[A dual-domain cone beam computed tomography reconstruction framework with improved differentiable domain transform for cone-angle artifact correction].

OBJECTIVE: We propose a dual-domain cone beam computed tomography (CBCT) reconstruction framework DualCBR-Net based on improved differentiable domain transform for cone-angle artifact correction. METHODS: The proposed CBCT dual-domain reconstruction framework DualCBR-Net consists of 3 individual modules: projection preprocessing, differentiable domain transform, and image post-processing. The projection preprocessing module first extends the original projection data in the row direction to ensure full coverage of the scanned object by X-ray. The differentiable domain transform introduces the FDK reconstruction and forward projection operators to complete the forward and gradient backpropagation processes, where the geometric parameters correspond to the extended data dimension to provide crucial prior information in the forward pass of the network and ensure the accuracy in the gradient backpropagation, thus enabling precise learning of cone-beam region data. The image post-processing module further fine-tunes the domain-transformed image to remove residual artifacts and noises. RESULTS: The results of validation experiments conducted on Mayo's public chest dataset showed that the proposed DualCBR-Net framework was superior to other comparison methods in terms of artifact removal and structural detail preservation. Compared with the latest methods, the DualCBR-Net framework improved the PSNR and SSIM by 0.6479 and 0.0074, respectively. CONCLUSION: The proposed DualCBR-Net framework for cone-angle artifact correction allows effective joint training of the CBCT dual-domain network and is especially effective for large cone-angle region.

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

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

Correcting common OCT artifacts enhances plaque classification and identification of higher-risk plaque features.

BACKGROUND: Optical coherence tomography (OCT) is used widely to guide stent placement, identify higher-risk plaques, and assess mechanisms of drug efficacy. However, a range of common artifacts can prevent accurate plaque classification and measurements, and limit usable frames in research studies. We determined whether pre-processing OCT images corrects artifacts and improves plaque classification. METHODS: We examined both ex-vivo and clinical trial OCT pullbacks for artifacts that prevented accurate tissue identification and/or plaque measurements. We developed Fourier transform-based software that reconstructed images free of common OCT artifacts, and compared corrected and uncorrected images. RESULTS: 48 % of OCT frames contained image artifacts, with 62 % of artifacts over or within lesions, preventing accurate measurement in 12 % frames. Pre-processing corrected >70 % of all artifacts, including thrombus, macrophage shadows, inadequate flushing, and gas bubbles. True tissue reconstruction was achieved in 63 % frames that would otherwise prevent accurate clinical measurements. Artifact correction was non-destructive and retained anatomical lumen and plaque parameters. Correction improved accuracy of plaque classification compared against histology and retained accurate assessment of higher-risk features. Correction also changed plaque classification and prevented artifact-related measurement errors in a clinical study, and reduced unmeasurable frames to <5 % ex-vivo and ~1 % in-vivo. CONCLUSIONS: Fourier transform-based pre-processing corrects a wide range of common OCT artifacts, improving identification of higher-risk features and plaque classification, and allowing more of the whole dataset to be used for clinical decision-making and in research. Pre-processing can augment OCT image analysis systems both for stent optimization and in natural history or drug studies.

Shredding artifacts: extracting brain activity in EEG from extreme artifacts during skateboarding using ASR and ICA.

Introduction: To understand brain function in natural real-world settings, it is crucial to acquire brain activity data in noisy environments with diverse artifacts. Electroencephalography (EEG), while susceptible to environmental and physiological artifacts, can be cleaned using advanced signal processing techniques like Artifact Subspace Reconstruction (ASR) and Independent Component Analysis (ICA). This study aims to demonstrate that ASR and ICA can effectively extract brain activity from the substantial artifacts occurring while skateboarding on a half-pipe ramp. Methods: A dual-task paradigm was used, where subjects were presented with auditory stimuli during skateboarding and rest conditions. The effectiveness of ASR and ICA in cleaning artifacts was evaluated using a support vector machine to classify the presence or absence of a sound stimulus in single-trial EEG data. The study evaluated the effectiveness of ASR and ICA in artifact cleaning using five different pipelines: (1) Minimal cleaning (bandpass filtering), (2) ASR only, (3) ICA only, (4) ICA followed by ASR (ICAASR), and (5) ASR preceding ICA (ASRICA). Three skateboarders participated in the experiment. Results: Results showed that all ICA-containing pipelines, especially ASRICA (69%, 68%, 63%), outperformed minimal cleaning (55%, 52%, 50%) in single-trial classification during skateboarding. The ASRICA pipeline performed significantly better than other pipelines containing ICA for two of the three subjects, with no other pipeline performing better than ASRICA. The superior performance of ASRICA likely results from ASR removing non-stationary artifacts, enhancing ICA decomposition. Evidenced by ASRICA identifying more brain components via ICLabel than ICA alone or ICAASR for all subjects. For the rest condition, with fewer artifacts, the ASRICA pipeline (71%, 82%, 75%) showed slight improvement over minimal cleaning (73%, 70%, 72%), performing significantly better for two subjects. Discussion: This study demonstrates that ASRICA can effectively clean artifacts to extract single-trial brain activity during skateboarding. These findings affirm the feasibility of recording brain activity during physically demanding tasks involving substantial body movement, laying the groundwork for future research into the neural processes governing complex and coordinated body movements.

A Case of a Mirror Image Artifact in the Forearm.

Teaching point: To emphasize the importance of recognizing mirror image artifacts in musculoskeletal ultrasound to avoid misdiagnosis, unnecessary interventions, and additional diagnostic procedures that can lead to patient anxiety, increased healthcare costs, and potential harm.

[Real-time Detection Method for Motion Artifact of Photoplethysmography Signals Based on Decision Trees].

PPG (photoplethysmography) holds significant application value in wearable and intelligent health devices. However, during the acquisition process, PPG signals can generate motion artifacts due to inevitable coupling motion, which diminishes signal quality. In response to the challenge of real-time detection of motion artifacts in PPG signals, this study analyzed the generation and significant features of PPG signal interference. Seven features were extracted from the pulse interval data, and those exhibiting notable changes were filtered using the dual-sample Kolmogorov-Smirnov test. The real-time detection of motion artifacts in PPG signals was ultimately based on decision trees. In the experimental phase, PPG signal data from 20 college students were collected to formulate the experimental dataset. The experimental results demonstrate that the proposed method achieves an average accuracy of (94.07±1.14)%, outperforming commonly used motion artifact detection algorithms in terms of accuracy and real-time performance.

[Methods for Processing Physiological Artifacts in Single/Few-Channel EEG Signals].

Electroencephalogram (EEG) is a non-invasive measurement method of brain electrical activity. In recent years, single/few-channel EEG has been used more and more, but various types of physiological artifacts seriously affect the analysis and wide application of single/few-channel EEG. In this paper, the regression and filtering methods, decomposition methods, blind source separation methods and machine learning methods involved in the various physiological artifacts in single/few-channel EEG are reviewed. According to the characteristics of single/few-channel EEG signals, hybrid EEG artifact removal methods for different scenarios are analyzed and summarized, mainly including single-artifact/multi-artifact scenes and online/offline scenes. In addition, the methods and metrics for validating the performance of the algorithm on semi-simulated and real EEG data are also reviewed. Finally, the development trend of single/few-channel EEG application and physiological artifact processing is briefly described.

A robust index for metal artifact quantification in computed tomography.

BACKGROUND: 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. PURPOSE: A new robust metric for quantifying metal artifacts in computed-tomography images is proposed and compared to other commonly used metrics. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Clinical High-Resolution Imaging of the Inner Ear by Using Magnetic Resonance Imaging (MRI) and Cone Beam Computed Tomography (CBCT).

PURPOSE: Imaging of the delicate inner ear morphology has become more and more precise owing to the rapid progress in magnetic resonance imaging (MRI). However, in clinical practice, the interpretation of imaging findings is hampered by a limited knowledge of anatomical details which are frequently obscured by artifacts. Corresponding review articles are as rare in journals as they are in reference books. This shortness prompted us to perform a direct comparison of imaging with anatomical whole-mount sections as a reference. It was the intention of this paper to compare the microscopic anatomy of a human inner ear as shown on anatomical whole-mount sections with high-resolution MRI and cone beam computed tomography (CBCT). Both are available in clinical routine and depict the structures with maximum spatial resolution. It was also a goal of this work to clarify if structures that were observed on MRI in a regular manner correlate with factual inner ear anatomy or correspond with artifacts typical for imaging. METHODS: A fresh human anatomical specimen was examined on a clinical 3-Tesla MRI scanner using a dedicated surface coil. The same specimen was then studied with CBCT. In each imaging modality, high-resolution 3D data sets which enabled multiplanar reformatting were created. In the second step, anatomical whole-mount sections of the specimen were cut and stained. This process enabled a direct comparison of imaging with anatomical conditions. RESULTS: Clinical MRI was able to depict the inner ear with remarkable anatomical precision. Strongly T2-weighted imaging protocols are exquisitely capable of showing the fluid-filled components of the inner ear. The macular organs, ampullar crests and cochlear aqueduct were clearly visible. Truncation artifacts are prone to be confused with the delicate membrane separating the endolymphatic from the perilymphatic compartment. However, it was not possible to directly depict this borderline. CONCLUSIONS: With the maximum resolution of magnetic resonance tomography, commonly used in everyday clinical practice, even the smallest details of the inner ear structures can be reliably displayed. However, it is important to distinguish between truncation artifacts and true anatomical structures. Therefore, this study can be useful as a reference for image analysis.

Artificial Intelligence-Based Atrial Fibrillation Recognition Method for Motion Artifact-Contaminated Electrocardiogram Signals Preprocessed by Adaptive Filtering Algorithm.

Atrial fibrillation (AF) is a common arrhythmia, and out-of-hospital, wearable, long-term electrocardiogram (ECG) monitoring can help with the early detection of AF. The presence of a motion artifact (MA) in ECG can significantly affect the characteristics of the ECG signal and hinder early detection of AF. Studies have shown that (a) using reference signals with a strong correlation with MAs in adaptive filtering (ADF) can eliminate MAs from the ECG, and (b) artificial intelligence (AI) algorithms can recognize AF when there is no presence of MAs. However, no literature has been reported on whether ADF can improve the accuracy of AI for recognizing AF in the presence of MAs. Therefore, this paper investigates the accuracy of AI recognition for AF when ECGs are artificially introduced with MAs and processed by ADF. In this study, 13 types of MA signals with different signal-to-noise ratios ranging from +8 dB to -16 dB were artificially added to the AF ECG dataset. Firstly, the accuracy of AF recognition using AI was obtained for a signal with MAs. Secondly, after removing the MAs by ADF, the signal was further identified using AI to obtain the accuracy of the AF recognition. We found that after undergoing ADF, the accuracy of AI recognition for AF improved under all MA intensities, with a maximum improvement of 60%.

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

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

Unusual application of twinkling artifact.

INTRODUCTION: The aim of this paper is to show how to improve diagnostic accuracy using CDUS and twinkling artifact in patients experiencing discomfort due to the presence of small FBs in the soft tissues not clearly visible at US grayscale examination. MATERIALS AND METHODS: We enrolled 7 adult patients presenting with small (2-4 mm) superficial FBs located in the subcutaneous and muscle tissues, barely or not detectable on US grayscale. All patients underwent US grayscale and CDUS examinations. RESULTS: We identified superficial FB with twinkling artifact in all 7 patients. All of these were confirmed to represent foreign bodies after surgical excision. CONCLUSION: TA is useful in the evaluation of subcutaneous and muscular FBs and provides information on their location, depth and shape, which is useful if surgical excision is required.

Semantic Aware Stitching for Panorama.

The most critical aspect of panorama generation is maintaining local semantic consistency. Objects may be projected from different depths in the captured image. When warping the image to a unified canvas, pixels at the semantic boundaries of the different views are significantly misaligned. We propose two lightweight strategies to address this challenge efficiently. First, the original image is segmented as superpixels rather than regular grids to preserve the structure of each cell. We propose effective cost functions to generate the warp matrix for each superpixel. The warp matrix varies progressively for smooth projection, which contributes to a more faithful reconstruction of object structures. Second, to deal with artifacts introduced by stitching, we use a seam line method tailored to superpixels. The algorithm takes into account the feature similarity of neighborhood superpixels, including color difference, structure and entropy. We also consider the semantic information to avoid semantic misalignment. The optimal solution constrained by the cost functions is obtained under a graph model. The resulting stitched images exhibit improved naturalness. Extensive testing on common panorama stitching datasets is performed on the algorithm. Experimental results show that the proposed algorithm effectively mitigates artifacts, preserves the completeness of semantics and produces panoramic images with a subjective quality that is superior to that of alternative methods.

Improved multi-layer wavelet transform and blind source separation based ECG artifacts removal algorithm from the sEMG signal: in the case of upper limbs.

Introduction: Surface electromyogram (sEMG) signals have been widely used in human upper limb force estimation and motion intention recognition. However, the electrocardiogram(ECG) artifact generated by the beating of the heart is a major factor that reduces the quality of the EMG signal when recording the sEMG signal from the muscle close to the heart. sEMG signals contaminated by ECG artifacts are difficult to be understood correctly. The objective of this paper is to effectively remove ECG artifacts from sEMG signals by a novel method. Methods: In this paper, sEMG and ECG signals of the biceps brachii, brachialis, and triceps muscle of the human upper limb will be collected respectively. Firstly, an improved multi-layer wavelet transform algorithm is used to preprocess the raw sEMG signal to remove the background noise and power frequency interference in the raw signal. Then, based on the theory of blind source separation analysis, an improved Fast-ICA algorithm was constructed to separate the denoising signals. Finally, an ECG discrimination algorithm was used to find and eliminate ECG signals in sEMG signals. This method consists of the following steps: 1) Acquisition of raw sEMG and ECG signals; 2) Decoupling the raw sEMG signal; 3) Fast-ICA-based signal component separation; 4) ECG artifact recognition and elimination. Results and discussion: The experimental results show that our method has a good effect on removing ECG artifacts from contaminated EMG signals. It can further improve the quality of EMG signals, which is of great significance for improving the accuracy of force estimation and motion intention recognition tasks. Compared with other state-of-the-art methods, our method can also provide the guiding significance for other biological signals.

Evaluation of artifacts produced by conventional dental materials in standard and high-resolution CBCT imaging.

The aim of this study was to identify and quantify artifacts produced by commonly used dental restorative materials in both standard and high-resolution cone beam computed tomographic imaging. In this in vitro study, 25 different dental materials were placed in holes (3 mm in diameter × 2 mm thick) prepared in the center of 10 × 10-mm polymethyl methacrylate plates. The specimens, along with a control plate prepared with an unfilled hole, were scanned at standard and high resolutions. The gray values (GVs) of the specimens were measured at 1-, 2-, 4-, and 8-mm distances from the material surfaces, and in 8 different directions, resulting in 32 measurements per specimen. The absolute value of the difference (ΔGV) between the GV of each measurement point on the specimen disc and the GV of the corresponding point on the control disc was considered to be the number of artifacts at that point. The median ΔGV of each material was calculated, and the materials were then ranked in terms of artifact formation using the Kruskal-Wallis test. At standard resolution, the greatest numbers of artifacts were caused by AH 26 root canal sealer and Heraenium S nickel-chromium alloy, and the fewest were caused by Whitepost DC #3 glass fiber post and ChemFil Superior glass ionomer cement. At high resolution, the greatest numbers of artifacts were found in amalgam (admix; SDI) and Heraenium S, and the fewest in Whitepost DC and GC Initial enamel porcelain. The median ΔGV values at standard and high resolutions were 46.0 and 57.0, respectively. High and standard resolutions were significantly different in terms of artifact formation (P = 0.001; Wilcoxon test). AH 26 sealer was the only material that demonstrated a statistically significant reduction in artifact formation at high resolution compared with standard resolution (P = 0.05; Wilcoxon test). The number of artifacts produced by dental materials at both resolutions decreased with an increasing distance from the surface of the material.

Expanding the clinical application of OPM-MEG using an effective automatic suppression method for the dental brace metal artifact.

Optically pumped magnetometer magnetoencephalography (OPM-MEG) holds significant promise for clinical functional brain imaging due to its superior spatiotemporal resolution. However, effectively suppressing metallic artifacts, particularly from devices such as orthodontic braces and vagal nerve stimulators remains a major challenge, hindering the wider clinical application of wearable OPM-MEG devices. A comprehensive analysis of metal artifact characteristics from time, frequency, and time-frequency perspectives was conducted for the first time using an OPM-MEG device in clinical medicine. This study focused on patients with metal orthodontics, examining the modulation of metal artifacts by breath and head movement, the incomplete regular sub-Gaussian distribution, and the high absolute power ratio in the 0.5-8 Hz band. The existing metal artifact suppression algorithms applied to SQUID-MEG, such as fast independent component analysis (FastICA), information maximization (Infomax), and algorithms for multiple unknown signal extraction (AMUSE), exhibit limited efficacy. Consequently, this study introduced the second-order blind identification (SOBI) algorithm, which utilized multiple time delays for the component separation of OPM-MEG measurement signals. We modified the time delays of the SOBI method to improve its efficacy in separating artifact components, particularly those in the ultralow frequency range. This approach employs the frequency-domain absolute power ratio, root mean square (RMS) value, and mutual information methods to automate the artifact component screening process. The effectiveness of this method was validated through simulation experiments involving four subjects in both resting and evoked experiments. In addition, the proposed method was also validated by the actual OPM-MEG evoked experiments of three subjects. Comparative analyses were conducted against the FastICA, Infomax, and AMUSE algorithms. Evaluation metrics included normalized mean square error, normalized delta band power error, RMS error, and signal-to-noise ratio, demonstrating that the proposed method provides optimal suppression of metal artifacts. This advancement holds promise for enhancing data quality and expanding the clinical applications of OPM-MEG.

Dynamics of crevice microbubbles that cause the twinkling artifact.

The Doppler ultrasound twinkling artifact, a rapid color shift, appears on pathological mineralizations and is theorized to arise from scattering off micron-sized crevice microbubbles. However, the influence of crevice number and size as well as the bubble dynamics on twinkling is not well-understood. Cylinders with diameters of 0.8-1.2 µm and depths of 1 µm were etched into a silicon wafer and crevice bubbles were driven at 0.75, 2.5, and 5.0 MHz while monitoring with high-speed photography. Experimental results were compared to a derived crevice bubble model. On three separate wafers, cylindrical crevices (10 or 100) with diameters of 1, 10, or 100 µm and depths of 10 µm were etched and imaged with a research ultrasound system in Doppler mode at 5, 7.8, and 18.5 MHz. Within the pressure ranges studied here (∼1MPa), no bubble oscillation was observed for the 0.8-1.2 µm crevice bubbles which matched computational results. Crevices with 1 and 10 µm diameters produced more twinkling than 100 µm crevices at 5 and 7.8 MHz. In contrast, 100 µm crevices produced more twinkling than 1 or 10 µm crevices at 18.5 MHz (p < 0.001 in all cases). These results provide better insight into how crevice bubbles cause twinkling on pathological mineralizations.

A Novel Color Doppler Ultrasound Artifact for Identifying Breast and Axilla Tumor Markers: Blooming-like Artifact.

RATIONALE AND OBJECTIVES: Detection of tumor marker location in the breast and axilla using a less commonly utilized color Doppler ultrasound (CDUS) artifact. MATERIALS AND METHODS: This prospective study was conducted between August and December 2023 and included consecutive patients with markers placed in the breast and axilla, both before and after neoadjuvant chemotherapy (NACT). Examinations were conducted using a 14 to 5 MHz linear array transducer with B-mode and Doppler capability. By reducing the velocity scale and increasing the color gain values, adjustments were made to create a bloom-like artifact. CDUS was performed with the ultrasound transmit frequency set between 14-5 MHz, color frequency between 6 and 7, color gain ranging from 58 to 80, and velocity scale within the range of 4.6-6.1 cm/s. RESULTS: Twenty patients, with a mean age of 55.50 years ± 12.04 SD (range, 31-72), were included in the study. 14 (70%) were pre-NACT, and six (30%) were post-NACT patients. A total of 20 breast lesions and six axillary lymph nodes were marked. The breast lesions and axillary lymph nodes where the biopsy marker was placed (14 breast lesions and five axillary lymph nodes before NACT, six breast lesions and one axillary lymph node after NACT) were localized with blooming-like artifact. The average size of breast lesions was 20.95 mm ± 6.56 SD (range, 15-40). For axillary lymph nodes, the average size was 20.63 mm ± 5.01 SD (range: 31-14). CONCLUSION: The blooming-like CDUS artifact is a novel and easily applicable method determining the location of metallic markers in the breast and axilla on diagnostic examinations.

Iterative Motion Correction Technique with Deep Learning Reconstruction for Brain MRI: A Volunteer and Patient Study.

The aim of this study was to investigate the effect of iterative motion correction (IMC) on reducing artifacts in brain magnetic resonance imaging (MRI) with deep learning reconstruction (DLR). The study included 10 volunteers (between September 2023 and December 2023) and 30 patients (between June 2022 and July 2022) for quantitative and qualitative analyses, respectively. Volunteers were instructed to remain still during the first MRI with fluid-attenuated inversion recovery sequence (FLAIR) and to move during the second scan. IMCoff DLR images were reconstructed from the raw data of the former acquisition; IMCon and IMCoff DLR images were reconstructed from the latter acquisition. After registration of the motion images, the structural similarity index measure (SSIM) was calculated using motionless images as reference. For qualitative analyses, IMCon and IMCoff FLAIR DLR images of the patients were reconstructed and evaluated by three blinded readers in terms of motion artifacts, noise, and overall quality. SSIM for IMCon images was 0.952, higher than that for IMCoff images (0.949) (p < 0.001). In qualitative analyses, although noise in IMCon images was rated as increased by two of the three readers (both p < 0.001), all readers agreed that motion artifacts and overall quality were significantly better in IMCon images than in IMCoff images (all p < 0.001). In conclusion, IMC reduced motion artifacts in brain FLAIR DLR images while maintaining similarity to motionless images.

Analysis during chest compressions in out-of-hospital cardiac arrest patients, a cross/sectional study: The DEFI 2022 study.

AIMS: During out-of-hospital cardiac arrest (OHCA), an automatic external defibrillator (AED) analyzes the cardiac rhythm every two minutes; however, 80% of refibrillations occur within the first minute post-shock. We have implemented an algorithm for Analyzing cardiac rhythm While performing chest Compression (AWC). When AWC detects a shockable rhythm, it shortens the time between analyses to one minute. We investigated the effect of AWC on cardiopulmonary resuscitation quality. METHOD: In this cross-sectional study, we compared patients treated in 2022 with AWC, to a historical cohort from 2017. Inclusion criteria were OHCA patients with a shockable rhythm at the first analysis. Primary endpoint was the chest compression fraction (CCF). Secondary endpoints were cardiac rhythm evolution and survival, including survival analysis of non-prespecified subgroups. RESULTS: In 2017 and 2022, 355 and 377 OHCAs met the inclusion criteria, from which we analyzed the 285 first consecutive cases in each cohort. CCF increased in 2022 compared to 2017 (77% [72-80] vs 72% [67-76]; P < 0.001) and VF recurrences were shocked more promptly (53 s [32-69] vs 117 s [90-132]). Survival did not differ between 2017 and 2022 (adjusted hazard-ratio 0.96 [95% CI, 0.78-1.18]), but was higher in 2022 within the sub-group of OHCAs that occurred in a public place and within a short time from call to AED switch-on (adjusted hazard ratio 0.85[0.76-0.96]). CONCLUSIONS: OHCA patients treated with AWC had higher CCF, shorter time spent in ventricular fibrillation, but no survival difference, except for OHCA that occurred in public places with short intervention time.