[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.

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.

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.

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.

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.

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.

Diagnostic performance of advanced tomosynthesis in patients with metal devices in the affected knee: a case report.

Simple radiography is the most frequently and widely available technology to examine bone pathologies. Computed tomography (CT) can evaluate pathologies more accurately in multiple planes and three dimensions; however, radiation exposure is much higher than with simple radiography. In addition, diagnostic ability is decreased for both technologies when metal devices are present. Tomosynthesis is a radiographic technology used to evaluate tissues quasi-three-dimensionally with less radiation exposure. Tomosynthesis technology was recently upgraded to reduce the effects of metal artifacts. This case report compares examination time, medical expense, image resolution, and radiation exposure for upgraded tomosynthesis, simple radiography, CT, and standard tomosynthesis in three patients with metal devices in the affected knees. Examination times were similar for the imaging technologies. Diagnostic performance was better for upgraded tomosynthesis than for simple radiography and standard tomosynthesis, and similar to that for CT. Moreover, radiation exposure and expense were higher for tomosynthesis than for simple radiography but lower than for CT. These findings suggest that upgraded tomosynthesis is the best method for evaluating bone pathology when metal devices are present and radiation exposure must be limited.

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.

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.

Onset and progression of postmortem histological changes in the central nervous system of RccHan™: WIST rats.

Death initiates a cascade of physiological and biochemical alterations in organs and tissues, resulting in microscopic changes that challenge the histopathological evaluation. Moreover, the brain is particularly susceptible to artifacts owing to its unique composition and its location within the cranial vault. The aim of this study was to compile and illustrate the microscopic changes in the central nervous system (CNS) of rats subjected to delayed postmortem fixation. It also scrutinizes the influence of exsanguination and cooling methods on the initiation and progression of these alterations. Twenty-four Wistar Han outbred rats (RccHan™: WIST) were sacrificed and stored either at room temperature (18-22°C) or under refrigeration (2-4°C). Necropsies were conducted at different time points postmortem (i.e., 0.5 h, 1 h, 4 h, 8 h, 12 h, 24 h, 36 h, 48 h, 7 days and 14 days). Brain sections underwent simultaneous digital evaluation by 14 pathologists until a consensus was reached on terminology, key findings, and intensity levels. Microscopic observations varied among cell types. Glial cells were similarly affected throughout the CNS and showed pericellular halo, chromatin condensation and nuclear shrinkage. Neurons showed two types of postmortem changes as most of them showed progressive shrinkage, cytoplasmic dissolution and karyorrhexis whereas others acquired a dark-neuron-like appearance. Neuronal changes showed marked differences among neuroanatomical locations. Additional postmortem changes encompassed: granulation and microcavitation in neuropil and white matter; retraction spaces; detachment of ependyma, choroid plexus, and leptomeninges. Severity of findings after 48 h at room temperature was higher than after seven days under refrigeration and similar to or slightly lower than after 14 days under refrigeration. No clear differences were observed related to the sex or weight of the animals or their exsanguination status. This work elucidates the onset and progression of autolytic changes in the brains of Wistar Han rats, offering insights to accurately identify and enhance the histopathological evaluation.

PixelPrint: Generating Patient-Specific Phantoms for Spectral CT using Dual Filament 3D Printing.

In recent years, the importance of spectral CT scanners in clinical settings has significantly increased, necessitating the development of phantoms with spectral capabilities. This study introduces a dual-filament 3D printing technique for the fabrication of multi-material phantoms suitable for spectral CT, focusing particularly on creating realistic phantoms with orthopedic implants to mimic metal artifacts. Previously, we developed PixelPrint for creating patient-specific lung phantoms that accurately replicate lung properties through precise attenuation profiles and textures. This research extends PixelPrint's utility by incorporating a dual-filament printing approach, which merges materials such as calcium-doped Polylactic Acid (PLA) and metal-doped PLA, to emulate both soft tissue and bone, as well as orthopedic implants. The PixelPrint dual-filament technique utilizes an interleaved approach for material usage, whereby alternating lines of calcium-doped and metal-doped PLA are laid down. The development of specialized filament extruders and deposition mechanisms in this study allows for controlled layering of materials. The effectiveness of this technique was evaluated using various phantom types, including one with a dual filament orthopedic implant and another based on a human knee CT scan with a medical implant. Spectral CT scanner results demonstrated a high degree of similarity between the phantoms and the original patient scans in terms of texture, density, and the creation of realistic metal artifacts. The PixelPrint technology's ability to produce multi-material, lifelike phantoms present new opportunities for evaluating and developing metal artifact reduction (MAR) algorithms and strategies.

[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 preliminary study of super-resolution deep learning reconstruction with cardiac option for evaluation of endovascular-treated intracranial aneurysms.

OBJECTIVES: To investigate the usefulness of super-resolution deep learning reconstruction (SR-DLR) with cardiac option in the assessment of image quality in patients with stent-assisted coil embolization, coil embolization, and flow-diverting stent placement compared with other image reconstructions. METHODS: This single-center retrospective study included fifty patients (mean age, 59 years; range, 44-81 years; 13 men) who were treated with stent-assisted coil embolization, coil embolization, and flow-diverting stent placement between January and July 2023. The images were reconstructed using filtered back projection (FBP), hybrid iterative reconstruction (IR), and SR-DLR. The objective image analysis included image noise in the Hounsfield unit (HU), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and full width at half maximum (FWHM). Subjectively, two radiologists evaluated the overall image quality for the visualization of the flow-diverting stent, coil, and stent. RESULTS: The image noise in HU in SR-DLR was 6.99 ± 1.49, which was significantly lower than that in images reconstructed with FBP (12.32 ± 3.01) and hybrid IR (8.63 ± 2.12) (p < 0.001). Both the mean SNR and CNR were significantly higher in SR-DLR than in FBP and hybrid IR (p < 0.001 and p < 0.001). The FWHMs for the stent (p < 0.004), flow-diverting stent (p < 0.001), and coil (p < 0.001) were significantly lower in SR-DLR than in FBP and hybrid IR. The subjective visual scores were significantly higher in SR-DLR than in other image reconstructions (p < 0.001). CONCLUSIONS: SR-DLR with cardiac option is useful for follow-up imaging in stent-assisted coil embolization and flow-diverting stent placement in terms of lower image noise, higher SNR and CNR, superior subjective image analysis, and less blooming artifact than other image reconstructions. ADVANCES IN KNOWLEDGE: SR-DLR with cardiac option allow better visualization of the peripheral and smaller cerebral arteries. SR-DLR with cardiac option can be beneficial for CT imaging of stent-assisted coil embolization and flow-diverting stent.

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.

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.

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.