Metal artifact reduction tool and mA levels impact on the diagnosis of fracture extension in endodontically treated teeth using cone-beam CT.

AIM: To evaluate the influence of different levels of metal artifact reduction (MAR) tool and milliamperage (mA) on the diagnosis of fracture extension in endodontically treated teeth using cone beam CT (CBCT). MATERIALS AND METHODS: Ten maxillary premolars were endodontically treated and positioned in the empty sockets of a human maxilla covered with wax. CBCT acquisitions were performed using the Eagle Edge device (Dabi Atlante, Brazil) adjusted to 120 kVp, FOV of 4 × 6 cm, exposure time of 24 s and voxel size of 0.2 mm in 8 different conditions with different MAR (1, 2 and 3) and mA (3.2 and 6.3) levels. Crown-root fractures were simulated in the universal testing machine, and CBCT images were acquired again. Five radiologists evaluated the presence and extension of fractures with a 5-point scale. Statistical analysis was performed by analysis of variance, Tukey and Kappa test (α = 0.05). RESULTS: Although different mA levels did not significantly (p > 0.05) affect the diagnosis values for fracture presence and extension, when evaluated the different levels of MAR, AUC and sensitivity showed significantly higher values (p < 0.05) for MAR 0 using 6.3 mA and kappa agreement showed significantly higher values (p < 0.05) for MAR 0 and 2 using 6.3 mA. CONCLUSIONS: Although mA levels do not have a diagnostic effect when isolating the MAR level; in 6.3 mA, MAR 0 and 2 can positively influence the diagnosis of fracture extension in endodontically treated teeth using CBCT. CLINICAL RELEVANCE: The isolate evaluation of dental fracture presence can overlook diagnostics error of its extension.

Integrating spatial and temporal features for enhanced artifact removal in multi-channel EEG recordings.

Objective.Various artifacts in electroencephalography (EEG) are a big hurdle to prevent brain-computer interfaces from real-life usage. Recently, deep learning-based EEG denoising methods have shown excellent performance. However, existing deep network designs inadequately leverage inter-channel relationships in processing multi-channel EEG signals. Typically, most methods process multi-channel signals in a channel-by-channel way. Considering the correlations among EEG channels during the same brain activity, this paper proposes utilizing channel relationships to enhance denoising performance.Approach.We explicitly model the inter-channel relationships using the self-attention mechanism, hypothesizing that these correlations can support and improve the denoising process. Specifically, we introduce a novel denoising network, named spatial-temporal fusion network (STFNet), which integrates stacked multi-dimension feature extractor to explicitly capture both temporal dependencies and spatial relationships.Main results.The proposed network exhibits superior denoising performance, with a 24.27% reduction in relative root mean squared error compared to other methods on a public benchmark. STFNet proves effective in cross-dataset denoising and downstream classification tasks, improving accuracy by 1.40%, while also offering fast processing on CPU.Significance.The experimental results demonstrate the importance of integrating spatial and temporal characteristics. The computational efficiency of STFNet makes it suitable for real-time applications and a potential tool for deployment in realistic environments.

Synthetic CT generation from CBCT based on structural constraint cycle-EEM-GAN.

Objective. Cone beam CT (CBCT) typically has severe image artifacts and inaccurate HU values, which limits its application in radiation medicines. Scholars have proposed the use of cycle consistent generative adversarial network (Cycle-GAN) to address these issues. However, the generation quality of Cycle-GAN needs to be improved. This issue is exacerbated by the inherent size discrepancies between pelvic CT scans from different patients, as well as varying slice positions within the same patient, which introduce a scaling problem during training.Approach. We introduced the Enhanced Edge and Mask (EEM) approach in our structural constraint Cycle-EEM-GAN. This approach is designed to not only solve the scaling problem but also significantly improve the generation quality of the synthetic CT images. Then data from sixty pelvic patients were used to investigate the generation of synthetic CT (sCT) from CBCT.Main results.The mean absolute error (MAE), the root mean square error (RMSE), the peak signal to noise ratio (PSNR), the structural similarity index (SSIM), and spatial nonuniformity (SNU) are used to assess the quality of the sCT generated from CBCT. Compared with CBCT images, the MAE improved from 53.09 to 37.74, RMSE from 185.22 to 146.63, SNU from 0.38 to 0.35, PSNR from 24.68 to 32.33, SSIM from 0.624 to 0.981. Also, the Cycle-EEM-GAN outperformed Cycle-GAN in terms of visual evaluation and loss.Significance.Cycle-EEM-GAN has improved the quality of CBCT images, making the structural details clear while prevents image scaling during the generation process, so that further promotes the application of CBCT in radiotherapy.

A novel procedure to automate the removal of PLI and motion artifacts using mode decomposition to enhance pattern recognition of sEMG signals for myoelectric control of prosthesis.

Hand Movement Recognition (HMR) with sEMG is crucial for artificial hand prostheses. HMR performance mostly depends on the feature information that is fed to the classifiers. However, sEMG often captures noise like power line interference (PLI) and motion artifacts. This may extract redundant and insignificant feature information, which can degrade HMR performance and increase computational complexity. This study aims to address these issues by proposing a novel procedure for automatically removing PLI and motion artifacts from experimental sEMG signals. This will make it possible to extract better features from the signal and improve the categorization of various hand movements. Empirical mode decomposition and energy entropy thresholding are utilized to select relevant mode components for artifact removal. Time domain features are then used to train classifiers (kNN, LDA, SVM) for hand movement categorization, achieving average accuracies of 92.36%, 93.63%, and 98.12%, respectively, across subjects. Additionally, muscle contraction efforts are classified into low, medium, and high categories using this technique. Validation is performed on data from ten subjects performing eight hand movement classes and three muscle contraction efforts with three surface electrode channels. Results indicate that the proposed preprocessing improves average accuracy by 9.55% with the SVM classifier, significantly reducing computational time.

Longitudinal registration of thoracic CT images with radiation-induced lung diseases: A divide-and-conquer approach based on component structure wise registration using coherent point drift.

BACKGROUND AND OBJECTIVE: Registration of pulmonary computed tomography (CT) images with radiation-induced lung diseases (RILD) was essential to investigate the voxel-wise relationship between the formation of RILD and the radiation dose received by different tissues. Although various approaches had been developed for the registration of lung CTs, their performances remained clinically unsatisfactory for registration of lung CT images with RILD. The main difficulties arose from the longitudinal change in lung parenchyma, including RILD and volumetric change of lung cancers, after radiation therapy, leading to inaccurate registration and artifacts caused by erroneous matching of the RILD tissues. METHODS: To overcome the influence of the parenchymal changes, a divide-and-conquer approach rooted in the coherent point drift (CPD) paradigm was proposed. The proposed method was based on two kernel ideas. One was the idea of component structure wise registration. Specifically, the proposed method relaxed the intrinsic assumption of equal isotropic covariances in CPD by decomposing a lung and its surrounding tissues into component structures and independently registering the component structures pairwise by CPD. The other was the idea of defining a vascular subtree centered at a matched branch point as a component structure. This idea could not only provide a sufficient number of matched feature points within a parenchyma, but avoid being corrupted by the false feature points resided in the RILD tissues due to globally and indiscriminately sampling using mathematical operators. The overall deformation model was built by using the Thin Plate Spline based on all matched points. RESULTS: This study recruited 30 pairs of lung CT images with RILD, 15 of which were used for internal validation (leave-one-out cross-validation) and the other 15 for external validation. The experimental results showed that the proposed algorithm achieved a mean and a mean of maximum 1 % of average surface distances <2 and 8 mm, respectively, and a mean and a maximum target registration error <2 mm and 5 mm on both internal and external validation datasets. The paired two-sample t-tests corroborated that the proposed algorithm outperformed a recent method, the Stavropoulou's method, on the external validation dataset (p < 0.05). CONCLUSIONS: The proposed algorithm effectively reduced the influence of parenchymal changes, resulting in a reasonably accurate and artifact-free registration.

Real-time identification of noise type contaminated in surface electromyogram signals using efficient statistical features.

Different types of noise contaminating the surface electromyogram (EMG) signal may degrade the recognition performance. For noise removal, the type of noise has to first be identified. In this paper, we propose a real-time efficient system for identifying a clean EMG signal and noisy EMG signals contaminated with any one of the following three types of noise: electrocardiogram interference, spike noise, and power line interference. Two statistical descriptors, kurtosis and skewness, are used as input features for the cascading quadratic discriminant analysis classifier. An efficient simplification of kurtosis and skewness calculations that can reduce computation time and memory storage is proposed. The experimental results from the real-time system based on an ATmega 2560 microcontroller demonstrate that the kurtosis and skewness values show root mean square errors between the traditional and proposed efficient techniques of 0.08 and 0.09, respectively. The identification accuracy with five-fold cross-validation resulting from the quadratic discriminant analysis classifier is 96.00%.

Subdiaphragmatic activity-related artifacts in myocardial perfusion scintigraphy.

BACKGROUND: Myocardial perfusion imaging (MPI) with single photon emission computed tomography is an established non-invasive technique for assessing myocardial ischemia. This method involves the intravenous administration of a radiopharmaceutical that accumulates in the heart muscle proportional to regional blood flow. However, image quality and diagnostic accuracy can be compromised by various technical and patient-related factors, including high non-specific radiopharmaceutical uptake in abdominal organs such as the stomach, intestines, liver, and gall-bladder, leading to subdiaphragmatic artifacts. These artifacts are particularly problematic for evaluating inferior wall perfusion and often necessitate repeated imaging, which decreases gamma camera availability and prolongs imaging times. CONCLUSIONS: Despite numerous investigated techniques to reduce interfering gastrointestinal activity, results have been inconsistent, and current MPI guidelines provide scant information on effective procedures to mitigate this issue. Based on our experience, some possible approaches to reducing artifacts include choosing stress testing with an exercise stress test, when possible, late imaging, fluid intake, and consuming carbonated water immediately before imaging.

Variability of morphology in photoplethysmographic waveform quantified with unsupervised wave-shape manifold learning for clinical assessment.

Objective.We investigated fluctuations of the photoplethysmography (PPG) waveform in patients undergoing surgery. There is an association between the morphologic variation extracted from arterial blood pressure (ABP) signals and short-term surgical outcomes. The underlying physiology could be the numerous regulatory mechanisms on the cardiovascular system. We hypothesized that similar information might exist in PPG waveform. However, due to the principles of light absorption, the noninvasive PPG signals are more susceptible to artifacts and necessitate meticulous signal processing.Approach.Employing the unsupervised manifold learning algorithm, dynamic diffusion map, we quantified multivariate waveform morphological variations from the PPG continuous waveform signal. Additionally, we developed several data analysis techniques to mitigate PPG signal artifacts to enhance performance and subsequently validated them using real-life clinical database.Main results.Our findings show similar associations between PPG waveform during surgery and short-term surgical outcomes, consistent with the observations from ABP waveform analysis.Significance.The variation of morphology information in the PPG waveform signal in major surgery provides clinical meanings, which may offer new opportunity of PPG waveform in a wider range of biomedical applications, due to its non-invasive nature.

Curated character of the Initial Upper Palaeolithic lithic artefact assemblages in Bacho Kiro Cave (Bulgaria).

The dispersal of Homo sapiens across Eurasia during MIS 3 in the Late Pleistocene is marked by technological shifts and other behavioral changes, known in the archaeological record under the term of Initial Upper Paleolithic (IUP). Bacho Kiro Cave in north Bulgaria, re-excavated by us from 2015 to 2021, is one of the reference sites for this phenomenon. The newly excavated lithic assemblages dated by radiocarbon between 45,040 and 43,280 cal BP and attributed to Homo sapiens encompass more than two thousand lithic artifacts. The lithics, primarily from Layer N1-I, exist amid diverse fauna remains, human fossils, pierced animal teeth pendants, and sediment with high organic content. This article focuses on the technological aspects of the IUP lithics, covering raw material origin and use-life, blank production, on-site knapping activities, re-flaking of lithic implements, and the state of retouched lithic components. We apply petrography for the identification of silicites and other used stones. We employ chaîne opératoire and reduction sequence approaches to profile the lithics techno-typologically and explore the lithic economy, particularly blade production methods, knapping techniques, and artifact curation. Raw material analysis reveals Lower Cretaceous flints from Ludogorie and Upper Cretaceous flints from the Danube region, up to 190 km and 130 km, respectively, from Bacho Kiro Cave, indicating long-distance mobility and finished products transport. Imported lithic implements, were a result of unidirectional and bidirectional non-Levallois laminar technology, likely of volumetric concept. Systematic on-anvil techniques (bipolar knapping) and tool segmentation indicate re-flaking and reshaping of lithic implements, reflecting on-site curation and multifaceted lithic economy. A limited comparison with other IUP sites reveals certain shared features and also regional variations. Bacho Kiro Cave significantly contributes to understanding the technological and behavioral evolution of early Homo sapiens in western Eurasia.

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.

A new method called MiKneeSoTA to minimize knee soft-tissue artifacts in kinematic analysis.

The use of marker-based optical motion capture to estimate joint kinematics during gait is currently limited by errors associated with soft-tissue-induced motion artefacts (STIMA) and ambiguity in landmark palpation. This study therefore presents a novel protocol aiming to Minimize Knee Soft-Tissue Artefacts (MiKneeSoTA) and their effect on kinematic estimates. Relying on an augmented marker set and a new inverse kinematics approach, our method leverages frame-by-frame optimization to adjust best-fit cylinders that have been automatically generated based on the relative position of lower limb markers during an initial static trial. Tibiofemoral rotations and translations are then calculated along the anatomical joint axes based on the relative 3D motion of these cylinders. When compared against the conventional Helen-Hayes approach, in vivo assessment of fifteen healthy subjects revealed the MiKneeSoTA approach led to kinematic profiles with significantly lower standard deviations in joint rotations across trials, and even visibly reduced the presence of high frequency fluctuations presumably associated with e.g. soft-tissue vibration. In addition to agreeing with previously published bone pin and fluoroscopy datasets, our results illustrate MiKneeSoTA's ability to abate the effect of STIMA induced by lateral knee ligaments. Our findings indicate that MiKneeSoTA is in fact a promising approach to mitigate knee joint STIMA and thus enable the previously unattainable accurate estimation of translational knee joint motion with an optoelectronic system.

Large-scale analysis of whole genome sequencing data from formalin-fixed paraffin-embedded cancer specimens demonstrates preservation of clinical utility.

Whole genome sequencing (WGS) provides comprehensive, individualised cancer genomic information. However, routine tumour biopsies are formalin-fixed and paraffin-embedded (FFPE), damaging DNA, historically limiting their use in WGS. Here we analyse FFPE cancer WGS datasets from England's 100,000 Genomes Project, comparing 578 FFPE samples with 11,014 fresh frozen (FF) samples across multiple tumour types. We use an approach that characterises rather than discards artefacts. We identify three artefactual signatures, including one known (SBS57) and two previously uncharacterised (SBS FFPE, ID FFPE), and develop an "FFPEImpact" score that quantifies sample artefacts. Despite inferior sequencing quality, FFPE-derived data identifies clinically-actionable variants, mutational signatures and permits algorithmic stratification. Matched FF/FFPE validation cohorts shows good concordance while acknowledging SBS, ID and copy-number artefacts. While FF-derived WGS data remains the gold standard, FFPE-samples can be used for WGS if required, using analytical advancements developed here, potentially democratising whole cancer genomics to many.

Ambulatory ECG noise reduction algorithm for conditional diffusion model based on multi-kernel convolutional transformer.

Ambulatory electrocardiogram (ECG) testing plays a crucial role in the early detection, diagnosis, treatment evaluation, and prevention of cardiovascular diseases. Clear ECG signals are essential for the subsequent analysis of these conditions. However, ECG signals obtained during exercise are susceptible to various noise interferences, including electrode motion artifact, baseline wander, and muscle artifact. These interferences can blur the characteristic ECG waveforms, potentially leading to misjudgment by physicians. To suppress noise in ECG signals more effectively, this paper proposes a novel deep learning-based noise reduction method. This method enhances the diffusion model network by introducing conditional noise, designing a multi-kernel convolutional transformer network structure based on noise prediction, and integrating the diffusion model inverse process to achieve noise reduction. Experiments were conducted on the QT database and MIT-BIH Noise Stress Test Database and compared with the algorithms in other papers to verify the effectiveness of the present method. The results indicate that the proposed method achieves optimal noise reduction performance across both statistical and distance-based evaluation metrics as well as waveform visualization, surpassing eight other state-of-the-art methods. The network proposed in this paper demonstrates stable performance in addressing electrode motion artifact, baseline wander, muscle artifact, and the mixed complex noise of these three types, and it is anticipated to be applied in future noise reduction analysis of clinical dynamic ECG signals.

Assessment of material identification and quantification in the presence of metals using spectral photon counting CT.

Spectral Photon Counting Computed Tomography (SPCCT), a ground-breaking development in CT technology, has immense potential to address the persistent problem of metal artefacts in CT images. This study aims to evaluate the potential of Mars photon-counting CT technology in reducing metal artefacts. It focuses on identifying and quantifying clinically significant materials in the presence of metal objects. A multi-material phantom was used, containing inserts of varying concentrations of hydroxyapatite (a mineral present in teeth, bones, and calcified plaque), iodine (used as a contrast agent), CT water (to mimic soft tissue), and adipose (as a fat substitute). Three sets of scans were acquired: with aluminium, with stainless steel, and without a metal insert as a reference dataset. Data acquisition was performed using a Mars SPCCT scanner (Microlab 5×120); operated at 118 kVp and 80 µA. The images were subsequently reconstructed into five energy bins: 7-40, 40-50, 50-60, 60-79, and 79-118 keV. Evaluation metrics including signal-to-noise ratio (SNR), linearity of attenuation profiles, root mean square error (RMSE), and area under the curve (AUC) were employed to assess the energy and material-density images with and without metal inserts. Results show decreased metal artefacts and a better signal-to-noise ratio (up to 25%) with increased energy bins as compared to reference data. The attenuation profile also demonstrated high linearity (R2 >0.95) and lower RMSE across all material concentrations, even in the presence of aluminium and steel. Material identification accuracy for iodine and hydroxyapatite (with and without metal inserts) remained consistent, minimally impacting AUC values. For demonstration purposes, the biological sample was also scanned with the stainless steel volar implant and cortical bone screw, and the images were objectively assessed to indicate the potential effectiveness of SPCCT in replicating real-world clinical scenarios.

[Research on dynamic blood oxygen saturation measurement based on motion noise reconstruction combined with convex combination least mean square adaptive filter].

The performance of a pulse oximeter based on photoelectric detection is greatly affected by motion noise (MA) in the photoplethysmographic (PPG) signal. This paper presents an algorithm for detecting motion oxygen saturation, which reconstructs a motion noise reference signal using ensemble of complete adaptive noise and empirical mode decomposition combined with multi-scale permutation entropy, and eliminates MA in the PPG signal using a convex combination least mean square adaptive filters to calculate dynamic oxygen saturation. The test results show that, under simulated walking and jogging conditions, the mean absolute error (MAE) of oxygen saturation estimated by the proposed algorithm and the reference oxygen saturation are 0.05 and 0.07, respectively, with means absolute percentage error (MAPE) of 0.05% and 0.07%, respectively. The overall Pearson correlation coefficient reaches 0.971 2. The proposed scheme effectively reduces motion artifacts in the corrupted PPG signal and is expected to be applied in portable photoelectric pulse oximeters to improve the accuracy of dynamic oxygen saturation measurement.

Respiratory motion-corrected T1 mapping of the abdomen.

OBJECTIVE: The purpose of this study was to investigate an approach for motion-corrected T1 mapping of the abdomen that allows for free breathing data acquisition with 100% scan efficiency. MATERIALS AND METHODS: Data were acquired using a continuous golden radial trajectory and multiple inversion pulses. For the correction of respiratory motion, motion estimation based on a surrogate was performed from the same data used for T1 mapping. Image-based self-navigation allowed for binning and reconstruction of respiratory-resolved images, which were used for the estimation of respiratory motion fields. Finally, motion-corrected T1 maps were calculated from the data applying the estimated motion fields. The method was evaluated in five healthy volunteers. For the assessment of the image-based navigator, we compared it to a simultaneously acquired ultrawide band radar signal. Motion-corrected T1 maps were evaluated qualitatively and quantitatively for different scan times. RESULTS: For all volunteers, the motion-corrected T1 maps showed fewer motion artifacts in the liver as well as sharper kidney structures and blood vessels compared to uncorrected T1 maps. Moreover, the relative error to the reference breathhold T1 maps could be reduced from up to 25% for the uncorrected T1 maps to below 10% for the motion-corrected maps for the average value of a region of interest, while the scan time could be reduced to 6-8 s. DISCUSSION: The proposed approach allows for respiratory motion-corrected T1 mapping in the abdomen and ensures accurate T1 maps without the need for any breathholds.

Elimination of positive and negative sampling artifacts in particulate organic carbon and PAHs using multi-sorbent coated and uncoated denuders.

Within the scope of this study, two equivalent PM2.5 samplers were designed and developed to eliminate sampling artifacts in the results of atmospheric particulate organic carbon (OC) and particulate polycyclic aromatic hydrocarbons (PAH) caused by volatile organic compounds (VOCs) and gas phase PAH compounds, respectively. A mass loss of less than 10% due to the denuders was observed. Study results showed that if an impregnated denuder is not used, the results of atmospheric particle OC concentrations will be reported with higher values due to positive errors of 53.2 ± 7.23% (median: 52.00%) on average. It was observed that the total error (net error) was still positive, but decreased to an average of 35.1 ± 16.8% (median: 31.0%) after including the negative errors quantified from the backup filter into the calculation. In cases where denuders were not used in the sampling, it was observed that the results with positive errors of 41.0 ± 14.6% (median: 33.8%) on average would be obtained for the total PAHs. Ozone-induced negative interference was the highest in Acenapthylene (28%), followed by Fluoranthane (20%), Phenanthrene (18%), and 15% for Np and Benzo[g,h,i]perylene compounds, relative to their medians. Negative errors of 10% or less were found in all other individual PAH compounds.

Deep learning applications for quantitative and qualitative PET in PET/MR: technical and clinical unmet needs.

We aim to provide an overview of technical and clinical unmet needs in deep learning (DL) applications for quantitative and qualitative PET in PET/MR, with a focus on attenuation correction, image enhancement, motion correction, kinetic modeling, and simulated data generation. (1) DL-based attenuation correction (DLAC) remains an area of limited exploration for pediatric whole-body PET/MR and lung-specific DLAC due to data shortages and technical limitations. (2) DL-based image enhancement approximating MR-guided regularized reconstruction with a high-resolution MR prior has shown promise in enhancing PET image quality. However, its clinical value has not been thoroughly evaluated across various radiotracers, and applications outside the head may pose challenges due to motion artifacts. (3) Robust training for DL-based motion correction requires pairs of motion-corrupted and motion-corrected PET/MR data. However, these pairs are rare. (4) DL-based approaches can address the limitations of dynamic PET, such as long scan durations that may cause patient discomfort and motion, providing new research opportunities. (5) Monte-Carlo simulations using anthropomorphic digital phantoms can provide extensive datasets to address the shortage of clinical data. This summary of technical/clinical challenges and potential solutions may provide research opportunities for the research community towards the clinical translation of DL solutions.

Intraindividual Comparison of Image Artifacts of Two Generations of Rotatable Cochlear Implant Magnets.

In cochlear implant recipients, the diagnostic value of magnetic resonance imaging (MRI) scans is reduced by image artifacts. The static magnetic field of a 3.0T scanner is associated with the risk of implant demagnetization. The development of rotatable implant magnets aimed to support the advancement of 3.0T MRI scanners and eliminate the risk of demagnetization of cochlear implant magnets. This study aimed to compare the image artifacts caused by first-t and second-generation rotatable cochlear implant magnets in 3.0T MRI. Three Tesla MRI T2W TSE sequences were performed on 3 subjects with first- and second-generation rotatable cochlear implant magnets. The cochlear implant was fixed to the head at the implantation position by a swim cap. The size of the image artifact was determined in the transverse plane. Intraindividual comparative analyses showed that within the margin of combined uncertainty of 5 mm at a resolution of 2 mm, the cochlear implant-induced image artifacts in all subjects showed for both (first- and second-generation rotatable cochlear implant magnets), the same maximum image artifact dimension of 125 mm. We could show that no difference in image artifact size was detected within the margin of error determined by resolution, localized induced shift of the scan, and reproducibility of the tilt angle of the head relative to the chest in a living subject. Assumed improved magnet attachment can be reached without compromising of the magnet artifact size.

High Prevalence of Artifacts in Optical Coherence Tomography With Adequate Signal Strength.

Purpose: This study aims to investigate the prevalence of artifacts in optical coherence tomography (OCT) images with acceptable signal strength and evaluate the performance of supervised deep learning models in improving OCT image quality assessment. Methods: We conducted a retrospective study on 4555 OCT images from 546 patients, with each image having an acceptable signal strength (≥6). A comprehensive analysis of prevalent OCT artifacts was performed, and five pretrained convolutional neural network models were trained and tested to infer images based on quality. Results: Our results showed a high prevalence of artifacts in OCT images with acceptable signal strength. Approximately 21% of images were labeled as nonacceptable quality. The EfficientNetV2 model demonstrated superior performance in classifying OCT image quality, achieving an area under the receiver operating characteristic curve of 0.950 ± 0.007 and an area under the precision recall curve of 0.985 ± 0.002. Conclusions: The findings highlight the limitations of relying solely on signal strength for OCT image quality assessment and the potential of deep learning models in accurately classifying image quality. Translational Relevance: Application of the deep learning-based OCT image quality assessment models may improve the OCT image data quality for both clinical applications and research.