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Purpose: Chest tomosynthesis (CTS) has a relatively longer acquisition time compared with chest X-ray, which may increase the risk of motion artifacts in the reconstructed images. Motion artifacts induced by breathing motion adversely impact the image quality. This study aims to reduce these artifacts by excluding projection images identified with breathing motion prior to the reconstruction of section images and to assess if motion compensation improves overall image quality. Approach: In this study, 2969 CTS examinations were analyzed to identify examinations where breathing motion has occurred using a method based on localizing the diaphragm border in each of the projection images. A trajectory over diaphragm positions was estimated from a second-order polynomial curve fit, and projection images where the diaphragm border deviated from the trajectory were removed before reconstruction. The image quality between motion-compensated and uncompensated examinations was evaluated using the image quality criteria for anatomical structures and image artifacts in a visual grading characteristic (VGC) study. The resulting rating data were statistically analyzed using the software VGC analyzer. Results: A total of 58 examinations were included in this study with breathing motion occurring either at the beginning or end ( n = 17 ) or throughout the entire acquisition ( n = 41 ). In general, no significant difference in image quality or presence of motion artifacts was shown between the motion-compensated and uncompensated examinations. However, motion compensation significantly improved the image quality and reduced the motion artifacts in cases where motion occurred at the beginning or end. In examinations where motion occurred throughout the acquisition, motion compensation led to a significant increase in ripple artifacts and noise. Conclusions: Compensation for respiratory motion in CTS by excluding projection images may improve the image quality if the motion occurs mainly at the beginning or end of the examination. However, the disadvantages of excluding projections may outweigh the benefits of motion compensation.

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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.

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Smart cities rely on a network of sensors to gather real-time data on various environmental factors, including air quality. This paper addresses the challenges of improving the accuracy of low-cost particulate matter sensors (LCPMSs) which can be compromised by environmental conditions, such as high humidity, which is common in many urban areas. Such weather conditions often lead to the overestimation of particle counts due to hygroscopic particle growth, resulting in a potential public concern, although most of the detected particles consist of just water. The paper presents an innovative design for an indicative air-quality measuring station that integrates the particulate matter sensor with a preconditioning subsystem designed to mitigate the impact of humidity. The preconditioning subsystem works by heating the incoming air, effectively reducing the relative humidity and preventing the hygroscopic growth of particles before they reach the sensor. To validate the effectiveness of this approach, parallel measurements were conducted using both preconditioned and non-preconditioned sensors over a period of 19 weeks. The data were analyzed to compare the performance of the sensors in terms of accuracy for PM1, PM2.5, and PM10 particles. The results demonstrated a significant improvement in measurement accuracy for the preconditioned sensor, especially in environments with high relative humidity. When the conditions were too severe and both sensors started measuring incorrect values, the preconditioned sensor-measured values were closer to the actual values. Also, the period of measuring incorrect values was shorter with the preconditioned sensor. The results suggest that the implementation of air preconditioning subsystems in LCPMSs deployed in smart cities can provide a cost-effective solution to overcome humidity-related inaccuracies, thereby improving the overall quality of measured air pollution data.

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Background: Low-dose following up computed tomography (CT) of percutaneous vertebroplasty (PVP) that involves the use of bone cement usually suffers from lightweight metal artifacts, where conventional techniques for CT metal artifact reduction are often not sufficiently effective. This study aimed to validate an artificial intelligence (AI)-based metal artifact correction (MAC) algorithm for use in low-dose following up CT for PVP. Methods: In experimental validation, an ovine vertebra phantom was designed to simulate the clinical scenario of PVP. With routine-dose images acquired prior to the cement introduction as the reference, low-dose CT scans were taken on the cemented phantom and processed with conventional MAC and AI-MAC. The resulting image quality was compared in CT attenuation, image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR), followed by a quantitative evaluation of the artifact correction accuracy based on adaptive segmentation of the paraspinal muscle. In clinical validation, ten cases of low-dose following up CT after PVP were enrolled to test the performance of diagnosing sarcopenia with measured CT attenuation per cemented vertebral segment, via receiver operating characteristic (ROC) analysis. Results: With respect to the reference image, no significant difference was found for AI-MAC in CT attenuation, image noise, SNRs, and CNR (all P>0.05). The paraspinal muscle segmented on the AI-MAC image was 18.6% and 8.3% more complete to uncorrected and MAC images. Higher area under the curve (AUC) of the ROC analysis was found for AI-MAC (AUC =0.92) compared to the uncorrected (AUC =0.61) and MAC images (AUC =0.70). Conclusions: In low-dose following up CT for PVP, the AI-MAC has been fully validated for its superior ability compared to conventional MAC in suppressing artifacts and may be a reliable alternative for diagnosing sarcopenia.

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Cardiac magnetic resonance imaging (CMR) is an important clinical tool that obtains high-quality images for assessment of cardiac morphology, function, and tissue characteristics. However, the technique may be prone to artifacts that may limit the diagnostic interpretation of images. This article reviews common artifacts which may appear in CMR exams by describing their appearance, the challenges they mitigate true pathology, and offering possible solutions to reduce their impact. Additionally, this article acts as an update to previous CMR artifacts reports by including discussion about new CMR innovations.

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Various techniques have been used in attempts to reduce interfering gastrointestinal activity in myocardial perfusion imaging (MPI); however, these approaches have yielded inconsistent results. The goal of this study was to investigate the efficacy of monitored walking, a previously unexplored technique, in reducing subdiaphragmatic activity-related artifacts during pharmacologic stress 99mTc-tetrofosmin MPI with SPECT to improve the overall image quality. Methods: The study included patients who underwent MPI with pharmacologic stress. They were given a step counter immediately after the radiotracer injection and were randomized into a group A, with a request to walk at least 1,000 steps before imaging, and a group B, with no specific instructions about walking. The reconstructed SPECT images were assessed visually. Moderate and severe levels of subdiaphragmatic tracer activity were considered relevant for the interpretation of the scans. Additionally, myocardial and abdominal activity was semiquantitatively assessed on raw planar images, and the mean myocardium-to-abdomen count ratios were calculated. Results: We enrolled 199 patients (95 patients in group A and 104 patients in group B). Clinical characteristics did not differ significantly between the 2 groups. Patients in group A walked more steps than patients in group B (P < 0.001), but there were no differences in the proportion of accepted scans between the 2 groups (P = 0.41). Additionally, there were no differences in the proportion of relevant subdiaphragmatic activity between the groups (P = 0.91). The number of steps did not impact the acceptance rate (P = 0.29). Conclusion: A higher number of steps walked during the waiting period between pharmacologic stress and acquisition does not affect subdiaphragmatic activity-related artifacts or the proportion of accepted scans after pharmacologic stress. However, pedometer use and clear instructions motivate patients to walk while awaiting imaging. Larger studies are required to compare a higher-step-count group with a sedentary control group to assess the influence of walking on gastrointestinal artifacts in MPI.

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PURPOSE: Changes in the foveal avascular zone (FAZ) metrics over time are key outcome measures for clinical trials in diabetic macular ischemia (DMI). However, artifacts and automatically delineated FAZ measurements may influence the results. We aimed to compare the artifact frequency and FAZ metrics on 3 × 3 versus 6 × 6 mm optical coherence tomography angiography (OCTA) macular scans in patients with DMI. DESIGN: Prospective, comparative image quality analysis with one-year follow-up. METHODS: Patients with diabetic retinopathy (DR) were recruited if they presented with OCTA evidence of DMI, defined as an automated FAZ (aFAZ) ≥0.5 mm2 or parafoveal capillary nonperfusion (CNP) ≥1 quadrant if the aFAZ <0.5 mm2. Only those who had both size scans were included in the analysis. The types of artifacts and FAZ delineation errors were graded before manual correction. After excluding scans with poor quality, the aFAZ, corrected FAZ (cFAZ), whole image superficial vessel density (wiSVD), and whole image deep vessel density (wiDVD) were compared on both size scans. RESULTS: Fifty-seven patients (81 eyes) with paired OCTA 3 × 3 and 6 × 6 mm scans at baseline were included in the image quality analysis. The 6 × 6 mm scan presented with more severe motion artifact (P = .02). Conversely, the 3 × 3 mm scans were more susceptible to mild decentration (P = .009). After removing all the poor-quality images, 55 eyes with both size scans entered the longitudinal analysis. The 3 × 3 mm FAZ was significantly larger than the 6 × 6 mm FAZ using either aFAZ or cFAZ (both P < .05). In contrast, the 6 × 6 mm wiSVD and wiDVD were remarkably higher than those on the 3 × 3 mm scans (both P < .001). There was a steady increase in cFAZ over one year on both size scans (both P < .01). However, the 3 × 3 mm aFAZ decreased numerically at 52 weeks (P = .02). After reviewing all the scans, poor identification of parafoveal CNP was the most common reason for erroneous aFAZ delineation. CONCLUSIONS: In DMI, the FAZ metrics are best evaluated on the 3 × 3 scan due to better resolution. However, manual correction of the FAZ margin is needed. The frequency of artifacts and aFAZ delineation errors suggest that further technical refinement is required.

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PURPOSE: To compare the image quality of magnetic resonance cholangiopancreatography (MRCP) in the supine position and prone position under the conditions of the same equipment, the same sequence (3D Navigator Triggered Sampling Perfection with Application-Optimized Contrast Using Different Flip-angle Evolutions, 3D-NT-SPACE) and the same patient, and to explore the clinical application value of prone position in MRCP examination to suppress respiratory motion artifacts. METHODS: 53 participants who underwent MRCP in our hospital from April 2020 to August 2022 were prospectively collected. The 3D-NT-SPACE sequence was used in these patients. The visibility of the common bile duct, common hepatic duct, main pancreatic duct, and first- and second- and third-level branches of the intrahepatic bile duct and the comfort of the participants in two positions were subjective-evaluated. The Signal-to-noise ratio (SNR) and contrast-to-noise ratio were objective-evaluated. Statistical analysis was performed using Shapiro-Wilk, Levene's, Mann Whitney U test, Pearson chi-square test, and one-sample chi-square test. RESULTS: 53 patients (51.92 years ± 2.02, 20 men) were evaluated. There were significant differences in the second- and third-level branches visibility score, the main pancreatic duct visibility score, the image quality score of the pancreaticobiliary tree, the blur and motion artifact score, the total image quality score, and SNR between the two positions (p < 0.05). CONCLUSIONS: The overall image quality of the prone position was better than that of the supine position. The prone position is a useful complement to the supine position.

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We developed a method for automated detection of motion and noise artifacts (MNA) in electrodermal activity (EDA) signals, based on a one-dimensional U-Net architecture. EDA has been widely employed in diverse applications to assess sympathetic functions. However, EDA signals can be easily corrupted by MNA, which frequently occur in wearable systems, particularly those used for ambulatory recording. MNA can lead to false decisions, resulting in inaccurate assessment and diagnosis. Several approaches have been proposed for MNA detection; however, questions remain regarding the generalizability and the feasibility of implementation of the algorithms in real-time especially those involving deep learning approaches. In this work, we propose a deep learning approach based on a one-dimensional U-Net architecture using spectrograms of EDA for MNA detection. We developed our method using four distinct datasets, including two independent testing datasets, with a total of 9602 128-s EDA segments from 104 subjects. Our proposed scheme, including data augmentation, spectrogram computation, and 1D U-Net, yielded balanced accuracies of 80.0 ± 13.7 % and 75.0 ± 14.0 % for the two independent test datasets; these results are better than or comparable to those of other five state-of-the-art methods. Additionally, the computation time of our feature computation and machine learning classification was significantly lower than that of other methods (p < .001). The model requires only 0.28 MB of memory, which is far smaller than the two deep learning approaches (4.93 and 54.59 MB) which were used as comparisons to our study. Our model can be implemented in real-time in embedded systems, even with limited memory and an inefficient microprocessor, without compromising the accuracy of MNA detection.

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Quality control (QC) assessment is a vital part of FMRI processing and analysis, and a typically underdiscussed aspect of reproducibility. This includes checking datasets at their very earliest stages (acquisition and conversion) through their processing steps (e.g., alignment and motion correction) to regression modeling (correct stimuli, no collinearity, valid fits, enough degrees of freedom, etc.) for each subject. There are a wide variety of features to verify throughout any single-subject processing pipeline, both quantitatively and qualitatively. We present several FMRI preprocessing QC features available in the AFNI toolbox, many of which are automatically generated by the pipeline-creation tool, afni_proc.py. These items include a modular HTML document that covers full single-subject processing from the raw data through statistical modeling, several review scripts in the results directory of processed data, and command line tools for identifying subjects with one or more quantitative properties across a group (such as triaging warnings, making exclusion criteria, or creating informational tables). The HTML itself contains several buttons that efficiently facilitate interactive investigations into the data, when deeper checks are needed beyond the systematic images. The pages are linkable, so that users can evaluate individual items across a group, for increased sensitivity to differences (e.g., in alignment or regression modeling images). Finally, the QC document contains rating buttons for each "QC block," as well as comment fields for each, to facilitate both saving and sharing the evaluations. This increases the specificity of QC, as well as its shareability, as these files can be shared with others and potentially uploaded into repositories, promoting transparency and open science. We describe the features and applications of these QC tools for FMRI.

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With the rapid and continuous development of AIGC, It is becoming increasingly difficult to distinguish between real and forged facial images, which calls for efficient forgery detection systems. Although many detection methods have noticed the importance of local artifacts, there has been a lack of in-depth discussion regarding the selection of locations and their effective utilization. Besides, the traditional image augmentation methods that are widely used have limited improvements for forgery detection tasks and require more specialized augmentation methods specifically designed for forgery detection tasks. In this paper, this study proposes Local Artifacts Amplification for Deepfakes Augmentation, which amplifies the local artifacts on the forged faces. Furthermore, this study incorporates prior knowledge about similar facial features into the model. This means that within the facial regions defined in this work, forged features exhibit similar patterns. By aggregating the results from all facial regions, the study can enhance the overall performance of the model. The evaluation experiments conducted in this research, achieving an AUC of 93.40% and an Acc of 87.03% in the challenging WildDeepfake dataset, demonstrate a promising improvement in accuracy compared to traditional image augmentation methods and achieve superior performance on intra-dataset evaluation. The cross-dataset evaluation also showed that the method presented in this study has strong generalization abilities.

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BACKGROUND: Metallic artefacts caused by metal implants, are a common problem in computed tomography (CT) imaging, degrading image quality and diagnostic accuracy. With advancements in artificial intelligence, novel deep learning (DL)-based metal artefact reduction (MAR) algorithms are entering clinical practice. OBJECTIVE: This systematic review provides an overview of the performance of the current supervised DL-based MAR algorithms for CT, focusing on three different domains: sinogram, image, and dual domain. METHODS: A literature search was conducted in PubMed, EMBASE, Web of Science, and Scopus. Outcomes were assessed using peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) or any other objective measure comparing MAR performance to uncorrected images. RESULTS: After screening, fourteen studies were selected that compared DL-based MAR-algorithms with uncorrected images. MAR-algorithms were categorised into the three domains. Thirteen MAR-algorithms showed a higher PSNR and SSIM value compared to the uncorrected images and to non-DL MAR-algorithms. One study showed statistically significant better MAR performance on clinical data compared to the uncorrected images and non-DL MAR-algorithms based on Hounsfield unit calculations. CONCLUSION: DL MAR-algorithms show promising results in reducing metal artefacts, but standardised methodologies are needed to evaluate DL-based MAR-algorithms on clinical data to improve comparability between algorithms. CLINICAL RELEVANCE STATEMENT: Recent studies highlight the effectiveness of supervised Deep Learning-based MAR-algorithms in improving CT image quality by reducing metal artefacts in the sinogram, image and dual domain. A systematic review is needed to provide an overview of newly developed algorithms.

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In computed tomography (CT) imaging, optimizing the balance between radiation dose and image quality is crucial due to the potentially harmful effects of radiation on patients. Although subjective assessments by radiologists are considered the gold standard in medical imaging, these evaluations can be time-consuming and costly. Thus, objective methods, such as the peak signal-to-noise ratio and structural similarity index measure, are often employed as alternatives. However, these metrics, initially developed for natural images, may not fully encapsulate the radiologists' assessment process. Consequently, interest in developing deep learning-based image quality assessment (IQA) methods that more closely align with radiologists' perceptions is growing. A significant barrier to this development has been the absence of open-source datasets and benchmark models specific to CT IQA. Addressing these challenges, we organized the Low-dose Computed Tomography Perceptual Image Quality Assessment Challenge in conjunction with the Medical Image Computing and Computer Assisted Intervention 2023. This event introduced the first open-source CT IQA dataset, consisting of 1,000 CT images of various quality, annotated with radiologists' assessment scores. As a benchmark, this challenge offers a comprehensive analysis of six submitted methods, providing valuable insight into their performance. This paper presents a summary of these methods and insights. This challenge underscores the potential for developing no-reference IQA methods that could exceed the capabilities of full-reference IQA methods, making a significant contribution to the research community with this novel dataset. The dataset is accessible at https://zenodo.org/records/7833096.

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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.

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PURPOSE: To enable diffusion weighted imaging in prostate patients with metallic total hip replacements in clinically feasible scan times for prostate cancer screening, and avoid distortion and dropout artifacts present in the conventionally used Echo Planar Imaging (EPI). METHODS: A reduced field of view (FOV) diffusion-prepared sequence that is robust to the B 0 $$ {\kern0em }_0 $$ inhomogeneities produced by total hip replacements was achieved using high radiofrequency (RF) bandwidth pulses and manipulation for stimulated echo pathways. The reduced FOV along the A/P direction was obtained using slice-select gradient reversal, and the prepared magnetization was imaged with a three-dimensional RF-spoiled gradient echo readout. The sequence was validated in phantom experiments, in vivo in healthy volunteers with and without total hip replacements, and in vivo in patients undergoing a standard MRI prostate exam. RESULTS: The proposed sequence is robust to shading and distortion artifacts that are encountered by standard diffusion-weighted EPI in the presence of moderate off-resonance. Apparent diffusion coefficient estimates obtained by the proposed sequence were comparable to those obtained with diffusion-weighted EPI. CONCLUSION: Acquisition of distortionless diffusion weighted images of the prostate is feasible in patients with total hip replacements on conventional, whole-body 3T MRI, using a b-value of 800 s / mm 2 $$ \mathrm{s}/{\mathrm{mm}}^2 $$ and nominal resolution of 1.7 × $$ \times $$ 1.7 × $$ \times $$ 4 mm3 in scan times of 6 min.

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OBJECTIVES: To evaluate the antimicrobial efficacy of white vinegar, acetic acid and peracetic acid on photostimulable phosphor (PSP) plates disinfection, and to assess the disinfectant influence on the radiographic quality. METHODS: Eight PSP plates (Express system) were contaminated with Streptococcus mutans and Candida albicans. These plates were wiped with tissues without any substance, with white vinegar, acetic acid, and peracetic acid, followed by an agar imprint. Number of microbial colonies formed was recorded. Afterwards, the quality of radiographs was tested using the more efficient disinfectant. Before disinfection and after every five disinfections, two radiographs of an acrylic-block and two radiographs of an aluminum step-wedge were acquired for each plate. Density, noise, uniformity, and contrast were analyzed. Three oral radiologists evaluated the images for the presence of artifacts. One-way Analysis of Variance compared changes on gray values among the disinfections (α = 0.05). Intra- and inter-examiner agreement for the presence of artifacts was calculated by weighted Kappa. RESULTS: Peracetic acid was the only one that eliminated both microorganisms. Density and uniformity decreased after 100 disinfections, and contrast changed without a pattern in the course of disinfections (P ≤ 0.05). Small artifacts were observed after 30 disinfections. Intra- and inter-examiner agreements were almost perfect. CONCLUSIONS: Disinfection with peracetic acid eliminated both microorganisms. However, it also affected density, uniformity and contrast of radiographs, and led to the formation of small artifacts.

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OBJECTIVE: Challenging HR conditions, such as elevated Heart Rate (HR) and Heart Rate Variability (HRV), are major contributors to motion artifacts in Coronary Computed Tomography Angiography (CCTA). This study aims to assess the impact of a deep learning-based motion correction algorithm (MCA) on motion artifacts in patients with challenging HR conditions, focusing on image quality and diagnostic performance of CCTA. MATERIALS AND METHODS: This retrospective study included 240 patients (mean HR: 88.1 ± 14.5 bpm; mean HRV: 32.6 ± 45.5 bpm) who underwent CCTA between June, 2020 and December, 2020. CCTA images were reconstructed with and without the MCA. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were measured to assess objective image quality. Subjective image quality was evaluated by two radiologists using a 5-point scale regarding vessel visualization, diagnostic confidence, and overall image quality. Moreover, all vessels with scores ≥ 3 were considered clinically interpretable. The diagnostic performance of CCTA with and without MCA for detecting significant stenosis (≥ 50%) was assessed in 34 patients at both per-vessel and per-patient levels, using invasive coronary angiography as the reference standard. RESULTS: The MCA significantly improved subjective image quality, increasing the vessel interpretability from 89.9% (CI: 0.88-0.92) to 98.8% (CI: 0.98-0.99) (p < 0.001). The use of MCA resulted in significantly higher diagnostic performance in both patient-based (AUC: 0.83 vs. 0.58, p = 0.04) and vessel-based (AUC: 0.92 vs. 0.81, p < 0.001) analyses, with the vessel-based accuracy notably increased from 79.4% (CI: 0.72-0.86) to 91.2% (CI: 0.85-0.95) (p = 0.01). There were no significant differences in objective image quality between the two reconstructions. The mean effective dose in this study was 2.8 ± 1.1 mSv. CONCLUSION: The use of MCA allows for obtaining high-quality CCTA images and superior diagnostic performance with low radiation exposure in patients with elevated HR and HRV.

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PURPOSE: Balanced steady-state free precession (bSSFP) imaging is susceptible to outflow effects where excited spins leaving the slice as part of the blood stream are misprojected back onto the imaging plane. Previous work proposed using slice-encoding steps to localize these outflow effects from corrupting the target slice, at the expense of prolonged scan time. This present study extends this idea by proposing a means of significantly reducing most of the outflowing signal from the imaged slice using a coil localization method that acquires a slice-encoded calibration scan in addition to the 2D data, without being nearly as time-demanding as our previous method. This coil localization method is titled UNfolding Coil Localized Errors from an imperfect slice profile using a Structured Autocalibration Matrix (UNCLE SAM). METHODS: Retrospective and prospective evaluations were carried out. Both featured a 2D acquisition and a separate slice-encoded calibration of the center in-plane k $$ k $$ -space lines across all desired slice-encoding steps. RESULTS: Retrospective results featured a slice-by-slice comparison of the slice-encoded images with UNCLE SAM. UNCLE SAM's subtraction from the slice-encoded image was compared with a subtraction from the flow-corrupted 2D image, to demonstrate UNCLE SAM's capability to unfold outflowing spins. UNCLE SAM's comparison with slice encoding showed that UNCLE SAM was able to unfold up to 74% of what slice encoding achieved. Prospective results showed significant reduction in outflow effects with only a marginal increase in scan time from the 2D acquisition. CONCLUSIONS: We developed a method that effectively unfolds most outflowing spins from corrupting the target slice and does not require the explicit use of slice-encoding gradients. This development offers a method to reduce most outflow effects from the target slice within a clinically feasible scan duration compared with the fully sampled slice-encoding technique.

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While standard polysomnography has revealed the importance of the sleeping brain in health and disease, more specific insight into the relevant brain circuits requires high-density electroencephalography (EEG). However, identifying and handling sleep EEG artifacts becomes increasingly challenging with higher channel counts and/or volume of recordings. Whereas manual cleaning is time-consuming, subjective, and often yields data loss (e.g., complete removal of channels or epochs), automated approaches suitable and practical for overnight sleep EEG remain limited, especially when control over detection and repair behavior is desired. Here, we introduce a flexible approach for automated cleaning of multichannel sleep recordings, as part of the free Matlab-based toolbox SleepTrip. Key functionality includes 1) channel-wise detection of various artifact types encountered in sleep EEG, 2) channel- and time-resolved marking of data segments for repair through interpolation, and 3) visualization options to review and monitor performance. Functionality for Independent Component Analysis is also included. Extensive customization options allow tailoring cleaning behavior to data properties and analysis goals. By enabling computationally efficient and flexible automated data cleaning, this tool helps to facilitate fundamental and clinical sleep EEG research.

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Skin surface imaging has been used to examine skin lesions with a microscope for over a century and is commonly known as epiluminescence microscopy, dermatoscopy, or dermoscopy. Skin surface microscopy has been recommended to reduce the necessity of biopsy. This imaging technique could improve the clinical diagnostic performance of pigmented skin lesions. Different imaging techniques are employed in dermatology to find diseases. Segmentation and classification are the two main steps in the examination. The classification performance is influenced by the algorithm employed in the segmentation procedure. The most difficult aspect of segmentation is getting rid of the unwanted artifacts. Many deep-learning models are being created to segment skin lesions. In this paper, an analysis of common artifacts is proposed to investigate the segmentation performance of deep learning models with skin surface microscopic images. The most prevalent artifacts in skin images are hair and dark corners. These artifacts can be observed in the majority of dermoscopy images captured through various imaging techniques. While hair detection and removal methods are common, the introduction of dark corner detection and removal represents a novel approach to skin lesion segmentation. A comprehensive analysis of this segmentation performance is assessed using the surface density of artifacts. Assessment of the PH2, ISIC 2017, and ISIC 2018 datasets demonstrates significant enhancements, as reflected by Dice coefficients rising to 93.49 (86.81), 85.86 (79.91), and 75.38 (51.28) respectively, upon artifact removal. These results underscore the pivotal significance of artifact removal techniques in amplifying the efficacy of deep-learning models for skin lesion segmentation.

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