotherapy, CT images have two main objectives: allow volumes delineation and obtain electron densities for dose calculation. The presence of dense structures such as hip prostheses or dental implants create artifacts that can significantly impair both of these goals. Iterative Metal Artifact Reduction (IMAR) is a metal artifact reduction algorithm developed by Siemens, which greatly facilitates the delimitation of volumes. We studied its impact on the modification of Hounsfield units (HU) as well as the dosimetric consequences for pelvis and head&neck (H&N) regions. Methods All CT acquisitions were made on a Somatom AS 20 Definition (Siemens) at 120 kV, 400 mAs and 2 mm slice thickness. For the pelvic region, we used a phantom composed of a water equivalent material in which we inserted two hip prostheses. For the H&N sphere, we used an anthropomorphic phantom (CIRS Atom 701) with two pieces of lead inserted in the teeth. CT acquisitions were performed with inserts (prostheses or lead), with IMAR reconstruction (imarCT), and without (artefactCT). We used the protocol HIP implants for pelvis and Dental fillings for H&N. A CT acquisitions without insert were also done (baselineCT). Using the Matlab software (V2014), we calculated, in significant regions (between metal inserts), the HU differences between baselineCT and respectively imarCT and artefactCT. In order to estimate the dosimetric impact of these HU variations we performed a treatment planification on both phantoms and on patients with pelvic and dental implants with the treatment planning system V13.6 (Varian). In pelvic we use 4 static fields of 15 MV and in the H&N 2 RapidArc beams of 6 MV. The results are given in terms of mean, standard deviation ( σ ) and most likely value (p). Results For the pelvic area, the HU deviations from the baseline CT are 84 HU (σ = 53 HU, p = 100 HU) and −106 HU (σ = 487 HU, p = 200 HU) respectively for imarCT and artefactCT. For the H&N the HU deviations are 15 HU (σ = 34 HU, p = 0 HU) and 44 HU (σ = 413 HU, p = 200 HU) respectively for imarCT and artefactCT. We note in both cases a spread of HU without IMAR and a slight overestimation of HU with IMAR. The dosimetric impact, based on the comparison between the planification made with imarCT and artefactCT showed for the pelvic area, in the phantom, a dose variation from −11% to 1% with an average of −4.6% (σ = 3%) while in the patient, the variation is between −10.8% and +0.4% with an average of−3.4% (σ = 2.3%). For the H&N area, in the phantom, the difference in dose varies from −10% to 7.4% with an average of −0.6% (σ = 2%), whereas in the patient, the difference in dose varies from −3% to 24% with an average of 0.6% (σ = 2%). Conclusions In addition to being an undeniable aid to delineation, it seems in this preliminary study that IMAR is a significant tool to issue metal artefact problem in calculating dose in patient with metal implants close to treated areas.

Metal artifact reduction on cervical CT images by deep residual learning

BackgroundCervical cancer is the fifth most common cancer among women, which is the third leading cause of cancer death in women worldwide. Brachytherapy is the most effective treatment for cervical cancer. For brachytherapy, computed tomography (CT) imaging is necessary since it conveys tissue density information which can be used for dose planning. However, the metal artifacts caused by brachytherapy applicators remain a challenge for the automatic processing of image data for image-guided procedures or accurate dose calculations. Therefore, developing an effective metal artifact reduction (MAR) algorithm in cervical CT images is of high demand.MethodsA novel residual learning method based on convolutional neural network (RL-ARCNN) is proposed to reduce metal artifacts in cervical CT images. For MAR, a dataset is generated by simulating various metal artifacts in the first step, which will be applied to train the CNN. This dataset includes artifact-insert, artifact-free, and artifact-residual images. Numerous image patches are extracted from the dataset for training on deep residual learning artifact reduction based on CNN (RL-ARCNN). Afterwards, the trained model can be used for MAR on cervical CT images.ResultsThe proposed method provides a good MAR result with a PSNR of 38.09 on the test set of simulated artifact images. The PSNR of residual learning (38.09) is higher than that of ordinary learning (37.79) which shows that CNN-based residual images achieve favorable artifact reduction. Moreover, for a 512 × 512 image, the average removal artifact time is less than 1 s.ConclusionsThe RL-ARCNN indicates that residual learning of CNN remarkably reduces metal artifacts and improves critical structure visualization and confidence of radiation oncologists in target delineation. Metal artifacts are eliminated efficiently free of sinogram data and complicated post-processing procedure.

Impact of Single-Energy Metal Artifact Reduction on CT image quality in patients with dental hardware

BackgroundTo evaluate whether Canon's Single-Energy Metal Artifact Reduction (SEMAR) algorithm can significantly improve subjective and objective image quality of patients with nonremovable dental hardware undergoing CT imaging of the oral cavity and oropharynx. Materials and methodsSEMAR was reconstructed from routine Adaptive Iterative Dose Reduction (AIDR) images in 154 patients (46 females and 108 males; mean age 66.3 ± 10.5 years). Subjective SEMAR and AIDR image quality of the mouth floor, sublingual glands, lymphatic ring and overall impression were evaluated by two independent radiologists on a 6-point scale (1 = very good image quality, 6 = poor image quality) and compared to ratings of an oral and maxillofacial surgeon. Interrater agreement was assessed using the intraclass correlation coefficient (ICC). Objective image analysis was performed by placing regions of interest (ROIs) on the mouth floor and measuring CT attenuation in Hounsfield units (HU) and standard deviation (SD). ResultsSEMAR significantly improved subjective image quality in all evaluated structures for all raters (p < 0.001). Furthermore, SEMAR significantly reduced objective metal artifacts and image noise (p < 0.001). ConclusionSEMAR significantly improved diagnostic quality of CT images of the oral cavity and oropharynx by reducing artifacts caused by dental hardware.

Comparison of Metal Artifact Reduction in Dual- and Single-Source CT: A Vertebral Phantom Study.

The objective of this study was to compare the capability of two algorithms for metal artifact reduction and virtual monoenergetic imaging (VME), a metal artifact reduction application for dual-source CT. A bovine vertebra phantom with 16 artificial osteolyses and two 20 × 4.5 mm stainless steel screws was scanned on two single-source CT scanner and one dual-source CT scanner at a dose identical to the single-source acquisitions. Datasets were reconstructed with a metal artifact reduction algorithm for orthopedic implants (O-MAR, Philips Healthcare), an iterative metal artifact reduction algorithm (iMAR, Siemens Healthineers), and VME. Blinded to the method used for artifact reduction, three independent observers evaluated datasets regarding the extent of metal artifacts using a 4-point scale. Depicted osteolyses were counted and screw diameters measured for each reconstruction. Interobserver variability was evaluated using the Kendall coefficient of concordance for ordinal variables and the intraclass correlation coefficient for continuous data. VME showed the best metal artifact reduction capability among evaluated methods; overall artifacts were rated 1.08 ± 0.29 for VME, 3.33 ± 0.65 for iMAR, and 3.91 ± 0.29 for O-MAR (p < 0.01). VME resulted in better representation of the cortical bone, trabecular structure, and soft tissue compared with the other two algorithms. VME provided the most realistic reconstruction of screw diameter. However, VME missed osteolyses. Good to almost perfect agreement was achieved for nearly all evaluated attributes. In our vertebral phantom, VME led to the most detailed representation of the osteosynthesis screw, caused the lowest amount of artifact, and represented the adjacent tissue best. Thus, VME should be considered as an alternative method to evaluate implants when other algorithms fail.

Subtraction CTA: An Alternative Imaging Option for the Follow-Up of Flow-Diverter-Treated Aneurysms?

This was a pilot study to explore the diagnostic accuracy and safety of subtraction CTA combined with a single-energy metal artifact reduction algorithm (SEMAR) compared to DSA for the evaluation of intracranial aneurysm occlusion after flow diverter treatment. We included patients treated with a flow diverter for an unruptured intracranial aneurysm between November 2015 and November 2016. The patient cohort comprised 2 groups: those who underwent follow-up imaging 1 month after flow-diverter treatment and those with a known residual intracranial aneurysm after flow diverter treatment who underwent imaging at regular follow-ups. Full-brain subtraction CTA was performed on a 320-detector row CT system. A low-dose non-enhanced volume acquisition was followed by a contrast-enhanced volume CTA. Iterative and noise-reduction filters, SEMAR, and SURESubtraction algorithms were applied. DSA was performed on a flat panel C-arm angiography system. Standard posteroanterior, lateral, 3D, and detailed 2D acquisitions were performed. Imaging was independently scored by 2 clinicians. Aneurysm occlusion (Raymond scale) was our primary outcome parameter. Thirteen intracranial aneurysms were evaluated with subtraction CTA and DSA. Nine aneurysm remnants were demonstrated by both subtraction CTA and DSA. The sensitivity and specificity of subtraction CTA for the detection of aneurysm occlusion were 100% (95% CI, 82.41%-100%) and 100% (95% CI, 67.55%-100%), respectively. Agreement between readers was perfect (κ = 1.0). The smallest neck remnant detected on subtraction CTA was 1.2 mm. No complications occurred. Subtraction CTA with single-electron metal artifact reduction is effective in the reduction of metal artifacts of flow diverters and might therefore be a viable alternative in the assessment of intracranial aneurysm occlusion after flow diverter treatment.

Study on the usefulness of Metal artifact reduction algorithm and MonoE reconstruction (Dual Layer Detector) overlapping application

Study on the usefulness of Metal artifact reduction algorithm and MonoE reconstruction (Dual Layer Detector) overlapping application