Evaluation of a Metal Artifact Reduction Algorithm for Image Reconstruction on a Novel CBCT Platform.

PURPOSE: The presence of metal implants can produce artifacts and distort Hounsfield units (HU) in patient computed tomography (CT) images. The purpose of this work was to characterize a novel metal artifact reduction (MAR) algorithm for reconstruction of CBCT images obtained by the HyperSight imaging system. METHODS: Three tissue-equivalent phantoms were fitted with materials commonly used in medical applications. The first consisted of a variety of metal samples centered within a solid water block, the second was an Advanced Electron Density phantom with metal rods, and the third consisted of hip prostheses positioned within a water tank. CBCT images of all phantoms were acquired and reconstructed using the MAR and iCBCT Acuros algorithms on the HyperSight system. The signal-to-noise ratio (SNR), artifact index (AI), structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), and mean-square error (MSE) were computed to assess the image quality in comparison to artifact-free reference images. The mean HU at various VOI positions around the cavity was calculated to evaluate the artifact dependence on distance and angle from the center of the cavity. The artifact volume of the phantom (excluding the cavity) was estimated by summing the volume of all voxels with HU values outside the 5th and 95th percentiles of the phantom CBCT with no artifact. RESULTS: The SNR, AI, SSIM, PSNR, and MSE metrics demonstrated significantly higher similarity to baseline when using MAR compared to iCBCT Acuros for all high-density materials, except for aluminum. Mean HU returned to expected solid water background at a shorter distance from metal sample in the MAR images, and the standard deviation remained lower for the MAR images at all distances from the insert. The artifact volume decreased using the novel MAR algorithm for all metal samples excluding aluminum (p < 0.001) and all hip prostheses (p < 0.05). CONCLUSION: Varian's HyperSight MAR reconstruction algorithm shows a reduction in metal artifact metrics, motivating the use of MAR reconstruction for patients with metal implants.

Magnetic Resonance Imaging of Total Ankle Arthroplasty: State-of-The-Art Assessment of Implant-Related Pain and Dysfunction.

Total ankle arthroplasty (TAA) is an effective alternative for treating patients with end-stage ankle degeneration, improving mobility, and providing pain relief. Implant survivorship is constantly improving; however, complications occur. Many causes of pain and dysfunction after total ankle arthroplasty can be diagnosed accurately with clinical examination, laboratory, radiography, and computer tomography. However, when there are no or inconclusive imaging findings, magnetic resonance imaging (MRI) is highly accurate in identifying and characterizing bone resorption, osteolysis, infection, osseous stress reactions, nondisplaced fractures, polyethylene damage, nerve injuries and neuropathies, as well as tendon and ligament tears. Multiple vendors offer effective, clinically available MRI techniques for metal artifact reduction MRI of total ankle arthroplasty. This article reviews the MRI appearances of common TAA implant systems, clinically available techniques and protocols for metal artifact reduction MRI of TAA implants, and the MRI appearances of a broad spectrum of TAA-related complications.

Effect of the cone-beam CT acquisition trajectory on image quality in spine surgery: experimental cadaver study.

BACKGROUND: Intraoperative 3D imaging with cone-beam CT (CBCT) improves assessment of implant position and reduces complications in spine surgery. It is also used for image-guided surgical techniques, resulting in improved quality of care. However, in some cases, metal artifacts can reduce image quality and make it difficult to assess pedicle screw position and reduction. PURPOSE: The objective of this study was to investigate whether a change in CBCT acquisition trajectory in relation to pedicle screw position during dorsal instrumentation can reduce metal artifacts and consequently improve image quality and clinical assessability. STUDY DESIGN: Experimental cadaver study. METHODS: A human cadaver was instrumented with pedicle screws in the thoracic and lumbar spine region (Th11 to L5). Then, the acquisition trajectory of the CBCT (Cios Spin, Siemens, Germany) to the pedicle screws was systematically changed in 5° steps in angulation (-30° to +30°) and swivel (-25° to +25°). Subsequently, radiological evaluation was performed by 3 blinded, qualified raters on image quality using 9 questions (including anatomical structures, implant position, appearance of artifacts) with a score (1-5 points). For statistical evaluation, the image quality of the different acquisition trajectories was compared to the standard acquisition trajectory and checked for significant differences. RESULTS: The angulated acquisition trajectory significantly increased the score for subjective image quality (p<.001) as well as the clinical assessability of pedicle screw position (p<.001) with particularly strong effects on subjective image quality in the vertebral pedicle region (d=1.61). Swivel of the acquisition trajectory significantly improved all queried domains of subjective image quality (p<.001) as well as clinical assessability of pedicle screw position (p<.001). CONCLUSIONS: In this cadaver study, the angulation as well as the swivel of the acquisition trajectory led to a significantly improved image quality in intraoperative 3D imaging (CBCT) with a constant isocenter. The data show that maximizing the angulation/swivel angle towards 30°/25° provides the best tested subjective image quality and enhances clinical assessability. Therefore, a correct adjustment of the acquisition trajectory can help to make intraoperative revision decisions more reliably. CLINICAL SIGNIFICANCE: The knowledge of enhanced image quality by changing the acquisition trajectory in intraoperative 3D imaging can be used for the assessment of critical screw positions in spine surgery. The implementation of this knowledge requires only a minor change of the current intraoperative imaging workflow without additional technical equipment and could further reduce the need for revision surgery.

Characterization of Cochlear Implant Artifact and Removal Based on Multi-Channel Wiener Filter in Unilateral Child Patients.

Cochlear implants (CI) allow deaf patients to improve language perception and improving their emotional valence assessment. Electroencephalographic (EEG) measures were employed so far to improve CI programming reliability and to evaluate listening effort in auditory tasks, which are particularly useful in conditions when subjective evaluations are scarcely appliable or reliable. Unfortunately, the presence of CI on the scalp introduces an electrical artifact coupled to EEG signals that masks physiological features recorded by electrodes close to the site of implant. Currently, methods for CI artifact removal have been developed for very specific EEG montages or protocols, while others require many scalp electrodes. In this study, we propose a method based on the Multi-channel Wiener filter (MWF) to overcome those shortcomings. Nine children with unilateral CI and nine age-matched normal hearing children (control) participated in the study. EEG data were acquired on a relatively low number of electrodes (n = 16) during resting condition and during an auditory task. The obtained results obtained allowed to characterize CI artifact on the affected electrode and to significantly reduce, if not remove it through MWF filtering. Moreover, the results indicate, by comparing the two sample populations, that the EEG data loss is minimal in CI users after filtering, and that data maintain EEG physiological characteristics.

Efficacy of the CALM® Algorithm in Reducing Motion-Induced Artifacts in CBCT Imaging: A Fractal Dimension Analysis of Trabecular Bone.

OBJECTIVE: The primary goal of this investigation was to ascertain the efficacy of the CALM® motion artifact reduction algorithm in diminishing motion-induced blurriness in Cone Beam Computed Tomography [CBCT] images. The assessment was conducted through Fractal Dimension [FD] analysis of the trabecular bone. METHODS AND MATERIALS: A desiccated human mandible was subjected to Planmeca ProMax 3D® scanning under eight distinct protocols, marked by variations in motion presence [at 5, 10, and 15 degrees] and the deployment of CALM®. In every scan, five distinct regions of interest [ROIs] were designated for FD analysis, meticulously avoiding tooth roots or cortical bone. The FD was computed employing the box-counting method with Image-J 1.53 software. RESULTS: Our findings reveal that a 5-degree motion does not significantly disrupt FD analysis, while a 10-degree motion and beyond exhibit statistical differences and volatility among the sites and groups. A decreased FD value, signifying a less intricate or "rough" bone structure, correlated with amplified motion blurriness. The utilization of CALM® software seemed to counteract this effect in some instances, reconciling FD values to those akin to the control groups. Nonetheless, CALM®'s efficacy differed across sites and motion degrees. Interestingly, at one site, CALM® application in the absence of motion resulted in FD values considerably higher than all other groups. CONCLUSION: The study indicates that motion, particularly at 10 degrees or more, can considerably impact the FD analysis of trabecular bone in CBCT images. In some situations, the CALM® motion artifact reduction algorithm can alleviate this impact, though its effectiveness fluctuates depending on the site and degree of motion. This underscores the necessity of factoring in motion and the employment of artifact reduction algorithms during the interpretation of FD analysis outcomes in CBCT imaging. More research is necessary to refine the application of such algorithms and to comprehend their influence on different sites under varying motion degrees.

A Comparison and Evaluation of Two Commercially Available Metal Artifact Reduction Applications.

Objective: The angiography systems A (A) and B (B), both incorporated at our hospital, are equipped with metal artifact reduction (MAR) applications. In clinical practice, it is crucial to understand the characteristics of MAR in both systems given that endovascular treatments are occasionally administered with both. In this study, we compared the artifact reduction effects of MAR on equipment A and B and clarified the differences between the two systems. Methods: An artifact evaluation phantom was created using a cylindrical water phantom and an iodine contrast medium. The phantom was imaged, MAR processing was performed on the obtained images, and an isotropic quantitative evaluation of artifacts was performed by extreme value statistical analysis using the Gumbel distribution. Results: The MAR reduction effects were approximately 45% and 40% for equipment A and B at concentrations of 8300 and 6000, respectively. The MAR reduction effect in both devices exhibited different trends depending on the concentration. Conclusion: In clinical procedures that make use of absorbents in medium concentrations of approximately 3000-5000, such as n-butyl-2-cyanoacrylate and Onyx, it is necessary to understand the MAR characteristics of both devices and consider the use of alternative devices as an option.

Dual-stage feedback network for lightweight color image compression artifact reduction.

Lossy image coding techniques usually result in various undesirable compression artifacts. Recently, deep convolutional neural networks have seen encouraging advances in compression artifact reduction. However, most of them focus on the restoration of the luma channel without considering the chroma components. Besides, most deep convolutional neural networks are hard to deploy in practical applications because of their high model complexity. In this article, we propose a dual-stage feedback network (DSFN) for lightweight color image compression artifact reduction. Specifically, we propose a novel curriculum learning strategy to drive a DSFN to reduce color image compression artifacts in a luma-to-RGB manner. In the first stage, the DSFN is dedicated to reconstructing the luma channel, whose high-level features containing rich structural information are then rerouted to the second stage by a feedback connection to guide the RGB image restoration. Furthermore, we present a novel enhanced feedback block for efficient high-level feature extraction, in which an adaptive iterative self-refinement module is carefully designed to refine the low-level features progressively, and an enhanced separable convolution is advanced to exploit multiscale image information fully. Extensive experiments show the notable advantage of our DSFN over several state-of-the-art methods in both quantitative indices and visual effects with lower model complexity.

Effect of auto-adaptive metal artifact reduction (aMAR) program in cone-beam computed tomography on assessing pre-implant bone levels.

This research aimed to introduce an auto-adaptive metal artifact reduction (aMAR) algorithm in cone-beam computed tomography (CBCT) to assess the levels of the pre-implant alveolar crest. Dental implants as a treatment modality for edentulous patients consist of a titanium alloy, which creates a metal artifact, resulting in a dark dental structure in the CBCT scans. Metallic artifacts are limiting factors for the precise detection in CBCT images. These are related to the dark areas around materials and metallic structures (e.g., restorations, implants, and endodontic instruments). To overcome this problem, the metal artifact reduction (MAR) program has been recommended as a post-procedure stage for CBCT image reconstruction. Recent developments offer CBCT scanners with an aMAR option with a greater dynamic range to help overcome the challenges of peri-implant bone evaluation to reach accurate dental diagnoses.

Angle Dependence of Electrode Lead-Related Artifacts in Single- and Dual-Energy Cardiac ECG-Gated CT Scanning-A Phantom Study.

Background: The electrodes of implantable cardiac devices (ICDs) may cause significant problems in cardiac computed tomography (CT) because they are a source of artifacts that obscure surrounding structures and possible pathology. There are a few million patients currently with ICDs, and some of these patients will require cardiac imaging due to coronary artery disease or problems with ICDs. Modern CT scanners can reduce some of the metal artifacts because of MAR software, but in some vendors, it does not work with ECG gating. Introduced in 2008, dual-energy CT scanners can generate virtual monoenergetic images (VMIs), which are much less susceptible to metal artifacts than standard CT images. Objective: This study aimed to evaluate if dual-energy CT can reduce metal artifacts caused by ICD leads by using VMIs. The second objective was to determine how the angle between the electrode and the plane of imaging affects the severity of the artifacts in three planes of imaging. Methods: A 3D-printed model was constructed to obtain a 0-90-degree field at 5-degree intervals between the electrode and each of the planes: axial, coronal, and sagittal. This electrode was scanned in dual-energy and single-energy protocols. VMIs with an energy of 40-140 keV with 10 keV intervals were reconstructed. The length of the two most extended artifacts originating from the tip of the electrode and 2 cm above it-at the point where the thick metallic defibrillating portion of the electrode begins-was measured. Results: For the sagittal plane, these observations were similar for both points of the ICDs that were used as the reference location. VMIs with an energy over 80 keV produce images with fewer artifacts than similar images obtained in the single-energy scanning mode. Conclusions: Virtual monoenergetic imaging techniques may reduce streak artifacts arising from ICD electrodes and improve the quality of the image. Increasing the angle of the electrode as well as the imaging plane can reduce artifacts. The angle between the electrode and the beam of X-rays can be increased by tilting the gantry of the scanner or lifting the upper body of the patient.

[A deep blur learning-based motion artifact reduction algorithm for dental cone-beam computed tomography images].

OBJECTIVE: We propose a motion artifact correction algorithm (DMBL) for reducing motion artifacts in reconstructed dental cone-beam computed tomography (CBCT) images based on deep blur learning. METHODS: A blur encoder was used to extract motion-related degradation features to model the degradation process caused by motion, and the obtained motion degradation features were imported in the artifact correction module for artifact removal. The artifact correction module adopts a joint learning framework for image blur removal and image blur simulation for treatment of spatially varying and random motion patterns. Comparative experiments were conducted to verify the effectiveness of the proposed method using both simulated motion data sets and clinical data sets. RESULTS: The experimental results with the simulated dataset showed that compared with the existing methods, the PSNR of the proposed method increased by 2.88%, the SSIM increased by 0.89%, and the RMSE decreased by 10.58%. The results with the clinical dataset showed that the proposed method achieved the highest expert level with a subjective image quality score of 4.417 (in a 5-point scale), significantly higher than those of the comparison methods. CONCLUSION: The proposed DMBL algorithm with a deep blur joint learning network structure can effectively reduce motion artifacts in dental CBCT images and achieve high-quality image restoration.

Evaluation of a prototype metal artifact reduction algorithm for cone beam CT in patients undergoing radioembolization.

Metal artifacts notoriously pose significant challenge in computed tomography (CT), leading to inaccuracies in image formation and interpretation. Artifact reduction tools have been designed to improve cone beam computed tomography (CBCT) image quality by reducing artifacts caused by certain high-density materials. Metal artifact reduction (MAR) tools are specific algorithms that are applied during image reconstruction to minimize or eliminate artifacts degrading CBCT images. The purpose of the study is to evaluate the effect of a MAR algorithm on image quality in CBCT performed for evaluating patients before transarterial radioembolization (TARE). We retrospectively included 40 consecutive patients (aged 65 ± 13 years; 23 males) who underwent 45 CBCT examinations (Allura FD 20, XperCT Roll protocol, Philips Healthcare, Best, The Netherlands) in the setting of evaluation for TARE between January 2017 and December 2018. Artifacts caused by coils, catheters, and surgical clips were scored subjectively by four readers on a 5-point scale (1 = artifacts affecting diagnostic information to 5 = no artifacts) using a side-by-side display of uncorrected and MAR-corrected images. In addition, readers scored tumor visibility and vessel discrimination. MAR-corrected images were assigned higher scores, indicating better image quality. The differences between the measurements with and without MAR were most impressive for coils with a mean improvement of 1.6 points (95%CI [1.5 1.8]) on the 5-point likert scale, followed by catheters 1.4 points (95%CI [1.3 1.5]) and clips 0.7 points (95%CI [0.3 1.1]). Improvements for other artifact sources were consistent but relatively small (below 0.25 points on average). Interrater agreement was good to perfect (Kendall's W coefficient = 0.68-0.95) and was higher for MAR-corrected images, indicating that MAR improves diagnostic accuracy. A metal artifact reduction algorithm can improve diagnostic and interventional accuracy of cone beam CT in patients undergoing radioembolization by reducing artifacts caused by diagnostic catheters and coils, lowering interference of metal artifacts with adjacent major structures, and improving tumor visibility.

Performance of different cone-beam computed tomography scan modes with and without metal artifact reduction in detection of recurrent dental caries under various restorative materials.

Purpose: We aimed to compare the diagnostic performance of different cone-beam computed tomography (CBCT) scan modes with and without the application of a metal artifact reduction (MAR) option under 5 different restorative materials. Material and methods: Our research was an in vitro study with 150 caries-free premolars and molars. The teeth were randomly divided into experimental (with artificially induced caries, n = 75) and control (without caries, n = 75) groups and were prepared based on 5 types of restorative materials, including conventional composites (Filtek Z250, Gradia), flow composite, glass ionomer, and amalgam. The teeth were examined under 2 CBCT scan modes (high-resolution [HIRes] and standard) with and without MAR application. Finally, the diagnostic accuracy index values (area under the receiver operating characteristic curve [AUC], sensitivity, and specificity) were calculated. Results: The AUC of standard scan mode with the MAR option was significantly lower than that of HIRes with MAR (p = 0.018) and without MAR option (p = 0.011) in detecting recurrent caries. Also, without MAR option, the diagnostic accuracy (AUC) of the standard mode was significantly lower than that of the HIRes (p = 0.020). Similar findings were observed for sensitivity and specificity. Moreover, diagnostic performance of standard and HIRes scan modes with and without MAR in the amalgam group was lower than that in other restorative material groups. Conclusions: Diagnostic performance of HIRes CBCT mode was higher than that of standard mode for recurrent caries and remained unaffected by MAR application. However, the accuracy in detecting recurrent caries was lower in the amalgam group compared with other restorative material groups.

Evaluation of video-based rPPG in challenging environments: Artifact mitigation and network resilience.

Video-based remote photoplethysmography (rPPG) has emerged as a promising technology for non-contact vital sign monitoring, especially under controlled conditions. However, the accurate measurement of vital signs in real-world scenarios faces several challenges, including artifacts induced by videocodecs, low-light noise, degradation, low dynamic range, occlusions, and hardware and network constraints. In this article, a systematic and comprehensive investigation of these issues is conducted, measuring their detrimental effects on the quality of rPPG measurements. Additionally, practical strategies are proposed for mitigating these challenges to improve the dependability and resilience of video-based rPPG systems. Methods for effective biosignal recovery in the presence of network limitations are detailed, along with denoising and inpainting techniques aimed at preserving video frame integrity. Compared to previous studies, this paper addresses a broader range of variables and demonstrates improved accuracy across various rPPG methods, emphasizing generalizability for practical applications in diverse scenarios with varying data quality. Extensive evaluations and direct comparisons demonstrate the effectiveness of these approaches in enhancing rPPG measurements under challenging environments, contributing to the development of more reliable and effective remote vital sign monitoring technologies.

PixelPrint: Generating Patient-Specific Phantoms for Spectral CT using Dual Filament 3D Printing.

In recent years, the importance of spectral CT scanners in clinical settings has significantly increased, necessitating the development of phantoms with spectral capabilities. This study introduces a dual-filament 3D printing technique for the fabrication of multi-material phantoms suitable for spectral CT, focusing particularly on creating realistic phantoms with orthopedic implants to mimic metal artifacts. Previously, we developed PixelPrint for creating patient-specific lung phantoms that accurately replicate lung properties through precise attenuation profiles and textures. This research extends PixelPrint's utility by incorporating a dual-filament printing approach, which merges materials such as calcium-doped Polylactic Acid (PLA) and metal-doped PLA, to emulate both soft tissue and bone, as well as orthopedic implants. The PixelPrint dual-filament technique utilizes an interleaved approach for material usage, whereby alternating lines of calcium-doped and metal-doped PLA are laid down. The development of specialized filament extruders and deposition mechanisms in this study allows for controlled layering of materials. The effectiveness of this technique was evaluated using various phantom types, including one with a dual filament orthopedic implant and another based on a human knee CT scan with a medical implant. Spectral CT scanner results demonstrated a high degree of similarity between the phantoms and the original patient scans in terms of texture, density, and the creation of realistic metal artifacts. The PixelPrint technology's ability to produce multi-material, lifelike phantoms present new opportunities for evaluating and developing metal artifact reduction (MAR) algorithms and strategies.

The Impact of AI on Metal Artifacts in CBCT Oral Cavity Imaging.

OBJECTIVE: This study aimed to assess the impact of artificial intelligence (AI)-driven noise reduction algorithms on metal artifacts and image quality parameters in cone-beam computed tomography (CBCT) images of the oral cavity. MATERIALS AND METHODS: This retrospective study included 70 patients, 61 of whom were analyzed after excluding those with severe motion artifacts. CBCT scans, performed using a Hyperion X9 PRO 13 × 10 CBCT machine, included images with dental implants, amalgam fillings, orthodontic appliances, root canal fillings, and crowns. Images were processed with the ClariCT.AI deep learning model (DLM) for noise reduction. Objective image quality was assessed using metrics such as the differentiation between voxel values (ΔVVs), the artifact index (AIx), and the contrast-to-noise ratio (CNR). Subjective assessments were performed by two experienced readers, who rated overall image quality and artifact intensity on predefined scales. RESULTS: Compared with native images, DLM reconstructions significantly reduced the AIx and increased the CNR (p < 0.001), indicating improved image clarity and artifact reduction. Subjective assessments also favored DLM images, with higher ratings for overall image quality and lower artifact intensity (p < 0.001). However, the ΔVV values were similar between the native and DLM images, indicating that while the DLM reduced noise, it maintained the overall density distribution. Orthodontic appliances produced the most pronounced artifacts, while implants generated the least. CONCLUSIONS: AI-based noise reduction using ClariCT.AI significantly enhances CBCT image quality by reducing noise and metal artifacts, thereby improving diagnostic accuracy and treatment planning. Further research with larger, multicenter cohorts is recommended to validate these findings.

Photon-Counting Computed Tomography: Experience in Musculoskeletal Imaging.

Since the emergence of the first photon-counting computed tomography (PCCT) system in late 2021, its advantages and a wide range of applications in all fields of radiology have been demonstrated. Compared to standard energy-integrating detector-CT, PCCT allows for superior geometric dose efficiency in every examination. While this aspect by itself is groundbreaking, the advantages do not stop there. PCCT facilitates an unprecedented combination of ultra-high-resolution imaging without dose penalty or field-of-view restrictions, detector-based elimination of electronic noise, and ubiquitous multi-energy spectral information. Considering the high demands of orthopedic imaging for the visualization of minuscule details while simultaneously covering large portions of skeletal and soft tissue anatomy, no subspecialty may benefit more from this novel detector technology than musculoskeletal radiology. Deeply rooted in experimental and clinical research, this review article aims to provide an introduction to the cosmos of PCCT, explain its technical basics, and highlight the most promising applications for patient care, while also mentioning current limitations that need to be overcome.

[Effects of Subject Position on Metal Artifact Reduction of a Reverse Shoulder Prosthesis Using Computed Tomography].

PURPOSE: To validate the effects of subject position on single energy metal artifact reduction (SEMAR) of a reverse shoulder prosthesis using computed tomography (CT). METHODS: A water phantom with a reverse shoulder prosthesis was scanned at four positions on the XY plane of the CT gantry (on-center, 50 mm, 100 mm, and 150 mm from on-center in the negative direction of the X axis, respectively). We obtained images with and without SEMAR. The artifact index (AI) was measured via physical assessment. Scheffé's (Ura) paired comparison methods were performed with the amount of metal artifact by ten radiological technologists via visual assessment. RESULTS: The AI was significantly reduced when using SEMAR. As the phantom moved away from the on-center position, the AI increased, and metal artifacts increased in Scheffé's methods. CONCLUSION: SEMAR reduces metal artifacts of a reverse shoulder prosthesis, but metal artifacts may increase as the subject position moves away from the on-center position.

Dual-energy CT in musculoskeletal imaging: technical considerations and clinical applications.

Dual-energy CT stands out as a robust and innovative imaging modality, which has shown impressive advancements and increasing applications in musculoskeletal imaging. It allows to obtain detailed images with novel insights that were once the exclusive prerogative of magnetic resonance imaging. Attenuation data obtained by using different energy spectra enable to provide unique information about tissue characterization in addition to the well-established strengths of CT in the evaluation of bony structures. To understand clearly the potential of this imaging modality, radiologists must be aware of the technical complexity of this imaging tool, the different ways to acquire images and the several algorithms that can be applied in daily clinical practice and for research. Concerning musculoskeletal imaging, dual-energy CT has gained more and more space for evaluating crystal arthropathy, bone marrow edema, and soft tissue structures, including tendons and ligaments. This article aims to analyze and discuss the role of dual-energy CT in musculoskeletal imaging, exploring technical aspects, applications and clinical implications and possible perspectives of this technique.

Clinical evaluation of isotropic MAVRIC-SL for symptomatic hip arthroplasties at 3 T MRI.

BACKGROUND: 3D multi-spectral imaging (MSI) of metal implants necessitates relatively long scan times. OBJECTIVE: We implemented a fast isotropic 3D MSI technique at 3 T and compared its image quality and clinical utility to non-isotropic MSI in the evaluation of hip implants. METHODS: Two musculoskeletal radiologists scored images from coronal proton density-weighted conventional MAVRIC-SL and an isotropic MAVRIC-SL sequence accelerated with robust-component-analysis on a 3-point scale (3: diagnostic, 2: moderately diagnostic, 1: non-diagnostic) for overall image quality, metal artifact, and visualization around femoral and acetabular components. Grades were compared using a signed Wilcoxon test. Images were evaluated for effusion, synovitis, osteolysis, loosening, pseudotumor, fracture, and gluteal tendon abnormalities. Reformatted axial and sagittal images for both sequences were subsequently generated and compared for image quality with the Wilcoxon test. Whether these reformats increased diagnostic confidence or revealed additional pathology, including findings unrelated to arthroplasty that may contribute to hip pain, was also compared using the McNemar test. Inter-rater agreement was measured by Cohen's kappa. RESULTS: 39 symptomatic patients with a total of 59 hip prostheses were imaged (mean age, 70 years ±9, 14 males, 25 females). Comparison scores between coronal images showed no significant difference in image quality, metal artifact, or visualization of the femur and acetabulum. Except for loosening, reviewers identified more positive cases of pathology on the original coronally-acquired isotropic sequence. In comparison of reformatted axial and sagittal images, the isotropic sequence scored significantly (p < 0.01) higher for overall image quality (3.0 vs 2.0) and produced significantly (p < 0.01) more cases of increased diagnostic confidence (42.4% vs 7.6%) or additional diagnoses (50.8% vs 22.9%). Inter-rater agreement was substantial (k = 0.798) for image quality. Mean scan times were 4.2 mins (isotropic) and 7.1 mins (non-isotropic). CONCLUSION: Compared to the non-isotropic sequence, isotropic 3D MSI was acquired in less time while maintaining diagnostically acceptable image quality. It identified more pathology, including postoperative complications and potential pain-generating pathology unrelated to arthroplasty. This fast isotropic 3D MSI sequence demonstrates promise for improving diagnostic evaluation of symptomatic hip prostheses at 3 T while simultaneously reducing scan time.

b-MAR: bidirectional artifact representations learning framework for metal artifact reduction in dental CBCT.

Objective.Metal artifacts in computed tomography (CT) images hinder diagnosis and treatment significantly. Specifically, dental cone-beam computed tomography (Dental CBCT) images are seriously contaminated by metal artifacts due to the widespread use of low tube voltages and the presence of various high-attenuation materials in dental structures. Existing supervised metal artifact reduction (MAR) methods mainly learn the mapping of artifact-affected images to clean images, while ignoring the modeling of the metal artifact generation process. Therefore, we propose the bidirectional artifact representations learning framework to adaptively encode metal artifacts caused by various dental implants and model the generation and elimination of metal artifacts, thereby improving MAR performance.Approach.Specifically, we introduce an efficient artifact encoder to extract multi-scale representations of metal artifacts from artifact-affected images. These extracted metal artifact representations are then bidirectionally embedded into both the metal artifact generator and the metal artifact eliminator, which can simultaneously improve the performance of artifact removal and artifact generation. The artifact eliminator learns artifact removal in a supervised manner, while the artifact generator learns artifact generation in an adversarial manner. To further improve the performance of the bidirectional task networks, we propose artifact consistency loss to align the consistency of images generated by the eliminator and the generator with or without embedding artifact representations.Main results.To validate the effectiveness of our algorithm, experiments are conducted on simulated and clinical datasets containing various dental metal morphologies. Quantitative metrics are calculated to evaluate the results of the simulation tests, which demonstrate b-MAR improvements of >1.4131 dB in PSNR, >0.3473 HU decrements in RMSE, and >0.0025 promotion in structural similarity index measurement over the current state-of-the-art MAR methods. All results indicate that the proposed b-MAR method can remove artifacts caused by various metal morphologies and restore the structural integrity of dental tissues effectively.Significance.The proposed b-MAR method strengthens the joint learning of the artifact removal process and the artifact generation process by bidirectionally embedding artifact representations, thereby improving the model's artifact removal performance. Compared with other comparison methods, b-MAR can robustly and effectively correct metal artifacts in dental CBCT images caused by different dental metals.