Metal Artifact Research Articles

Iodine concentration, HU accuracy, and metal artifacts evaluation on second-generation dual-layer spectral detector CT images with metal implants: a phantom study.

Metal implants may affect the image quality, iodine concentration (IC), and CT Hounsfield unit (HU) quantification accuracy. To investigate the quantitative accuracy of IC and HU from dual-layer spectral detector (DLCT) in the presence of metal artifacts. An experimental cylindrical phantom containing eight iodine inserts and two metal inserts was designed. The phantom underwent scanning at three radiation dose levels and two tube voltage settings. A set of conventional images (CIs), virtual monoenergetic images (VMIs), and iodine concentration maps (ICMs) were generated and measured for all the eight iodine inserts. Quantitative indicators of mean absolute percentage error (MAPE), artifact index (AI), contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), and standard deviation (SD) on CIs and VMIs were calculated for IC and HU. Subjective score evaluation was also conducted. The MAPEiodine values of all regions of interest across different scanning configurations were all <5%. Almost all APEiodine values were <5%, indicating that metal artifacts had little impact on IC measurements. When the tube voltage was fixed, the SD value of attenuation decreased with the increase of the tube current; this is also true when the tube current was fixed. The middle energy reconstructions seemed to give a good balance between reducing artifacts and improving contrast. VMIs from DLCT can reduce metal artifacts, the accuracy of IC quantification is not sensitive to imaging parameters. In summary, metal implants exhibit minimal impact on image quality and IC quantification accuracy in reconstructed images from DLCT.

Metal implant segmentation in CT images based on diffusion model

BackgroundComputed tomography (CT) is widely in clinics and is affected by metal implants. Metal segmentation is crucial for metal artifact correction, and the common threshold method often fails to accurately segment metals.PurposeThis study aims to segment metal implants in CT images using a diffusion model and further validate it with clinical artifact images and phantom images of known size.MethodsA retrospective study was conducted on 100 patients who received radiation therapy without metal artifacts, and simulated artifact data were generated using publicly available mask data. The study utilized 11,280 slices for training and verification, and 2,820 slices for testing. Metal mask segmentation was performed using DiffSeg, a diffusion model incorporating conditional dynamic coding and a global frequency parser (GFParser). Conditional dynamic coding fuses the current segmentation mask and prior images at multiple scales, while GFParser helps eliminate high-frequency noise in the mask. Clinical artifact images and phantom images are also used for model validation.ResultsCompared with the ground truth, the accuracy of DiffSeg for metal segmentation of simulated data was 97.89% and that of DSC was 95.45%. The mask shape obtained by threshold segmentation covered the ground truth and DSCs were 82.92% and 84.19% for threshold segmentation based on 2500 HU and 3000 HU. Evaluation metrics and visualization results show that DiffSeg performs better than other classical deep learning networks, especially for clinical CT, artifact data, and phantom data.ConclusionDiffSeg efficiently and robustly segments metal masks in artifact data with conditional dynamic coding and GFParser. Future work will involve embedding the metal segmentation model in metal artifact reduction to improve the reduction effect.

Analysis of Three-Dimensional Tooth Movement: A Comparative Study Between Digital Dental Models and Craniofacial Models.

This study aims to validate the efficacy of using a digital dental model (DM) with reference to the palatal region of interest (PROI) for assessing orthodontic tooth movement (TM)by comparing it with the analysis of a computed tomography (CT) model with reference to the cranial region of interest (CROI). Thirty-four patients (mean age: 21 years and 11 months) with jaw deformities underwent DM and CT scans before and after presurgical orthognathic treatment. Linear and angular measurements during TM were conducted in three dimensions using both DM and CT to assess reliability. DM analysis with PROI registration exhibited high levels of reproducibility, with minimal standard errors in X, Y, and Z displacements (<0.15 mm) and 0.43 degrees in angular change. CT analysis with CROI registration demonstrates similarly high reproducibility, with standard errors inferior to DM analysis (<0.20 mm). Bland-Altman analysis indicated agreement in linear changes of each X, Y, and Z displacement between DM and CT measurements, with limits of agreement (LOA) below 0.91 mm. The results of this study suggest that the PROI, focusing on the third palatal rugae and the horizontal part of the palatal vault, serves as a reliable reference region for evaluating three-dimensional (3D) tooth movement. Digital dental models offer distinct advantages including the absence of X-ray exposure, no metal artifacts, and the ability to generate high-resolution 3D models. The methodology demonstrated high precision and reproducibility, supporting its potential clinical utility in orthodontic treatment planning and assessment.

The usefulness of contrast-enhanced subtraction magnetic resonance imaging for detecting endoleaks after endovascular aortic repair with prophylactic intraoperative sac embolization.

Metallic and hyperdense artifacts and T1-shortening substances in the abdominal aortic aneurysm (AAA) sac generated by embolic materials and lipiodol pose challenges in the identification of endoleaks on follow-up computed tomography (CT) or magnetic resonance imaging (MRI). To evaluate the usefulness of contrast-enhanced subtraction MRI (CES-MRI) for detecting endoleaks after endovascular abdominal aortic aneurysm repair (EVAR) with intraoperative AAA sac embolization compared with CE-CT, this study was conducted. In this study, 28 consecutive patients who underwent EVAR with prophylactic AAA sac embolization were included. All patients underwent CES-MRI and CE-CT to detect endoleaks. The definitive diagnosis of endoleaks was a consensus reading of CE-CT and CES-MRI by two certified radiologists, in addition to angiography or reproducible radiological findings in the observational examination. Analysis was performed to evaluate which examination was better for detecting endoleaks. The sensitivity, specificity, and area under the curve of CE-CT and CES-MRI according to observer 1 were 50%, 100%, and 0.813 (95% confidence interval [CI] = 0.625-1.00) and 100%, 95%, and 0.997 (95% CI = 0.984-1.00), respectively, and those according to observer 2 were 50%, 100%, and 0.750 (95% CI = 0.514-0.986) and 100%, 95%, and 0.969 (95% CI = 0.903-1.00), respectively. Intolerable artifacts were significantly observed on CE-CT. The severity of the artifacts did not depend on the stent graft on CT and MRI. Although no significant difference was observed, CES-MRI tended to have better accuracy for endoleak detection in EVAR with intraoperative AAA sac embolization than CE-CT.

Dual-Energy CTA Iodine Map Reconstructions Improve Visualization of Residual Cerebral Aneurysms following Endovascular Coiling.

Material-specific reconstructions of dual-energy CTA (DECTA) can highlight iodinated contrast, subtract predefined materials, and reduce metal artifact. We present a technique to improve detection of residual aneurysms after endovascular coiling by which iodine-map DECTA (IM-DECTA) reconstructions subtract platinum coil artifacts in MIP images (MIP IM-DECTA) and assess if IM-DECTA offers improved detection over conventional CTA (CCTA) or monoenergetic DECTA. We included consecutive patients who underwent endovascular aneurysm coiling with follow-up DECTA and DSA within 24 months. DECTA was performed at 80- and 150-kVp tube voltages on a rapid kV-switching single-source Revolution scanner. CCTA and IM-DECTA series were reconstructed. Reference-standard DSA was compared with CCTA, 50- and 70-keV virtual monochromatic DECTA, IM-DECTA, and MIP IM-DECTA. Blinded to DSA data, cross-section images were reviewed in consensus by 3 neurointerventionalists for residual aneurysms and assigned modified Raymond-Roy classifications (mRRC). Sensitivity, specificity, and accuracy of each series is reported relative to DSA, and single-factor ANOVA and pair-wise Spearman correlation coefficients compared the accuracy of each series. Readers provided ROI measurements of HU deviation adjacent to the aneurysm neck for quantitative noise assessment and qualitatively scored each series on a 3-point Likert-style scale ranging from uninterpretable to excellent image quality. Twenty-one patients with 25 coiled aneurysms were included. Mean time from DECTA to DSA was 286 ± 212 days. IM-DECTA and MIP IM-DECTA most sensitively (89% and 90%) and specifically (93% and 93%) detected residual aneurysms relative to CCTA (6% and 86%). Relative to DSA, IM-DECTA and MIP IM-DECTA most accurately detected (92% versus 28% for CCTA) and classified residual aneurysms by mRRC (ρC-CTA = -0.08; ρIM = 0.50; ρIM-MIP = 0.55; P < .001). Reader consensus reported the best image quality at the aneurysm neck with IM-DECTA and MIP IM-DECTA, with 56% of CCTAs considered uninterpretable versus 0% of IM-DECTAs, and image noise was significantly lower for IM-DECTA (27.9 ± 3.6 HU) or MIP IM-DECTA (26.8 ± 3.5 HU) than CCTA (103.2 ± 13.3 HU; P < .001). MIP IM-DECTA can subtract coil mass artifact and is more sensitive and specific than CCTA for the detection of residual aneurysms after endovascular coiling.

Quantification of metallic artifact on CT associated with titanium pedicle screws.

In dogs undergoing vertebral column stabilization, post-operative computed tomography (CT) evaluates implant placement. The impact on the interpretation of metallic artifact associated with titanium implants in dogs remains to be established. Our objective was to quantify metallic artifact on CT associated with titanium pedicle screws. The study design included an in vitro model and a retrospective review of 11 dogs with vertebral column stabilization. Twenty four titanium pedicle screws (6 each: 2.0 mm, 2.7 mm, 3.5 mm, and 4.5 mm) were inserted into a 20% ballistic gel, and CT scan of the construct was performed. Three blinded raters used a bone window to measure the maximum width (effective size) of each screw, one rater measured effective size using an ultrawide window and 45 titanium pedicle screws (3×2.0 mm, 5×2.7 mm, 30×3.5 mm, and 7×4.5 mm) in 11 clinical cases. Effective size measurements were compared to actual screw sizes. The effective size was 26.9-43.8%, 9.2-18.5%, and 21.1-30.5% larger than the actual size for the in vitro system (bone window), in vitro system (ultrawide window), and clinical cases, respectively. The mean gross difference for the in vitro measurements varied by implant size (p < 0.001) and was positively correlated with implant size (r = 0.846), but the mean percentage difference was negatively correlated with implant size (p < 0.001). Overestimation was larger for the in vitro model bone window compared to the ultrawide window (p < 0.001) and clinical cases (p = 0.001). Metallic artifact associated with titanium pedicle screws on CT resulted in an overestimation of screw size. This information might aid in the interpretation of implant placement on post-operative imaging.

Population affinities in pre-colonial West Africa: The case of the burial cave Iroungou (Gabon, 14th-15th century CE).

Our knowledge of the populations of sub-Saharan Africa in the periods before European colonization is limited. Few archeological sites containing human remains have been identified, and written sources for these periods are rare. The discovery in 2018 of the Iroungou sepulchral cave (Gabon), whose use predates the arrival of the Portuguese (14th-15th centuries CE), is an exceptional source of information: at least 28 individuals associated with numerous metal artifacts were found there. The anthropobiological remains were left in situ, but the eight best preserved crania were digitized. This study focuses on the population affinities of these crania, whose morphology was described using 237 landmarks. Geometric morphometric analyses were used to compare the eight Iroungou specimens with 154 individuals representing 12 well-defined African populations. After alignment (Generalized Procrustes Analysis), morphological affinity was assessed using Euclidean and Mahalanobis distances, and posterior probabilities of population membership (discriminant analysis). Results indicate that the eight Iroungou crania have, on average, more affinity with Bayaka Pygmy, followed by Central African Bantu. Nevertheless, individually, the Iroungou specimens show an important morphological variation and the eight crania can be separated into different affinity groups: Bayaka and Central African Bantu, KhoeSan, and East-African Bantu. Finally, one individual presents strong affinity with Somalis. This phenetic mapping of the Iroungou sample raises questions about the profile of the individuals deposited in the cave in a geographical area known for the Loango pre-colonial kingdom, which ruling class seemed to have had privileged relationships with the Pygmy populations.

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.

ReMAR: a preoperative CT angiography guided metal artifact reduction framework designed for follow-up CTA of endovascular coiling

Objective. Follow-up computed tomography angiography (CTA) is necessary for ensuring occlusion effect of endovascular coiling. However, the implanted metal coil will introduce artifacts that have a negative spillover into radiologic assessment. Method. A framework named ReMAR is proposed in this paper for metal artifacts reduction (MARs) from follow-up CTA of patients with coiled aneurysms. It employs preoperative CTA to provide the prior knowledge of the aneurysm and the expected position of the coil as a guidance thus balances the metal artifacts removal performance and clinical feasibility. The ReMAR is composed of three modules: segmentation, registration and MAR module. The segmentation and registration modules obtain the metal coil knowledge via implementing aneurysms delineation on preoperative CTA and alignment of follow-up CTA. The MAR module consisting of hybrid convolutional neural network- and transformer- architectures is utilized to restore sinogram and remove the artifact from reconstructed image. Both image quality and vessel rendering effect after metal artifacts removal are assessed in order to responding clinical concerns. Main results. A total of 137 patients undergone endovascular coiling have been enrolled in the study: 13 of them have complete diagnosis/follow-up records for end-to-end validation, while the rest lacked of follow-up records are used for model training. Quantitative metrics show ReMAR significantly reduced the metal-artifact burden in follow-up CTA. Qualitative ranks show ReMAR could preserve the morphology of blood vessels during artifact removal as desired by doctors. Significance. The ReMAR could significantly remove the artifacts caused by implanted metal coil in the follow-up CTA. It can be used to enhance the overall image quality and convince CTA an alternative to invasive follow-up in treated intracranial aneurysm.

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.

Artificial intelligence methods in dental radiography

Introduction: The application of artificial intelligence in dentistry can significantly improve the outcome of patient treatment and reduce human error. Computer systems and certain methods that use complex algorithms are able to encode digital data about the radiographic image, obtained by X-ray radiation, and transfer them to a computer language for processing a large set of data. Applied as an auxiliary tool, it can give information about the patient and diagnostics of teeth and surrounding dental structures on radiographic images (two-dimensional and three-dimensional). The aim of the paper: The aim of the paper is to present the development and application of artificial intelligence in dentistry and at the same time emphasizing the importance of segmentation methods and methods of improving the quality of dental radiographic images. Discussion: The common goal of the development and application of artificial intelligence methods in dental radiography is directed on obtaining radiograms from which diagnostically valuable information will be collected faster and easier for the purpose of successful treatment of the patients. Methods of segmentation of a dental structures have been developed to automatically number and localize the tooth or independently mark and display the desired structures on the image. Methods of image quality enhancement tend to work on, for example, improving image resolution or reducing metal artifacts. Research has shown that artificial intelligence can be applied in everyday clinical practice, but it has certain limitations. Conclusion: Artificial intelligence methods in dental radiography are an useful auxiliary tool in clinical dental practice because they can speed up the process of retrieving patient data. Automatic localization of dental structure reduces the dentist's time for manual image analysis, but cannot completely replace human knowledge. For the full implementation of the mentioned methods, more development standards and resources are needed to overcome the ethical and legal problems of replacing humans with computer systems.

Comparison of dual-energy computed tomography (DECT) polychromatic and monochromatic images with and without iterative metal artifact reduction algorithm in patients with dental implants.

Dual-energy computed tomography (DECT) and iterative metal artifact reduction (iMAR) algorithms are valuable tools for reducing metal artifacts. Different parameters of these technologies and their combination can achieve different performance. This study compared various polychromatic and monochromatic images obtained via DECT with and without using iMAR algorithm to reduce artifacts in patients with dental implants. This study included 30 patients with dental implants who underwent DECT for head and neck imaging. The computed tomography (CT) image sets comprised DECT polychromatic image sets [dual-energy (DE) polychromatic] that linearly blended 100 kV and tin-filtered 140 kV images using composition ratios of -1, -0.6, -0.3, 0, and 0.6, and virtual monochromatic images (DE monochromatic) at 90, 110, 130, 150, and 170 keV. These image sets were obtained with and without using iMAR, resulting in a total of 20 image sets. For subjective analysis, metal artifacts and image quality were assessed using a 5-point Likert scale. For objective analysis, CT attenuation, standard deviation (SD), contrast-to-noise ratio (CNR) and artifact index (AI) were evaluated. In addition, subgroup analysis was performed based on implant size. In the subjective evaluation, iMAR + DE polychromatic (-0.3) images exhibited the lowest metal artifact scores [median (interquartile range): 2 (2-3)]. iMAR + DE monochromatic (110 keV) images demonstrated optimal image quality scores [median (interquartile range): 2 (2-3)]. In the objective evaluation, none of the images demonstrated a significant difference in the CNR, except polychromatic images with a composition of -1 and 0.6. iMAR + DE polychromatic (0) exhibited the lowest AI [median (interquartile range): 8.7 (5.9-14.5)]. There was no significant difference between the two groups with different implant sizes for the techniques combined with iMAR (all P>0.05). iMAR + DE polychromatic (-0.3 and 0) and iMAR + DE monochromatic (110 keV) images exhibited better image quality and substantial metal artifact reduction (MAR) compared with the other image sets. The performance of the techniques combined with iMAR was not affected by the size of the implant.

Enhancing image quality in computed tomography angiography follow-ups after endovascular aneurysm repair: a comparative study of reconstruction techniques

BackgroundThe image quality of computed tomography angiography (CTA) images following endovascular aneurysm repair (EVAR) is not satisfactory, since artifacts resulting from metallic implants obstruct the clear depiction of stent and isolation lumens, and also adjacent soft tissues. However, current techniques to reduce these artifacts still need further advancements due to higher radiation doses, longer processing times and so on. Thus, the aim of this study is to assess the impact of utilizing Single-Energy Metal Artifact Reduction (SEMAR) alongside a novel deep learning image reconstruction technique, known as the Advanced Intelligent Clear-IQ Engine (AiCE), on image quality of CTA follow-ups conducted after EVAR.MaterialsThis retrospective study included 47 patients (mean age ± standard deviation: 68.6 ± 7.8 years; 37 males) who underwent CTA examinations following EVAR. Images were reconstructed using four different methods: hybrid iterative reconstruction (HIR), AiCE, the combination of HIR and SEMAR (HIR + SEMAR), and the combination of AiCE and SEMAR (AiCE + SEMAR). Two radiologists, blinded to the reconstruction techniques, independently evaluated the images. Quantitative assessments included measurements of image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), the longest length of artifacts (AL), and artifact index (AI). These parameters were subsequently compared across different reconstruction methods.ResultsThe subjective results indicated that AiCE + SEMAR performed the best in terms of image quality. The mean image noise intensity was significantly lower in the AiCE + SEMAR group (25.35 ± 6.51 HU) than in the HIR (47.77 ± 8.76 HU), AiCE (42.93 ± 10.61 HU), and HIR + SEMAR (30.34 ± 4.87 HU) groups (p < 0.001). Additionally, AiCE + SEMAR exhibited the highest SNRs and CNRs, as well as the lowest AIs and ALs. Importantly, endoleaks and thrombi were most clearly visualized using AiCE + SEMAR.ConclusionsIn comparison to other reconstruction methods, the combination of AiCE + SEMAR demonstrates superior image quality, thereby enhancing the detection capabilities and diagnostic confidence of potential complications such as early minor endleaks and thrombi following EVAR. This improvement in image quality could lead to more accurate diagnoses and better patient outcomes.

Major handicrafts of Uttar Pradesh and various institutions and schemes for their development

Uttar Pradesh is a state that is rich in various arts and crafts. Uttar Pradesh is home to many unique and diverse crafts that make it one of the most culturally rich states. Being home to various cultures, there are diversities in the crafts of Uttar Pradesh. The same legacy of arts and crafts has been carried forward by the people of Uttar Pradesh till date and that is why the crafts and handicrafts of Uttar Pradesh are world famous. Uttar Pradesh is famous for its unique arts and crafts that tell its rich heritage, traditions and culture. Uttar Pradesh is famous for various types of crafts, silk sarees, pottery, woven carpets, perfumes etc. Agra and Kanpur are known for their leather goods, Moradabad for its metal artifacts and Lucknow for its chikankari embroidery. The present research paper makes us aware of the various handicrafts of Uttar Pradesh and the various institutions and schemes involved in their development.

Reduction of metal artefacts from bilateral hip prostheses during lower extremity computed tomography angiography: an experimental phantom study.

Image quality reduction due to metallic artefacts is a significant challenge during vascular computed tomography (CT) imaging of the lower extremities in patients with hip prostheses. This study aims to analyse various reconstruction algorithms' ability to reduce metal artefacts due to two types of hip prostheses during lower extremity CT angiography examinations. A pelvis phantom was fabricated with the insertion of a tube filled with contrast media to simulate the femoral artery, and the phantom was then CT scanned with and without hip prostheses. Multimodal images were acquired using different kilovoltage peak (kVp) settings and reconstructed with different algorithms, such as filtered back projection (FBP), iterative reconstruction (iDose4), iterative model-based reconstruction (IMR) and orthopaedic metal artefact reduction (O-MAR). Image quality was assessed based on image noise, signal-to-noise ratio (SNR) and Hounsfield unit (HU) deviation. The IMR approach significantly improved image quality compared to iDose4 and FBP. For the vascular region, O-MAR improves SNR by 5 ± 1, 23 ± 5 and 42 ± 9 for FBP, iDose4 and IMR respectively, and improves HU precision towards the baseline values by 49% and 83% for FBP and IMR, respectively. The noise reduction was 71% and 89% for FBP and IMR, and 57% for iDose4. O-MAR greatly enhances SNR corrections among the most severe artefacts, with 29 ± 1 and 43 ± 4 for FBP and IMR, compared to iDose4 by 37 ± 7. IMR combined with O-MAR could improve the CT angiography of the lower extremities of patients with a hip prosthesis.

CBCT Artifact Evaluation in a Single Device: Insights and Limitations

Objective: To classify the types of artifacts in cone-beam computed tomography (CBCT) and to evaluate them according to age and gender. Methods: CBCT images of 1500 patients (766 males and 734 females) aged 5-92 (mean age: 40.89 ± 18.82 years) were retrospectively evaluated and the patients were categorized into 4 age groups: under 20 years old, 20-39 ages, 40-59 and over 60 years old. The types of artifacts encountered in CBCT images were classified. The relationship between the artifact types with age and gender were investigated. Chi-square test was applied to analyze the relationships between variables and distribution of parameters. Results: Of the cases, 284 (18.9%) were under the age of 20, 389 (25.9%) were between the ages of 20-39, 554 (36.9%) were between the ages of 40-59 and 273 (18.2%) were over the age of 60. Moire artifact was observed at the highest rate (100%), while motion artifact was determined at the lowest rate (19.5%), and no ring artifact was detected in the analyzed images. Metallic artifact, metallic artifact removal, streak artifact and presence of dark bands were found to be statistically significant in females (p = .002, p = .001, p = .002 and p = .002, respectively). There was no statistically significant correlation between cupping artifact, metallic artifact, metallic artifact removal, streak artifact, dark band and noise, and stitched artifact (p &gt; .05). Conclusion: Both device and patient-based artifacts in CBCT images should be known, as well as the ways to prevent them.

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 robust index for metal artifact quantification in computed tomography.

Objective assessment of metal artifact strength and the effectiveness of metal artifact reduction algorithms in computed tomography requires a quantitative metric. Metrics described in the literature are typically employed to compare the artifact strength in images reconstructed from the same raw data, but their robustness against varying scan conditions and repeated scans over time as it occurs in periodic quality assurance has not been investigated. A new robust metric for quantifying metal artifacts in computed-tomography images is proposed and compared to other commonly used metrics. The proposed artifact metric is based on the location parameter of the Gumbel distribution, described previously in the literature, but normalized to the location parameter in a background region-of-interest to obtain a noise-independent artifact metric. The metric was compared to three other quantitative metal artifact metrics (artifact-index, contrast-to-noise ratio, Gumbel-evaluation method) by evaluating metals artifacts in phantom scans and in clinical images. Robustness of the artifact metrics was evaluated using repeated scans with varying noise and against small variations in the selected regions-of-interest. The proposed artifact metric was independent of the underlying image noise and could be reproduced more consistently under slight changes of the region-of-interest within the artifact than the other investigated methods. The coefficient-of-variation was 5.7% on average with varying regions-of-interest in phantom scans and 2.5% in patient scans compared to 9.2% in phantoms scans and 9.9% in patient scans for the next-best performing noise-independent metric. Setup reproducibility was better than 5% and was comparable to the other metrics. The new metric correlated linearly with the artifact strength. The contrast-to-noise ratio, although often used in artifact quantification, was found to be an inadequate metric due to its lack of robustness against minute changes in the position, size, and pixel values of the region-of-interest chosen for calculating the metric and because it showed no correlation with the artifact strength. A new metal artifact metric has been proposed that is robust under changing scan conditions and less sensitive to user-dependent choices of the region-of-interest than other metrics. The new metric is straightforward to calculate and simple to implement in software commonly used for evaluation of medical imaging systems.

Generative Adversarial Networks (GANs) in the Field of Head and Neck Surgery: Current Evidence and Prospects for the Future-A Systematic Review.

Background: Generative Adversarial Networks (GANs) are a class of artificial neural networks capable of generating content such as images, text, and sound. For several years already, artificial intelligence algorithms have shown promise as tools in the medical field, particularly in oncology. Generative Adversarial Networks (GANs) represent a new frontier of innovation, as they are revolutionizing artificial content generation, opening opportunities in artificial intelligence and deep learning. Purpose: This systematic review aims to investigate what the stage of development of such technology is in the field of head and neck surgery, offering a general overview of the applications of such algorithms, how they work, and the potential limitations to be overcome in the future. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed in conducting this study, and the PICOS framework was used to formulate the research question. The following databases were evaluated: MEDLINE, Embase, Cochrane Central Register of Controlled Trials (CENTRAL), Scopus, ClinicalTrials.gov, ScienceDirect, and CINAHL. Results: Out of 700 studies, only 9 were included. Eight applications of GANs in the head and neck region were summarized, including the classification of craniosynostosis, recognition of the presence of chronic sinusitis, diagnosis of radicular cysts in panoramic X-rays, segmentation of craniomaxillofacial bones, reconstruction of bone defects, removal of metal artifacts from CT scans, prediction of the postoperative face, and improvement of the resolution of panoramic X-rays. Conclusions: Generative Adversarial Networks may represent a new evolutionary step in the study of pathology, oncological and otherwise, making the approach to the disease much more precise and personalized.

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

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. 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. 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. 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.