Artifact Reduction Algorithm Research Articles

Comparative Evaluation of Artifacts Originated by Four Different Post Materials Using Different CBCT Settings.

Forty premolar teeth were used in this study. Root canals were treated, and teeth were randomly distributed into four subgroups (n = 10) for the preparation of post materials: titanium, gold (Nordin), quartz fiber (Bisco DT Light), and glass fiber (Rely X). The CBCT images were taken with two different kVps, three different metal artifact reduction (MAR) algorithm options, and two different resolutions. For each protocol, the effective dose was calculated according to the dose area production (DAP) value. The standard analysis of variance technique and the Tukey multiple comparison adjustment method were used to assess interactions among material types, kVp, MAR, and voxel settings. More artifacts were found in the middle third than in the cervical third (p < 0.05). The mean value of artifacts was highest for gold (Nordin), 90 kVp, no MAR, and 100 voxel size. Glass or quartz fiber posts at low resolution, with high MAR and 96 kVp, originated fewer artifacts. Moreover, the use of 90 and 96 kVp with 200 voxel size and high MAR provided the least amount of radiation. The best setting for radiographic follow-up of post materials on the Planmeca ProMax is 96 kVp with low resolution and high MAR; this setting produced one of the lowest effective doses. This study estimated the best scanning protocol by lowering the effective dose to a minimum level according to the "as low as reasonably achievable" principle, as well as assessing the tooth region and the post material generating the fewest artifacts, in order to prevent image interpretation challenges such as false-positive and false-negative results stemming from the deterioration of the visibility of the root canal due to perforation, fractures, and voids in the root canal region.

Advances in Bone Joint Imaging-Metal Artifact Reduction.

Numerous types of metal implants have been introduced in orthopedic surgery and are used in everyday practice. To precisely evaluate the postoperative condition of arthroplasty or trauma surgery, periprosthetic infection, and the loosening of implants, it is important to reduce artifacts induced by metal implants. In this review, we focused on technical advances in metal artifact reduction using digital tomosynthesis, computed tomography, and magnetic resonance imaging. We discussed new developments in diagnostic imaging methods and the continuous introduction of novel technologies to reduce metal artifacts; however, these innovations have not yet completely removed metal artifacts. Different algorithms need to be selected depending on the size, shape, material and implanted body parts of an implant. Future advances in metal artifact reduction algorithms and techniques and the development of new sequences may enable further reductions in metal artifacts even on original images taken previously. Moreover, the combination of different imaging modalities may contribute to further reductions in metal artifacts. Clinicians must constantly update their knowledge and work closely with radiologists to select the best diagnostic imaging method for each metal implant.

A novel approach for eliminating metal artifacts based on MVCBCT and CycleGAN.

To develop a metal artifact reduction (MAR) algorithm and eliminate the adverse effects of metal artifacts on imaging diagnosis and radiotherapy dose calculations. Cycle-consistent adversarial network (CycleGAN) was used to generate synthetic CT (sCT) images from megavoltage cone beam CT (MVCBCT) images. In this study, there were 140 head cases with paired CT and MVCBCT images, from which 97 metal-free cases were used for training. Based on the trained model, metal-free sCT (sCT_MF) images and metal-containing sCT (sCT_M) images were generated from the MVCBCT images of 29 metal-free cases and 14 metal cases, respectively. Then, the sCT_MF and sCT_M images were quantitatively evaluated for imaging and dosimetry accuracy. The structural similarity (SSIM) index of the sCT_MF and metal-free CT (CT_MF) images were 0.9484, and the peak signal-to-noise ratio (PSNR) was 31.4 dB. Compared with the CT images, the sCT_MF images had similar relative electron density (RED) and dose distribution, and their gamma pass rate (1 mm/1%) reached 97.99% ± 1.14%. The sCT_M images had high tissue resolution with no metal artifacts, and the RED distribution accuracy in the range of 1.003 to 1.056 was improved significantly. The RED and dose corrections were most significant for the planning target volume (PTV), mandible and oral cavity. The maximum correction of Dmean and D50 for the oral cavity reached 90 cGy. Accurate sCT_M images were generated from MVCBCT images based on CycleGAN, which eliminated the metal artifacts in clinical images completely and corrected the RED and dose distributions accurately for clinical application.

Combined clustered scan-based metal artifact reduction algorithm (CCS-MAR) for ultrasound-guided cardiac radioablation

Cardiac radioablation is a promising treatment for cardiac arrhythmias, but accurate dose delivery can be affected by heart motion. For this reason, real-time cardiac motion monitoring during radioablation is of paramount importance. Real-time ultrasound (US) guidance can be a solution. The US-guided cardiac radioablation workflow can be simplified by the simultaneous US and planning computed tomography (CT) acquisition, which can result in US transducer-induced metal artifacts on the planning CT scans. To reduce the impact of these artifacts, a new metal artifact reduction (MAR) algorithm (named: Combined Clustered Scan-based MAR [CCS-MAR]) has been developed and compared with iMAR (Siemens), O-MAR (Philips) and MDT (ReVision Radiology) algorithms. CCS-MAR is a fully automated sinogram inpainting-based MAR algorithm, which uses a two-stage correction process based on a normalized MAR method. The second stage aims to correct errors remaining from the first stage to create an artifact-free combined clustered scan for the process of metal artifact reduction. To evaluate the robustness of CCS-MAR, conventional CT scans and/or dual-energy CT scans from three anthropomorphic phantoms and transducers with different sizes were used. The performance of CCS-MAR for metal artifact reduction was compared with other algorithms through visual comparison, image quality metrics analysis, and HU value restoration evaluation. The results of this study show that CCS-MAR effectively reduced the US transducer-induced metal artifacts and that it improved HU value accuracy more or comparably to other MAR algorithms. These promising results justify future research into US transducer-induced metal artifact reduction for the US-guided cardiac radioablation purposes.

Metal artifact reduction and iterative reconstruction algorithms in computed tomography imaging of hip prostheses

Introduction: Computed tomography (CT) of metal hip prostheses are prone to experiencing metal-related artefacts during the imaging process. The resulting low image quality can affect the surrounding tissue and the metal implant itself which can lead to the loss of relevant findings. Various techniques have been used to reduce metal artifacts, which can improve the image quality by minimising their origin.

Demonstration and Performance Evaluation of Two Novel Algorithms for Removing Artifacts From Automated Intraoperative Temperature Data Sets: Multicenter, Observational, Retrospective Study.

BackgroundThe automated acquisition of intraoperative patient temperature data via temperature probes leads to the possibility of producing a number of artifacts related to probe positioning that may impact these probes’ utility for observational research.ObjectiveWe sought to compare the performance of two de novo algorithms for filtering such artifacts.MethodsIn this observational retrospective study, the intraoperative temperature data of adults who received general anesthesia for noncardiac surgery were extracted from the Multicenter Perioperative Outcomes Group registry. Two algorithms were developed and then compared to the reference standard—anesthesiologists’ manual artifact detection process. Algorithm 1 (a slope-based algorithm) was based on the linear curve fit of 3 adjacent temperature data points. Algorithm 2 (an interval-based algorithm) assessed for time gaps between contiguous temperature recordings. Sensitivity and specificity values for artifact detection were calculated for each algorithm, as were mean temperatures and areas under the curve for hypothermia (temperatures below 36 °C) for each patient, after artifact removal via each methodology.ResultsA total of 27,683 temperature readings from 200 anesthetic records were analyzed. The overall agreement among the anesthesiologists was 92.1%. Both algorithms had high specificity but moderate sensitivity (specificity: 99.02% for algorithm 1 vs 99.54% for algorithm 2; sensitivity: 49.13% for algorithm 1 vs 37.72% for algorithm 2; F-score: 0.65 for algorithm 1 vs 0.55 for algorithm 2). The areas under the curve for time × hypothermic temperature and the mean temperatures recorded for each case after artifact removal were similar between the algorithms and the anesthesiologists.ConclusionsThe tested algorithms provide an automated way to filter intraoperative temperature artifacts that closely approximates manual sorting by anesthesiologists. Our study provides evidence demonstrating the efficacy of highly generalizable artifact reduction algorithms that can be readily used by observational studies that rely on automated intraoperative data acquisition.

The impact of a metal artefact reduction algorithm on treatment planning for patients undergoing radiotherapy of the pelvis.

Metallic implants cause artefacts in computed tomography (CT) images and can introduce significant errors to structure visualisation and dosimetric calculation within the radiotherapy planning process. This study evaluated an orthopaedic metal artefact reduction algorithm and its effect on the CT number, image noise, structure delineation, and treatment dose. Raw CT data were reconstructed using standard filtered back projection and an artefact reduction algorithm to create 'standard' and 'corrected' images. A phantom containing tissue-mimicking inserts and two titanium plugs was imaged. The average CT number was compared to baseline data acquired without metal inserts. Data from 11 pelvic external beam radiotherapy (EBRT) patients with bi- or uni-lateral hip implants were retrospectively analysed. The clinically used treatment plans were re-computed on the corrected images. A prostate-mimicking phantom containing metal 'implants' was imaged, and 11 observers contoured both reconstructions. The artefact reduction algorithm improved the CT number in those areas most affected by metal artefacts and decreased noise by 19% (P=.04) Changes in dose distributions on corrected images compared to those calculated using the current clinical protocol were clinically insignificant. Volumes contoured on the corrected phantom images had larger Dice coefficients than those contoured on the standard images (P=.001), as well as a 36% lower standard deviation in volumes. This study demonstrates that the metal artefact reduction software reduces the error in CT numbers, can improve delineation accuracy, and can reduce inter-observer variability. It has the potential to streamline the planning pathway and improve treatment planning accuracy.

Metal implants on abdominal CT: does split-filter dual-energy CT provide additional value over iterative metal artifact reduction?

PurposeTo assess image quality and metal artifact reduction in split-filter dual-energy CT (sfDECT) of the abdomen with hip or spinal implants using virtual monoenergetic images (VMI) and iterative metal artifact reduction algorithm (iMAR).Methods102 portal-venous abdominal sfDECTs of patients with hip (n = 71) or spinal implants (n = 31) were included in this study. Images were reconstructed as 120kVp-equivalent images (Mixed) and VMI (40–190 keV), with and without iMAR. Quantitative artifact and image noise was measured using 12 different ROIs. Subjective image quality was rated by two readers using a five-point Likert-scale in six categories, including overall image quality and vascular contrast.ResultsLowest quantitative artifact in both hip and spinal implants was measured in VMI190keV-iMAR. However, it was not significantly lower than in MixediMAR (for all ROIs, p = 1.00), which were rated best for overall image quality (hip: 1.00 [IQR: 1.00–2.00], spine: 3.00 [IQR:2.00–3.00]). VMI50keV-iMAR was rated best for vascular contrast (hip: 1.00 [IQR: 1.00–2.00], spine: 2.00 [IQR: 1.00–2.00]), which was significantly better than Mixed (both, p < 0.001). VMI50keV-iMAR provided superior overall image quality compared to Mixed for hip (1.00 vs 2.00, p < 0.001) and similar diagnostic image quality for spinal implants (2.00 vs 2.00, p = 0.51).ConclusionFor abdominal sfDECT with hip or spinal implants MixediMAR images should be used. High keV VMI do not further improve image quality. IMAR allows the use of low keV images (VMI50keV) to improve vascular contrast, compared to Mixed images.Graphical abstract

Diagnostic efficacy of 3 imaging modalities in the detection of fractured endodontic instruments: an in vitro study

<h3>Objectives</h3> To compare the diagnostic efficacy of periapical radiography (PAR) and cone beam computed tomography (CBCT) without and with a metal artifact reduction algorithm (MARA) in the detection of fractured endodontic instruments in unfilled and filled root canals that were straight and curved. <h3>Study Design</h3> In total, 144 root canals from 48 mandibular molars were divided into 4 groups: the control group with empty canals, the fracture group with a fractured instrument, the fill group with root canal filling, and the fracture/fill group with a fractured instrument and root canal filling. The teeth were examined with PAR using a complementary metal oxide semiconductor sensor and CBCT without and with MARA to evaluate the diagnostic efficacy of the 3 techniques in the detection of fractured instruments. Three examiners evaluated the radiographs. <h3>Results</h3> Overall, PAR exhibited significantly better diagnostic outcomes than CBCT without MARA. PAR yielded significantly better results than both CBCT protocols in the presence of root filling and in straight canals. In the absence of filling and in curved canals, there were no statistically significant differences among the 3 techniques. <h3>Conclusions</h3> PAR was the imaging technique that best detected fractured instruments in filled and unfilled straight root canals.

Prediction of proton beam range in phantom with metals based on monochromatic energy CT images.

The purpose of the study was to evaluate the accuracy of monochromatic energy (MonoE) computed tomography (CT) images reconstructed by spectral CT in predicting the stopping power ratio $( SP{R}_w)$ of materials in the presence of metal. The CIRS062 phantom was scanned three times using spectral CT. In the first scan, a solid water insert was placed at the center of the phantom $(C{T}_{no\ metal})$. In the second scan, the solid water insert was replaced with a titanium alloy femoral head $(C{T}_{metal})$. The metal artifact reduction (MAR) algorithm was used in the last scan $(C{T}_{metal+ MAR})$. The MonoE-CT images of 40keV and 80keV were reconstructed. Finally, the single-energy CT method (SECT) and the dual-energy CT method (DECT) were used to calculate the $SP{R}_w$. The mean absolute error (MAE) of the $SP{R}_w$ of the inner layer inserts calculated by the SECT method were 3.19%, 13.88% and 2.71%, corresponding to $C{T}_{no\ metal}$, $C{T}_{metal}$ and $C{T}_{metal+ MAR}$, respectively. For the outer layer inserts, the MAE of $SP{R}_w$ were 3.43%, 5.42% and 2.99%, respectively. Using the DECT method, the MAE of the $SP{R}_w$ of the inner layer inserts was 1.30%, 3.69% and 1.46% and the MAE of the outer layer inserts- was 1.34%, 1.36% and 1.05%. The studies shows that, compared with the SECT method, the accuracy of the DECT method in predicting the $SP{R}_w$ of a material is more robust to the presence of metal. Using the MAR algorithm when performing CT scans can further improve the accuracy of predicting the SPR of materials in the presence of metal.

Metal artifact reduction in ultra-high-resolution cone-beam CT imaging with a twin robotic X-ray system

Cone-beam computed tomography (CBCT) has been shown to be a powerful tool for 3D imaging of the appendicular skeleton, allowing for detailed visualization of bone microarchitecture. This study was designed to compare artifacts in the presence of osteosynthetic implants between CBCT and multidetector computed tomography (MDCT) in cadaveric wrist scans. A total of 32 scan protocols with varying tube potential and current were employed: both conventional CBCT and MDCT studies were included with tube voltage ranging from 60 to 140 kVp as well as additional MDCT protocols with dedicated spectral shaping via tin prefiltration. Irrespective of scanner type, all examinations were conducted in ultra-high-resolution (UHR) scan mode. For reconstruction of UHR-CBCT scans an additional iterative metal artifact reduction algorithm was employed, an image correction tool which cannot be used in combination with UHR-MDCT. To compare applied radiation doses between both scanners, the volume computed tomography dose index for a 16 cm phantom (CTDIvol) was evaluated. Images were assessed regarding subjective and objective image quality. Without automatic tube current modulation or tube potential control, radiation doses ranged between 1.3 mGy (with 70 kVp and 50.0 effective mAs) and 75.2 mGy (with 140 kVp and 383.0 effective mAs) in UHR-MDCT. Using the pulsed image acquisition method of the CBCT scanner, CTDIvol ranged between 2.3 mGy (with 60 kVp and 0.6 mean mAs per pulse) and 61.0 mGy (with 133 kVp and 2.5 mean mAs per pulse). In essence, all UHR-CBCT protocols employing a tube potential of 80 kVp or more were found to provide superior overall image quality and artifact reduction compared to UHR-MDCT (all p < .050). Interrater reliability of seven radiologists regarding image quality was substantial for tissue assessment and moderate for artifact assessment with Fleiss kappa of 0.652 (95% confidence interval 0.618–0.686; p < 0.001) and 0.570 (95% confidence interval 0.535–0.606; p < 0.001), respectively. Our results demonstrate that the UHR-CBCT scan mode of a twin robotic X-ray system facilitates excellent visualization of the appendicular skeleton in the presence of metal implants. Achievable image quality and artifact reduction are superior to dose-comparable UHR-MDCT and even MDCT protocols employing spectral shaping with tin prefiltration do not achieve the same level of artifact reduction in adjacent soft tissue.

Impact of metal artifact reduction algorithm on gross tumor volume delineation in tonsillar cancer: reducing the interobserver variation

BackgroundPatients with tonsillar cancer (TC) often have dental fillings that can significantly degrade the quality of computed tomography (CT) simulator images due to metal artifacts. We evaluated whether the use of the metal artifact reduction (MAR) algorithm reduced the interobserver variation in delineating gross tumor volume (GTV) of TC.MethodsEighteen patients with TC with dental fillings were enrolled in this study. Contrast-enhanced CT simulator images were reconstructed using the conventional (CTCONV) and MAR algorithm (CTMAR). Four board-certified radiation oncologists delineated the GTV of primary tumors using routine clinical data first on CTCONV image datasets (GTVCONV), followed by CTCONV and CTMAR fused image datasets (GTVMAR) at least 2 weeks apart. Intermodality differences in GTV values and Dice similarity coefficient (DSC) were compared using Wilcoxon’s signed-rank test.ResultsGTVMAR was significantly smaller than GTVCONV for three observers. The other observer showed no significant difference between GTVCONV and GTVMAR values. For all four observers, the mean GTVCONV and GTVMAR values were 14.0 (standard deviation [SD]: 7.4) cm3 and 12.1 (SD: 6.4) cm3, respectively, with the latter significantly lower than the former (p < 0.001). The mean DSC of GTVCONV and GTVMAR was 0.74 (SD: 0.10) and 0.77 (SD: 0.10), respectively, with the latter significantly higher than that of the former (p < 0.001).ConclusionsThe use of the MAR algorithm led to the delineation of smaller GTVs and reduced interobserver variations in delineating GTV of the primary tumors in patients with TC.

A streak artifact reduction algorithm in sparse-view CT using a self-supervised neural representation.

Sparse-view computed tomography (CT) has been attracting attention for its reduced radiation dose and scanning time. However, analytical image reconstruction methods suffer from streak artifacts due to insufficient projection views. Recently, various deep learning-based methods have been developed to solve this ill-posed inverse problem. Despite their promising results, they are easily overfitted to the training data, showing limited generalizability to unseen systems and patients. In this work, we propose a novel streak artifact reduction algorithm that provides a system- and patient-specificsolution. Motivated by the fact that streak artifacts are deterministic errors, we regenerate the same artifacts from a prior CT image under the same system geometry. This prior image need not be perfect but should contain patient-specific information and be consistent with full-view projection data for accurate regeneration of the artifacts. To this end, we use a coordinate-based neural representation that often causes image blur but can greatly suppress the streak artifacts while having multiview consistency. By employing techniques in neural radiance fields originally proposed for scene representations, the neural representation is optimized to the measured sparse-view projection data via self-supervised learning. Then, we subtract the regenerated artifacts from the analytically reconstructed original image to obtain the final correctedimage. To validate the proposed method, we used simulated data of extended cardiac-torso phantoms and the 2016 NIH-AAPM-Mayo Clinic Low-Dose CT Grand Challenge and experimental data of physical pediatric and head phantoms. The performance of the proposed method was compared with a total variation-based iterative reconstruction method, naive application of the neural representation, and a convolutional neural network-based method. In visual inspection, it was observed that the small anatomical features were best preserved by the proposed method. The proposed method also achieved the best scores in the visual information fidelity, modulation transfer function, and lung nodulesegmentation. The results on both simulated and experimental data suggest that the proposed method can effectively reduce the streak artifacts while preserving small anatomical structures that are easily blurred or replaced with misleading features by the existing methods. Since the proposed method does not require any additional training datasets, it would be useful in clinical practice where the large datasets cannot becollected.

Near-field clutter artifact reduction algorithm based on wavelet thresholding method in echocardiography using 3D printed cardiac phantom

Near-field clutter artifact reduction algorithm based on wavelet thresholding method in echocardiography using 3D printed cardiac phantom

An adaptive CT metal artifact reduction algorithm that combines projection interpolation and physical correction

To propose an adaptive weighted CT metal artifact reduce algorithm that combines projection interpolation and physical correction. A normalized metal projection interpolation algorithm was used to obtain the initial corrected projection data. A metal physical correction model was then introduced to obtain the physically corrected projection data. To verify the effectiveness of the method, we conducted experiments using simulation data and clinical data. For the simulation data, the quantitative indicators PSNR and SSIM were used for evaluation, while for the clinical data, the resultant images were evaluated by imaging experts to compare the artifact-reducing performance of different methods. For the simulation data, the proposed method improved the PSNR value by at least 0.2 dB and resulted in the highest SSIM value among the methods for comparison. The experiment with the clinical data showed that the imaging experts gave the highest scores of 3.616±0.338 (in a 5-point scale) to the images processed using the proposed method, which had significant better artifact-reducing performance than the other methods (P < 0.001). The metal artifact reduction algorithm proposed herein can effectively reduce metal artifacts while preserving the tissue structure information and reducing the generation of new artifacts.

Accuracy Assessment of Percutaneous Pedicle Screw Placement Using Cone Beam Computed Tomography with Metal Artifact Reduction.

Metal artifact reduction (MAR) algorithms are used with cone beam computed tomography (CBCT) during augmented reality surgical navigation for minimally invasive pedicle screw instrumentation. The aim of this study was to assess intra- and inter-observer reliability of pedicle screw placement and to compare the perception of baseline image quality (NoMAR) with optimized image quality (MAR). CBCT images of 24 patients operated on for degenerative spondylolisthesis using minimally invasive lumbar fusion were analyzed retrospectively. Images were treated using NoMAR and MAR by an engineer, thus creating 48 randomized files, which were then independently analyzed by 3 spine surgeons and 3 radiologists. The Gertzbein and Robins classification was used for screw accuracy rating, and an image quality scale rated the clarity of pedicle screw and bony landmark depiction. Intra-class correlation coefficients (ICC) were calculated. NoMAR and MAR led to similarly good intra-observer (ICC > 0.6) and excellent inter-observer (ICC > 0.8) assessment reliability of pedicle screw placement accuracy. The image quality scale showed more variability in individual image perception between spine surgeons and radiologists (ICC range 0.51–0.91). This study indicates that intraoperative screw positioning can be reliably assessed on CBCT for augmented reality surgical navigation when using optimized image quality. Subjective image quality was rated slightly superior for MAR compared to NoMAR.

Correction of Motion Artifacts in Dark-Field Radiography of the Human Chest.

Dark-field radiography of the human chest is a promising novel imaging technique with the potential of becoming a valuable tool for the early diagnosis of chronic obstructive pulmonary disease and other diseases of the lung. The large field-of-view needed for clinical purposes could recently be achieved by a scanning system. While this approach overcomes the limited availability of large area grating structures, it also results in a prolonged image acquisition time, leading to concomitant motion artifacts caused by intrathoracic movements (e.g. the heartbeat). Here we report on a motion artifact reduction algorithm for a dark-field X-ray scanning system, and its successful evaluation in a simulated chest phantom and human in vivo chest X-ray dark-field data. By partitioning the acquired data into virtual scans with shortened acquisition time, such motion artifacts may be reduced or even fully avoided. Our results demonstrate that motion artifacts (e.g. induced by cardiac motion or diaphragmatic movements) can effectively be reduced, thus significantly improving the image quality of dark-field chest radiographs.

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.

3D Printing of Tooth Impressions Based on Multi-Detector Computed Tomography Images Combined with Beam Hardening Artifact Reduction in Metal Structures

We investigated the role of metal artifact reduction by taking 3D print impressions using 3D data of Computed Tomography (CT) images based on the algorithm applied. We manufactured a phantom of a human mandible tooth made of gypsum and nickel alloy to measure the metal artifacts. CT images were obtained by changing the phantom tube voltage and tube current. The signal intensity of the image generated by the metal artifacts before and after the iterative metal artifact reduction algorithm (iMAR) was measured. A 3D printing process was performed after converting the images, before and after iMAR application, into STL files using InVesalius version 3.1.1 by selecting the conditions that minimized the effect of the artifact. Regarding metal artifacts, the Hounsfield unit (HU) value showed low as the tube voltage increased. The iMAR-applied images acquired under the same conditions showed a significantly lower HU. The artifacts, in the form of flashes, persisted in the 3D-printed product of the image not subjected to iMAR, but were largely removed in the 3D-printed product following iMAR application. In this study, the application of iMAR and data acquired using high tube voltage eliminated a significant portion of the metal artifacts, resulting in an impression shape that was consistent with the human body.

Utility of an automatic adaptive iterative metal artifact reduction AiMAR algorithm in improving CT imaging of patients with hip prostheses evaluated for suspected bladder malignancy.

To compare the utility of a novel metal artifact reduction algorithm to standard imaging in improving visualization of key structures, diagnostic confidence, and patient-level confidence in malignancy in patients with suspected bladder cancer. Patients with hip implants undergoing CT urography for suspected bladder malignancy were enrolled. Images were reconstructed using 3 methods: (1) Filtered Back Projection (FBP), (2) Iterative Metal Artifact Reduction (iMAR), and (3) Adaptive Iterative Metal Artifact Reduction (AiMAR) strength 4. In multiple reading sessions, three radiologists graded visualization of critical anatomic structures and artifact severity (6-point scales, lower scores desirable), and diagnostic confidence in blinded fashion. They also graded patient-level confidence in malignancy based on imaging findings in each patient. Thirty-two patients (8 females) with a mean age of 74.5 ± 8.5years were included. The median (range) visualization scores for FBP, iMAR, and AiMAR were 3.6 (1.1-4.9), 1.6 (0.3-2.8), and 1.6 (0.3-2.6), respectively. Both iMAR and AiMAR had anatomicvisualization and artifact scores better than FBP (P < 0.001 for both) and similar to each other (P > 0.05). Structures with the most improvement in visualization score with the use of metal artifact reduction algorithms included theobturator internus muscle, internal and external iliac nodal chains, and vagina. iMAR and AiMAR improved diagnostic confidence (P < 0.001) and patient-level confidence in malignancy (P ≤ 0.24). For patients with hip prostheses and suspected bladder malignancy, the use of iMAR or AiMAR was shown to significantly reduce metal artifacts, thus improving diagnostic confidence and patient-level confidence in malignancy.