Metal Artifact Research Articles

Virtual Implant Treatment Planning Using the Existing Denture With Metal Frameworks as a Radiographic Guide: A Technique Note.

A radiographic guide can provide adequate visualization of proposed tooth position and prosthetic space, which is essential for the guided implant surgery. Many existing methods for fabricating radiographic guides involve duplicating the patient's existing denture or fabricating a new diagnostic template especially when the existing denture has metal framework, which may lead to strong metallic artifacts. This article describes a convenient and cost-effective technique for fabricating radiographic guide and aiding the design of implant surgical guide by using an existing denture with a metal framework and radiopaque and flowable composite resin in a single visit.

Optimization method of 3D reconstruction of metal cultural relics based on 3D laser scanning data reduction

AbstractThe extraction effect and feature matching are poor in the three‐dimensional reconstruction of metal cultural relics, which leads to a poor reconstruction effect. Therefore, an optimization method of three‐dimensional reconstruction of metal cultural relics based on 3D laser scanning data reduction is proposed. The overall technical design route of the method is shown as follows. Based on this model, the 3D laser scanning method is used to collect 3D images of metal artefacts, combined with colour space and 2D entropy detection methods to pre‐process 3D images, and feature matching of point clouds is carried out to extract and optimize the significant value of superpixels, and a 3D reconstructed visual model is constructed. Affine transformation is used to obtain the affine invariant moment of the structural light parameters of the visual feature lines of metal cultural relics. The light stability adjustment of the three‐dimensional reconstruction of metal cultural relics is realized by using the linear structural light adjustment method in the HSV colour space. The rigid and non‐rigid registration methods are introduced to match the point cloud. The product quantization algorithm is used to linearize the error function, and the block feature detection and matching model of spatial image is obtained. The noise number is judged by the threshold value, and the three‐dimensional reconstruction technology is combined to realize the three‐dimensional reconstruction of metal relics. The simulation results show that this method has good visual expression ability, high feature recognition rate, and improves the three‐dimensional reconstruction ability of metal relics.

Isotopic metal compositions of Viking and medieval tin artifacts from Poland reveal expansive trade network

The lack of historically known tin deposits in Poland requires that the source of tin metal in ancient artifacts must be derived from foreign sources. To identify these external sources in the Viking Period, a combination of Pb and Sn isotope compositions and trace element analyses on a group of tin and tin rich alloys from three settlements in Poland was conducted. The integration of the chemical techniques reveals several sources for the tin rich artifacts, where group: 1) possesses Pb isotope values that overlap the Europe array which possess the highest (+1.6 ‰) and lowest (−1.1 ‰) Sn isotope values coupled with elevated In concentrations 2) has Pb isotope values that overlap the Slovakian array which possess lower Sn isotope values (+0.1 ‰ to + 0.3 ‰) coupled with low In and Te concentrations 3) has a Pb isotope value that is radiogenic which possesses a high Sn isotope value (+1.1 ‰). Group 1 artifacts are split into two sources designated by the higher Sn isotope values from Cornwall and the lower Sn isotope values from Brittany and are found in the two coastal settlements. Group two artifacts match a Slovakian origin, while one artifact labeled in group three possesses an Anatolian source. Defining the tin rich artifact sources allows constraint of the tin sources for the mixed alloys like bronze and pewter. In this instance the tin isotope values fall within two sources defined by the tin metal artifacts, Brittany and Cornwall. The chemical approach presented here defines distal metal sources to reveal a geographically expansive interconnected tin trade network that was predominantly European, in Viking times through chemical analysis of ornaments, coins, and beads.

Connectivity Between Northern Iberia and Western France (2900–1100 cal bc): The Flux of Metalwork in the Bay of Biscay Modelled by Multivariate Clustering

Connections between northern Iberia and western France around the Bay of Biscay during the Chalcolithic, Early Bronze Age, and Middle Bronze Age are addressed in this article through a multivariate cluster analysis of a dataset of 1273 metal finds, comprising 4554 metal artefacts grouped into five multiregional clusters with distinctive distributions, chronologies, content, and contexts. Changes in distribution and chronology show that metalwork from faraway regions was deposited in similar ways, reflecting changing patterns of interregional connectivity. Changes in context and content suggest social transformations. The clustering method known as Latent Class Analysis is presented here in the hope that it will be applicable to other datasets elsewhere in the world.

Ultra-high resolution photon-counting CT with tin prefiltration for bone-metal interface visualization

PurposeTo investigate the metal artifact suppression potential of combining tin prefiltration and virtual monoenergetic imaging (VMI) for osseous microarchitecture depiction in ultra-high-resolution (UHR) photon-counting CT (PCCT) of the lower extremity. MethodDerived from tin-filtered UHR scans at 140 kVp, polychromatic datasets (T3D) and VMI reconstructions at 70, 110, 150, and 190 keV were compared in 117 patients with lower extremity metal implants (53 female; 62.1 ± 18.0 years). Three implant groups were investigated (total arthroplasty [n = 48], osteosynthetic material [n = 43], and external fixation [n = 26]). Image quality was assessed with regions of interest placed in the most pronounced artifacts and adjacent soft tissue, measuring the respective attenuation. Additionally, artifact extent, bone-metal interface interpretability and overall image quality were independently evaluated by three radiologists. ResultsArtifact reduction was superior with increasing keV level of VMI. While T3D was superior to VMI70keV (p ≥ 0.117), artifacts were more severe in T3D than in VMI ≥ 110 keV (all p ≤ 0.036). Image noise was highest for VMI70keV (all p < 0.001) and lowest for VMI110keV with comparable results for VMI110keV – VMI190keV. Subjective image quality regarding artifacts was superior for VMI ≥ 110 keV (all p ≤ 0.042) and comparable for VMI110keV – VMI190keV. Bone-metal interface interpretability was superior for VMI110keV (all p ≤ 0.001), while T3D, VMI150keV and VMI190keV were comparable. Overall image quality was deemed best for VMI110keV and VMI150keV. Interreader reliability was good in all cases (ICC ≥ 0.833). ConclusionsTin-filtered UHR-PCCT scans of the lower extremity combined with VMI reconstructions allow for efficient artifact reduction in the vicinity of bone-metal interfaces.

Unsupervised metal artifacts reduction network for CT images based on efficient transformer

In computed tomography (CT) imaging, artifacts caused by metals can seriously interfere with the accurate judgment, evaluation, and analysis of object structures. Deep learning presented new opportunities for minimizing metal artifacts. The currently proposed supervised methods mainly resort to simulated data for network training, which is difficult to be applied in real scenarios. Unsupervised methods do not completely suppress artifacts and are prone to instability in the corrected results. Therefore, this paper proposes an unsupervised CycleGAN model based on the efficient Transformer for metal artifact reduction in CT images. The encoder and decoder based on the efficient Transformer achieve the extraction and processing of global image content features and artifact features. By correlating global features through attention mechanisms, vital features are generated to enhance the consistency of recovery information. The efficient Transformer framework reduces the computational complexity of the attention mechanism, allowing efficient processing of standard medical CT images with 512 × 512 pixels. Projection preserving loss uses the metal trace outer projection as a fidelity constraint to overcome the problem that unsupervised networks are difficult to train and prone to spurious information. Experimental results indicate that the proposed method outperforms the original CycleGAN and ADN networks in suppressing metal artifacts while avoiding introducing spurious structures. Specifically, the proposed method improved the PSNR of the correction results by 9.23% and reduced the NMAD by 13.21% compared to the original CycleGAN, and improved the PSNR by 5.17% and reduced the NMAD by 5.57% compared to the ADN.

Impact of different metal artifact reduction techniques in photon-counting computed tomography head and neck scans in patients with dental hardware

ObjectivesMetal artifacts remain a challenge in computed tomography. We investigated the potential of photon-counting computed tomography (PCD-CT) for metal artifact reduction using an iterative metal artifact reduction (iMAR) algorithm alone and in combination with high keV monoenergetic images (140 keV) in patients with dental hardware.Material and methodsConsecutive patients with dental implants were prospectively included in this study and received PCD-CT imaging of the craniofacial area. Four series were reconstructed (standard [PCD-CTstd], monoenergetic at 140 keV [PCD-CT140keV], iMAR corrected [PCD-CTiMAR], combination of iMAR and 140 keV monoenergetic [PCD-CTiMAR+140keV]). All reconstructions were assessed qualitatively by four radiologists (independent and blinded reading on a 5-point Likert scale [5 = excellent; no artifact]) regarding overall image quality, artifact severity, and delineation of adjacent and distant anatomy. To assess signal homogeneity and evaluate the magnitude of artifact reduction, we performed quantitative measures of coefficient of variation (CV) and a region of interest (ROI)–based relative change in artifact reduction [PCD-CT/PCD-CTstd].ResultsWe enrolled 48 patients (mean age 66.5 ± 11.2 years, 50% (n = 24) males; mean BMI 25.2 ± 4.7 kg/m2; mean CTDIvol 6.2 ± 6 mGy). We found improved overall image quality, reduced artifacts and superior delineation of both adjacent and distant anatomy for the iMAR vs. non-iMAR reconstructions (all p < 0.001). No significant effect of the different artifact reduction approaches on CV was observed (p = 0.42). The ROI-based analysis indicated the most effective artifact reduction for the iMAR reconstructions, which was significantly higher compared to PCD-CT140keV (p < 0.001).ConclusionPCD-CT offers highly effective approaches for metal artifact reduction with the potential to overcome current diagnostic challenges in patients with dental implants.Clinical relevance statementMetallic artifacts pose a significant challenge in CT imaging, potentially leading to missed findings. Our study shows that PCD-CT with iMAR post-processing reduces artifacts, improves image quality, and can possibly reveal pathologies previously obscured by artifacts, without additional dose application.Key Points• Photon-counting detector CT (PCD-CT) offers highly effective approaches for metal artifact reduction in patients with dental fillings/implants.• Iterative metal artifact reduction (iMAR) is superior to high keV monoenergetic reconstructions at 140 keV for artifact reduction and provides higher image quality.• Signal homogeneity of the reconstructed images is not affected by the different artifact reduction techniques.

Simultaneous multimaterial operando tomography of electrochemical devices.

The performance of electrochemical energy devices, such as fuel cells and batteries, is dictated by intricate physiochemical processes within. To better understand and rationally engineer these processes, we need robust operando characterization tools that detect and distinguish multiple interacting components/interfaces in high contrast. Here, we uniquely combine dual-modality tomography (simultaneous neutron and x-ray tomography) and advanced image processing (iterative reconstruction and metal artifact reduction) for high-contrast multimaterial imaging, with signal and contrast enhancements of up to 10 and 48 times, respectively, compared to conventional single-modality imaging. Targeted development and application of these methods to electrochemical devices allow us to resolve operando distributions of six interacting fuel cell components (including void space) with the highest reported pairwise contrast for simultaneous yet decoupled spatiotemporal characterization of component morphology and hydration. Such high-contrast tomography ushers in key gold standards for operando electrochemical characterization, with broader applicability to numerous multimaterial systems.

Quality improvement of images with metal artifact reduction using a noise recovery technique in computed tomography.

In metal artifact reduction (MAR) in computed tomography (CT) based on projection data inpainting, X-ray photon noise has not been considered in the inpainting process. This study aims to assess the effectiveness of a MAR technique incorporating noise recovery in such projection data regions, compared with existing MAR techniques based on projection data normalization (NMAR), including one with frequency splitting (FSNMAR). Phantoms simulating hip prostheses and dental fillings were scanned using a 64-row multi slice CT scanner. The projection data was processed by NMAR and NMAR with noise recovery (NRNMAR); the processed data was sent back to the CT system for reconstruction. For the phantoms and clinical cases with hip prostheses and dental fillings, images were reconstructed without MAR, and with NMAR, NRNMAR, and FSNMAR (incorporated in the CT system). To validate the efficacy of noise recovery, noise power spectra (NPSs) were measured from the images of the hip prosthesis phantom with and without metals. The artifact index (AI) was compared between NRNMAR and FSNMAR. The resultant NPSs of NRNMAR were very similar to those of phantom images with no metals, endorsing the efficacy of noise recovery. The NMAR images had unnatural noise textures and FSNMAR caused additional streaks. NRNMAR exhibited some significant improvements in these respects: It reduced the AI by as much as 66.2-88.6% compared to FSNMAR, except for the case of a unilateral prosthesis. In conclusion, NRNMAR, which simply adds white noise to the projection data, would be effective in improving the quality of CT images with metal artifacts reduction.

Abstract 351: Novel Quantitative Imaging Biomarkers From Acute Brain CT Using Artificial Intelligence for Predicting Neurological Prognosis in Cardiac Arrest Patients: A Pilot Study

Background: Effective use of information extracted from medical images has attracted attention for decision support in primary care of patients with cardiac arrest (CA). Hypothesis and Aims: This study aimed to predict the neurological prognosis of patients with cardiac arrest by quantitative imaging biomarkers (QIBs) extracted from acute CT brain images before admission using artificial intelligence. Methods: Eighty-six CA patients (good prognosis; Glasgow-Pittsburgh cerebral performance categories [CPC]=1 or 2 at 90 days after the CA : 32 patients, poor prognosis; CPC=3, 4, or 5 at 90 days after the CA : 54 patients) treated at three hospitals between 2017 and 2019 were retrospectively analyzed. None of the CA patients caused by trauma or brain damage were included in the database. The data were divided into training and test data, and the training data were used to select informative QIBs for classification between the good and poor neurological prognosis in this analysis. Results: Seven CA patients’ data were excluded from the study due to severe metal and bone artifacts broadly spreading on the brain image, and 1170 QIBs were extracted from whole brain and regional brain interest volumes on CT images of each patient. The QIBs were composed of first-order histogram and texture features and informative QIBs for the classification were selected by feature selection algorithms using the training dataset. The feature corresponding to the gray level having a maximum gradient in the image histogram from the brain region of interest on CT images showed the strongest significant difference between the good and poor neurological prognosis of the test data set with p=0.009 and AUC=0.775 (95% CI; 0.590-0.960). Conclusions: We have shown that QIB can be used to predict neurological prognosis in patients with CA. Because this is a pilot study for methodological development and there are limitations in sample size and outcomes, new studies on a larger number of patients are required.

Metal artifact reduction around cervical spine implant using diffusion tensor imaging at 3T: A phantom study

PurposeDiffusion MRI continues to play a key role in non-invasively assessing spinal cord integrity and pre-operative injury evaluation. However, post-operative Diffusion Tensor Imaging (DTI) acquisition of patients with metal implants results in severe geometric distortion. We propose and demonstrate a method to alleviate the technical challenges facing the acquisition of DTI on post-operative cases and longitudinal evaluation of therapeutics. Material and methodsThe described technique is based on the combination of the reduced Field-Of-View (rFOV) strategy and the phase segmented EPI, termed rFOV-PS-EPI. A custom-built phantom based on a cervical spine model with metal implants was used to collect DTI data at 3 Tesla scanner using: rFOV-PS-EPI, reduced Field-Of-View single-shot EPI (rFOV-SS-EPI), and conventional full FOV techniques including SS-EPI, PS-EPI, and readout-segmented EPI (RS-EPI). Geometric distortion, SNR, and signal void were assessed to evaluate images and compare the sequences. A two-sample t-test was performed with p-value of 0.05 or less to indicate statistical significance. ResultsThe reduced FOV techniques showed better capability to reduce distortions compared to the Full FOV techniques. The rFOV-PS-EPI method provided DTI images of the phantom at the level of the hardware whereas the conventional rFOV-SS-EPI is useful only when the metal is approximately 20 mm away. In addition, compared to the rFOV-SS-EPI technique, the suggested approach produced smaller signal voids area as well as significantly reduced geometric distortion in Circularity (p < 0.005) and Eccentricity (p < 0.005) measurements. No statistically significant differences were found for these geometric distortion measurements between the rFOV-PS-EPI DTI sequence and conventional structural T2 images (p > 0.05). ConclusionThe combination of rFOV and a phase-segmented acquisition approach is effective for reducing metal-induced distortions in DTI scan on spinal cord with metal hardware at 3 T.

Triple-energy virtual monochromatic imaging with a photon-counting detector for reducing metal artifacts in half-beam dental computed tomography

Dental computed tomography (DCT) is crucial for accurate diagnosis and treatment planning in modern dental care. However, the presence of metal artifacts from dental restorations or implants often compromises the image quality. This study introduces and evaluates a novel technique, triple-energy virtual monochromatic imaging (TEVM), which employs a photon-counting detector (PCD) to enhance image quality. TEVM synthesizes images captured at three distinct energy levels to produce a virtual monochromatic image (VMI) with improved contrast and sharpness, particularly in areas affected by metal artifacts. We compared the efficacy of TEVM in mitigating metal artifacts using half-beam dental and endodontic CT with that of conventional methods. The results indicate that TEVM significantly reduces metal artifacts and improves image quality. In addition, this technique minimizes the need for repeated scans, thereby reducing radiation exposure. In conclusion, TEVM combined with PCD offers a promising avenue for overcoming the challenges posed by metal artifacts in dental and endodontic CT, thereby enhancing both diagnostic accuracy and patient care.

An Innovative Metal Artifact Reduction Algorithm based on Res-U-Net GANs

During X-ray computed tomography (CT) scans, the metal implants in the patient's body will produce severe artifacts, which reduce the image quality and interferes with the doctor's judgment. Therefore, it is necessary to develop an algorithm for removing metal artifacts in CT images and reconstructing high-quality images. In this paper, we proposed a generative adversarial networks (GANs)-based metal artifact reduction algorithm for the image domain, Res-U-Net GANs. This method can effectively suppress noise and remove metal artifacts in CT images. Our new approach includes a generator and a discriminator. The generator contains several residual blocks, a U-Net structure and skip connections. And a weighted joint loss function is also used for training. These structures can reduce metal artifacts in images, improve image quality, and restore implant details. We use SSIM, PSNR and RMSE to evaluate the performance of the proposed method. The mean SSIM, PSNR and RMSE of the testing set images are 0.977, 39.044 and 0.011, respectively. And the trained model which is compiled and encapsulated, also show excellent performance in processing clinical data sets, which can remove metal artifacts in clinical CT images. We consider that the proposed algorithm can remove metal artifacts in CT images and restore image details, which is very helpful for radiologists.

SemiMAR: Semi-Supervised Learning for CT Metal Artifact Reduction.

Metal artifacts lead to CT imaging quality degradation. With the success of deep learning (DL) in medical imaging, a number of DL-based supervised methods have been developed for metal artifact reduction (MAR). Nonetheless, fully-supervised MAR methods based on simulated data do not perform well on clinical data due to the domain gap. Although this problem can be avoided in an unsupervised way to a certain degree, severe artifacts cannot be well suppressed in clinical practice. Recently, semi-supervised metal artifact reduction (MAR) methods have gained wide attention due to their ability in narrowing the domain gap and improving MAR performance in clinical data. However, these methods typically require large model sizes, posing challenges for optimization. To address this issue, we propose a novel semi-supervised MAR framework. In our framework, only the artifact-free parts are learned, and the artifacts are inferred by subtracting these clean parts from the metal-corrupted CT images. Our approach leverages a single generator to execute all complex transformations, thereby reducing the model's scale and preventing overlap between clean part and artifacts. To recover more tissue details, we distill the knowledge from the advanced dual-domain MAR network into our model in both image domain and latent feature space. The latent space constraint is achieved via contrastive learning. We also evaluate the impact of different generator architectures by investigating several mainstream deep learning-based MAR backbones. Our experiments demonstrate that the proposed method competes favorably with several state-of-the-art semi-supervised MAR techniques in both qualitative and quantitative aspects.

X-ray CT Metal Artifact Reduction Using Segmentation and TV Regularisation

X-ray CT Metal Artifact Reduction Using Segmentation and TV Regularisation

Artificial Intelligence-Assisted Sac Diameter Assessment for Complex Endovascular Aortic Repair.

Artificial intelligence (AI) using an automated, deep learning-based method, Augmented Radiology for Vascular Aneurysm (ARVA), has been verified as a viable aide in aneurysm morphology assessment. The aim of this study was to evaluate the accuracy of ARVA when analyzing preoperative and postoperative computed tomography angiography (CTA) in patients managed with fenestrated endovascular repair (FEVAR) for complex aortic aneurysms (cAAs). Preoperative and postoperative CTAs from 50 patients (n=100 CTAs) who underwent FEVAR for cAAs were extracted from the picture archiving and communication system (PACS) of a single aortic center equipped with ARVA. All studies underwent automated AI aneurysm morphology assessment by ARVA. Appropriate identification of the outer wall of the aorta was verified by manual review of the AI-generated overlays for each patient. Maximum outer-wall aortic diameters were measured by 2 clinicians using multiplanar reconstruction (MPR) and curved planar reformatting (CPR), and among studies where the aortic wall was appropriately identified by ARVA, they were compared with ARVA automated measurements. Identification of the outer wall of the aorta was accurate in 89% of CTA studies. Among these, diameter measurements by ARVA were comparable to clinician measurements by MPR or CPR, with a median absolute difference of 2.4 mm on the preoperative CTAs and 1.6 mm on the postoperative CTAs. Of note, no significant difference was detected between clinician measurements using MPR or CPR on preoperative and postoperative scans (range 0.5-0.9 mm). For patients with cAAs managed with FEVAR, ARVA provides accurate preoperative and postoperative assessment of aortic diameter in 89% of studies. This technology may provide an opportunity to automate cAA morphology assessment in most cases where time-intensive, manual clinician measurements are currently required. In this retrospective analysis of preoperative and postoperative imaging from 50 patients managed with FEVAR, AI provided accurate aortic diameter measurements in 89% of the CTAs reviewed, despite the complexity of the aortic anatomies, and in post-operative CTAs despite metal artifact from stent grafts, markers and embolization materials. Outliers with imprecise automated aortic overlays were easily identified by scrolling through the axial AI-generated segmentation MPR cuts of the entire aorta.This study supports the notion that such emerging AI technologies can improve efficiency of routine clinician workflows while maintaining excellent measurement accuracy when analyzing complex aortic anatomies by CTA.

Optimal combination periprosthetic vasculature visualization and metal artifact reduction by spectral computed tomography using virtual monoenergetic images in total hip arthroplasty

ObjectivesTo investigate the optimal parameters of spectral CT for preferably visualizing the periprosthetic vasculature and metal artifact reduction (MAR) in total hip arthroplasty (THA).MethodsA total of 34 THA of 30 patients were retrospectively included. Image reconstructions included conventional image (CI), CI combined with MAR (CIMAR), and virtual monoenergetic images (VMI) combined with MAR (VMIMAR) at 50–120 keV. The attenuation and standard deviation of the vessel and artifact, and the width of artifact were measured. Qualitative scoring was evaluated including the vascular contour, the extent of artifact, and overall diagnostic evaluation.ResultsThe attenuation, noise of the vessel and artifact, and the width of artifact decreased as the energy level increased (p < 0.001). The downtrend was relatively flat at 80–120 keV, and the vascular attenuation dropped to 200 HU at 90 keV. The qualitative rating of vascular contour was significantly higher at CIMAR (3.47) and VMIMAR 60–80 keV (2.82–3.65) compared with CI (2.03) (p ≤ 0.029), and the highest score occurred at 70 and 80 keV (3.65 and 3.56). The score of the extent of artifact was higher at VMIMAR 80 keV than CIMAR (3.53 VS 3.12, p = 0.003). The score of the overall diagnostic evaluation was higher at VMIMAR 70 and 80 keV (3.32 and 3.53, respectively) than CIMAR (3.12) (p ≤ 0.035).ConclusionEighty kiloelectron volts on VMIMAR, providing satisfactorily reduced metal artifacts and improved vascular visualization, can be an optimal recommended parameter of spectrum CT for the assessment of periprosthetic vasculature in THA patients.Critical relevance statementThe metal artifact is gradually reducing with increasing energy level; however, the vascular visualization is worsening. The vascular visualization is terrible above 100 keV, while the vessel is disturbed by artifacts below 70 keV. The best performance is found at 80 keV.Key points• VMIMAR can provide both reduced metal artifacts and improved vascular visualization.• Eighty kiloelectron volts on VMIMAR performs best in vascular visualization of total hip arthroplasty patients.• Energy spectrum CT is recommended for routine use in patients with total hip arthroplasty.Graphical

PDS-MAR: a fine-grained projection-domain segmentation-based metal artifact reduction method for intraoperative CBCT images with guidewires

Objective. Since the invention of modern Computed Tomography (CT) systems, metal artifacts have been a persistent problem. Due to increased scattering, amplified noise, and limited-angle projection data collection, it is more difficult to suppress metal artifacts in cone-beam CT, limiting its use in human- and robot-assisted spine surgeries where metallic guidewires and screws are commonly used. Approach. To solve this problem, we present a fine-grained projection-domain segmentation-based metal artifact reduction (MAR) method termed PDS-MAR, in which metal traces are augmented and segmented in the projection domain before being inpainted using triangular interpolation. In addition, a metal reconstruction phase is proposed to restore metal areas in the image domain. Main results. The proposed method is tested on both digital phantom data and real scanned cone-beam computed tomography (CBCT) data. It achieves much-improved quantitative results in both metal segmentation and artifact reduction in our phantom study. The results on real scanned data also show the superiority of this method. Significance. The concept of projection-domain metal segmentation would advance MAR techniques in CBCT and has the potential to push forward the use of intraoperative CBCT in human-handed and robotic-assisted minimal invasive spine surgeries.

The role of advanced metal artifact reduction MRI in the diagnosis of periprosthetic joint infection.

Identification and diagnosis of periprosthetic joint infection (PJI) are challenging, requiring a multi-disciplinary approach involving clinical evaluation, laboratory tests, and imaging studies. MRI is advantageous to alternative imaging techniques due to superior soft tissue contrast and absence of ionizing radiation. However, the presence of metallic implants can cause signal loss and artifacts. Metal artifact suppression (MARS) MRI techniques have been developed that mitigate metal artifacts and improve periprosthetic soft tissue visualization. This paper provides a review of the various MARS MRI techniques, their clinical applicability and accuracy in PJI diagnosis and evaluation, and current challenges and future perspectives.

Filtered back projection vs. iterative reconstruction for CBCT: effects on image noise and processing time.

To assess the effect of standard filtered back projection (FBP) and iterative reconstruction (IR) methods on CBCT image noise and processing time (PT), acquired with various acquisition parameters with and without metal artefact reduction (MAR). CBCT scans using the Midmark EIOS unit of a human mandible embedded in soft tissue equivalent material with and without the presence of an implant at mandibular first molar region were acquired at various acquisition settings (milliamperages [4mA-14mA], FOV [5 × 5, 6 × 8, 9 × 10 cm], and resolutions [low, standard, high] and reconstructed using standard FBP and IR, and with and without MAR. The processing time was recorded for each reconstruction. ImageJ was used to analyze specific axial images. Radial transaxial fiducial lines were created relative to the implant site. Standard deviations of the gray density values (image noise) were calculated at fixed distances on the fiducial lines on the buccal and lingual aspects at specific axial levels, and mean values for FBP and IR were compared using paired t-tests. Significance was defined as p < 0.05. The overall mean for image noise (± SD) for FBP was 198.65 ± 55.58 and 99.84 ± 16.28 for IR. IR significantly decreased image noise compared to FBP at all acquisition parameters (p < 0.05). Noise reduction among different scanning protocols ranged between 29.7% (5 × 5 cm FOV) and 58.1% (5mA). IR increased processing time by an average of 35.1 s. IR significantly reduces CBCT image noise compared to standard FBP without substantially increasing processing time.