Search

Background Purpose Methods Results Conclusions Four‐dimensional computed tomography (4DCT) is an es sential tool in radiation therapy. However, the 4D acquisition process may cause motion artifacts which can obscure anatomy and distort functional measurements from CT scans.We describe a deep learning algorithm to identify the location of artifacts within 4DCT images. Our method is flexible enough to handle different types of artifacts, including duplication, misalignment, truncation, and interpolation.We trained and validated a U‐net convolutional neural network artifact detection model on more than 23 000 coronal slices extracted from 98 4DCT scans. The receiver operating characteristic (ROC) curve and precision‐recall curve were used to evaluate the model's performance at identifying artifacts compared to a manually identified ground truth. The model was adjusted so that the sensitivity in identifying artifacts was equivalent to that of a human observer, as measured by computing the average ratio of artifact volume to lung volume in a given scan.The model achieved a sensitivity, specificity, and precision of 0.78, 0.99, and 0.58, respectively. The ROC area‐under‐the‐curve (AUC) was 0.99 and the precision‐recall AUC was 0.73. Our model sensitivity is 8% higher than previously reported state‐of‐the‐art artifact detection methods.The model developed in this study is versatile, designed to handle duplication, misalignment, truncation, and interpolation artifacts within a single image, unlike earlier models that were designed for a single artifact type. [ABSTRACT FROM AUTHOR]

Motion artifacts can be a significant noise source in biomagnetic measurements when magnetic sensors are not separated from the signal source. In ambient environments, motion artifacts can be up to ten times stronger than the desired signals, varying with environmental conditions. This study evaluates the variability of these artifacts and the effectiveness of a gradiometer in reducing them in such settings. To achieve these objectives, we first measured the single channel output in varying magnetic field conditions to observe the effect of homogeneous and gradient background fields. Our analysis revealed that the variability in motion artifact within an ambient environment is primarily influenced by the gradient magnetic field rather than the homogeneous one. Subsequently, we configured a gradiometer in parallel and vertical alignment with the direction of vibration (X-axis). Our findings indicated that in a gradient background magnetic field ranging from 1 nT/mm to 10 nT/mm, the single-channel sensor output exhibited a change of 164.97 pT per mm unit increase, while the gradiometer output showed a change of only 0.75 pT/mm within the same range. Upon repositioning the gradiometer vertically (Y direction), perpendicular to the direction of vibration, the singlechannel output slope increased to 196.85 pT, whereas the gradiometer output only increased by 1.06 pT/mm for the same range. Our findings highlight the influence of ambient environments on motion artifacts and demonstrate the potential of gradiometers to mitigate these effects. In the future, we plan to record biomagnetic signals both inside and outside the shielded room to compare the efficacy of different gradiometer designs under varying environmental conditions. [ABSTRACT FROM AUTHOR]

Despite the progress in the development of innovative EEG acquisition systems, their use in dynamic applications is still limited by motion artifacts compromising the interpretation of the collected signals. Therefore, extensive research on the genesis of motion artifacts in EEG recordings is still needed to optimize existing technologies, shedding light on possible solutions to overcome the current limitations. We identified three potential sources of motion artifacts occurring at three different levels of a traditional biopotential acquisition chain: the skin-electrode interface, the connecting cables between the detection and the acquisition systems, and the electrode-amplifier system. The identified sources of motion artifacts were modelled starting from experimental observations carried out on EEG signals. Consequently, we designed customized EEG electrode systems aiming at experimentally disentangling the possible causes of motion artifacts. Both analytical and experimental observations indicated two main residual sites responsible for motion artifacts: the connecting cables between the electrodes and the amplifier and the sudden changes in electrode-skin impedance due to electrode movements. We concluded that further advancements in EEG technology should focus on the transduction stage of the biopotentials amplification chain, such as the electrode technology and its interfacing with the acquisition system. [ABSTRACT FROM AUTHOR]

Quality assessment (QA) of magnetic resonance imaging (MRI) encompasses several factors such as noise, contrast, homogeneity, and imaging artifacts. Quality evaluation is often not standardized and relies on the expertise, and vigilance of the personnel, posing limitations especially with large datasets. Machine learning based on convolutional neural networks (CNNs) is a promising approach to address these challenges by performing automated inspection of MR images. In this study, a CNN for the detection of random head motion artifacts (RHM) in T1-weighted MRI as one aspect of image quality is proposed. A two-step approach aimed to first identify images exhibiting pronounced motion artifacts, and second to evaluate the feasibility of a more detailed three-class classification. The utilized dataset consisted of 420 T1-weighted whole-brain image volumes with isotropic resolution. Human experts assigned each volume to one of three classes of artifact prominence. Results demonstrate an accuracy of 95% for the identification of images with pronounced artifact load. The addition of an intermediate class retained an accuracy of 76%. The findings highlight the potential of CNN-based approaches to increase the efficiency of post-hoc QAs in large datasets by flagging images with potentially relevant artifact loads for closer inspection. [ABSTRACT FROM AUTHOR]

The measurement accuracy of current wearable pulse sensors is grandly challenged by motion artifacts caused by body biomechanical activities. In this study, a honeycomb‐structure‐inspired wearable pulse sensor is reported which not only performs ambulant cardiovascular monitoring but also realizes biometric authentication utilizing the acquired individual pulse wave profiles. The sensor showcases an impressive sensitivity of 46.2 mV Pa−1, a swift response time of 21 ms, and exceptional durability (minimal degradation after 6000 cycles). For practical application in clinical settings, the sensor is able to record pulse signals continuously and accurately from individuals aged between 27 and 57 years, especially including a 29‐year‐old pregnant woman. Leveraging deep learning algorithms, the sensor further utilizes individual pulse wave profiles for biometric authentication, reaching a classification accuracy of up to 99.4%. The honeycomb‐structure‐inspired wearable pulse sensor marks a significant advancement in the field of practical cardiovascular monitoring and biometric authentication. [ABSTRACT FROM AUTHOR]

Monitoring heart rate (HR) through photoplethysmography (PPG) signals is a challenging task due to the complexities involved, even during routine daily activities. These signals can indeed be heavily contaminated by significant motion artifacts resulting from the subjects' movements, which can lead to inaccurate heart rate estimations. In this paper, our objective is to present an innovative necklace sensor that employs low-computational-cost algorithms for heart rate estimation in individuals performing non-abrupt movements, specifically drivers. Our solution facilitates the acquisition of signals with limited motion artifacts and provides acceptable heart rate estimations at a low computational cost. More specifically, we propose a wearable sensor necklace for assessing a driver's well-being by providing information about the driver's physiological condition and potential stress indicators through HR data. This innovative necklace enables real-time HR monitoring within a sleek and ergonomic design, facilitating seamless and continuous data gathering while driving. Prioritizing user comfort, the necklace's design ensures ease of wear, allowing for extended use without disrupting driving activities. The collected physiological data can be transmitted wirelessly to a mobile application for instant analysis and visualization. To evaluate the sensor's performance, two algorithms for estimating the HR from PPG signals are implemented in a microcontroller: a modified version of the mountaineer's algorithm and a sliding discrete Fourier transform. The goal of these algorithms is to detect meaningful peaks corresponding to each heartbeat by using signal processing techniques to remove noise and motion artifacts. The developed design is validated through experiments conducted in a simulated driving environment in our lab, during which drivers wore the sensor necklace. These experiments demonstrate the reliability of the wearable sensor necklace in capturing dynamic changes in HR levels associated with driving-induced stress. The algorithms integrated into the sensor are optimized for low computational cost and effectively remove motion artifacts that occur when users move their heads. [ABSTRACT FROM AUTHOR]

Significance: Motion artifacts in the signals recorded during optical fiber-based measurements can lead to misinterpretation of data. In this work, we address this problem during in-vivo rodent experiments and develop a motion artifacts correction (MAC) algorithm for single-fiber system (SFS) hemodynamics measurements from the brains of rodents. Aim: (i) To distinguish the effect of motion artifacts in the SFS signals. (ii) Develop a MAC algorithm by combining information from the experiments and simulations and validate it. Approach: Monte-Carlo (MC) simulations were performed across 450 to 790 nm to identify wavelengths where the reflectance is least sensitive to blood absorptionbased changes. This wavelength region is then used to develop a quantitative metric to measure motion artifacts, termed the dissimilarity metric (DM). We used MC simulations to mimic artifacts seen during experiments. Further, we developed a mathematical model describing light intensity at various optical interfaces. Finally, an MAC algorithm was formulated and validated using simulation and experimental data. Results: We found that the 670 to 680 nm wavelength region is relatively less sensitive to blood absorption. The standard deviation of DM (sDM) can measure the relative magnitude of motion artifacts in the SFS signals. The artifacts cause rapid shifts in the reflectance data that can be modeled as transmission changes in the optical lightpath. The changes observed during the experiment were found to be in agreement to those obtained from MC simulations. The mathematical model developed to model transmission changes to represent motion artifacts was extended to an MAC algorithm. TheMAC algorithmwas validated using simulations and experimental data. Conclusions: We distinguished motion artifacts from SFS signals during in vivo hemodynamic monitoring experiments. From simulation and experimental data, we showed that motion artifacts can be modeled as transmission changes. The developed MAC algorithm was shown to minimize artifactual variations in both simulation and experimental data. [ABSTRACT FROM AUTHOR]

ECG signals play a vital role in the diagnosis of cardiovascular conditions. However, they often suffer from the effects of various noise sources, including baseline wandering, respiratory artifact noise, power line interference and electrode motion artifacts. To overcome these challenges, it is imperative to implement low-frequency signal noise reduction strategies. Such strategies aim to significantly improve the quality of ECG signals, thus promoting more accurate and reliable diagnosis of cardiovascular disorders. This paper conducts a comparative analysis to assess the effectiveness of commonly used filtering and wavelet techniques in reducing Baseline Wander (BW) noise within ECG signals generated by the influence of breathing or electrode movements. It is common to observe the selection and evaluation of only one particular technique in the existing literature. In contrast, this study aims to provide a comprehensive comparative analysis, providing insight into the performance and relative merits of different techniques. Our research uses both filtering and Discrete Wavelet Transform (DWT) techniques in baseline noise removal. In this context, a reference point is established utilizing noise-free signals and a meticulous investigation of the wavelet-based approach that most effectively eliminates the resulting noise is provided. Subsequently, we assess the reference input and output signal via Signal-to-Noise Ratio (SNR) and Kolmogorov–Smirnov statistical test measurements. The most important contribution of this work to the scientific community resides in the comprehensive examination of IIR/FIR-based and wavelet method-based filtering methods capable of yielding the highest SNR levels across various ECG signals with various types of BW noise. Additionally, the effectiveness of the Chebychev-II filter in BW noise removal is highlighted. Our study was conducted using the MATLAB platform and code command lines were shared to facilitate the reproduction of our study by other researchers. It is considered that this study will be an important reference in the selection of effective techniques for removing BW noise within ECG signals. [ABSTRACT FROM AUTHOR]

The optical brain imaging method functional near-infrared spectroscopy (fNIRS) is a promising tool for real-time applications such as neurofeedback and brain-computer interfaces. Its combination of spatial specificity and mobility makes it particularly attractive for clinical use, both at the bedside and in patients' homes. Despite these advantages, optimizing fNIRS for real-time use requires careful attention to two key aspects: ensuring good spatial specificity and maintaining high signal quality. While fNIRS detects superficial cortical brain regions, consistently and reliably targeting specific regions of interest can be challenging, particularly in studies that require repeated measurements. Variations in cap placement coupled with limited anatomical information may further reduce this accuracy. Furthermore, it is important tomaintain good signal quality in real-time contexts to ensure that they reflect the true underlying brain activity. However, fNIRS signals are susceptible to contamination by cerebral and extracerebral systemic noise as well as motion artifacts. Insufficient real-time preprocessing can therefore cause the system to run on noise instead of brain activity. The aim of this review article is to help advance the progress of fNIRS-based real-time applications. It highlights the potential challenges in improving spatial specificity and signal quality, discusses possible options to overcome these challenges, and addresses further considerations relevant to real-time applications. By addressing these topics, the article aims to help improve the planning and execution of future real-time studies, thereby increasing their reliability and repeatability. [ABSTRACT FROM AUTHOR]

Optical tracking of head pose via fiducial markers has been proven to enable effective correction of motion artifacts in the brain during magnetic resonance imaging but remains difficult to implement in the clinic due to lengthy calibration and set up times. Advances in deep learning for markerless head pose estimation have yet to be applied to this problem because of the sub-millimetre spatial resolution required for motion correction. In the present work, two optical tracking systems are described for the development and training of a neural network: one marker-based system (a testing platform for measuring ground truth head pose) with high tracking fidelity to act as the training labels, and one markerless deep-learning-based system using images of the markerless head as input to the network. The markerless system has the potential to overcome issues of marker occlusion, insufficient rigid attachment of the marker, lengthy calibration times, and unequal performance across degrees of freedom (DOF), all of which hamper the adoption of marker-based solutions in the clinic. Detail is provided on the development of a custom moiré-enhanced fiducial marker for use as ground truth and on the calibration procedure for both optical tracking systems. Additionally, the development of a synthetic head pose dataset is described for the proof of concept and initial pre-training of a simple convolutional neural network. Results indicate that the ground truth system has been sufficiently calibrated and can track head pose with an error of <1 mm and <1°. Tracking data of a healthy, adult participant are shown. Pre-training results show that the average root-mean-squared error across the 6 DOF is 0.13 and 0.36 (mm or degrees) on a head model included and excluded from the training dataset, respectively. Overall, this work indicates excellent feasibility of the deep-learning-based approach and will enable future work in training and testing on a real dataset in the MRI environment. [ABSTRACT FROM AUTHOR]

Previous research compared the motion artifacts under tracking and fixating conditions with moving stimulus and suggested that shorter duty ratios resulted in enhanced display quality by minimize motion blur in tracking condition (Hoffman et al, 2014). However, in real‐world, interactions between eye movements and stimulus motion are more intricate than their experimental setups. We investigated the interaction further by employing various eye movement and stimulus motion combinations: A. Smooth Pursuit + Moving stimulus, B. Smooth Pursuit + Stationary stimulus, C. Fixation + Moving stimulus, D. Saccade + Stationary stimulus, and E. Saccade + Moving stimulus. Participants compared short duty ratio (10%) and a long duty ratio (90%) stimuli at 60 Hz to a reference stimulus of a 100% duty ratio at 1440 Hz. Participants perceived the short duty ratio stimulus as more closely resembling the reference stimulus in condition A as in the previous study. Interestingly, in conditions B and E, participants reported the long‐duty ratio stimulus is more similar to the reference stimulus. There was no significant difference between short and long duty ratios in conditions C and D. These findings challenge the conventional assumption that less motion blur (associated with short duty ratio) is inherently more natural and suggest that even strong motion blur (associated with long duty ratio) can be perceived as more natural under specific conditions. This study underscores the importance of considering the complex interactions between eye movements, duty ratio, and stimulus motion when evaluating display image quality. [ABSTRACT FROM AUTHOR]

Motion artifacts can be a significant noise source in biomagnetic measurements when magnetic sensors are not separated from the signal source. In ambient environments, motion artifacts can be up to ten times stronger than the desired signals, varying with environmental conditions. This study evaluates the variability of these artifacts and the effectiveness of a gradiometer in reducing them in such settings. To achieve these objectives, we first measured the single channel output in varying magnetic field conditions to observe the effect of homogeneous and gradient background fields. Our analysis revealed that the variability in motion artifact within an ambient environment is primarily influenced by the gradient magnetic field rather than the homogeneous one. Subsequently, we configured a gradiometer in parallel and vertical alignment with the direction of vibration (X-axis). Our findings indicated that in a gradient background magnetic field ranging from 1 nT/mm to 10 nT/mm, the single-channel sensor output exhibited a change of 164.97 pT per mm unit increase, while the gradiometer output showed a change of only 0.75 pT/mm within the same range. Upon repositioning the gradiometer vertically (Y direction), perpendicular to the direction of vibration, the single-channel output slope increased to 196.85 pT, whereas the gradiometer output only increased by 1.06 pT/mm for the same range. Our findings highlight the influence of ambient environments on motion artifacts and demonstrate the potential of gradiometers to mitigate these effects. In the future, we plan to record biomagnetic signals both inside and outside the shielded room to compare the efficacy of different gradiometer designs under varying environmental conditions.

Optical mapping is a widely used tool to record and visualize the electrophysiological properties in a variety of myocardial preparations such as Langendorff-perfused isolated hearts, coronary-perfused wedge preparations, and cell culture monolayers. Motion artifact originating from the mechanical contraction of the myocardium creates a significant challenge to performing optical mapping of contracting hearts. Hence, to minimize the motion artifact, cardiac optical mapping studies are mostly performed on non-contracting hearts, where the mechanical contraction is removed using pharmacological excitation-contraction uncouplers. However, such experimental preparations eliminate the possibility of electromechanical interaction, and effects such as mechano-electric feedback cannot be studied. Recent developments in computer vision algorithms and ratiometric techniques have opened the possibility of performing optical mapping studies on isolated contracting hearts. In this review, we discuss the existing techniques and challenges of optical mapping of contracting hearts.

BackgroundMotion during data acquisition leads to artifacts in computed tomography (CT) reconstructions. In cases such as cardiac imaging, not only is motion unavoidable, but evaluating the motion of the object is of clinical interest. Reducing motion artifacts has typically been achieved by developing systems with faster gantry rotation or via algorithms which measure and/or estimate the displacement. However, these approaches have had limited success due to both physical constraints as well as the challenge of estimating non-rigid, temporally varying, and patient-specific motion fields.PurposeTo develop a novel reconstruction method which generates time-resolved, artifact-free images without estimation or explicit modeling of the motion.MethodsWe describe an analysis-by-synthesis approach which progressively regresses a solution consistent with the acquired sinogram. In our method, we focus on the movement of object boundaries. Not only are the boundaries the source of image artifacts, but object boundaries can simultaneously be used to represent both the object as well as its motion over time without the need for an explicit motion model. We represent the object boundaries via a signed distance function (SDF) which can be efficiently modeled using neural networks. As a result, optimization can be performed under spatial and temporal smoothness constraints without the need for explicit motion estimation.ResultsWe illustrate the utility of DiFiR-CT in three imaging scenarios with increasing motion complexity: translation of a small circle, heart-like change in an ellipse's diameter, and a complex topological deformation. Compared to filtered backprojection, DiFiR-CT provides high quality image reconstruction for all three motions without hyperparameter tuning or change to the architecture. We also evaluate DiFiR-CT's robustness to noise in the acquired sinogram and found its reconstruction to be accurate across a wide range of noise levels. Lastly, we demonstrate how the approach could be used for multi-intensity scenes and illustrate the importance of the initial segmentation providing a realistic initialization. Code and supplemental movies are available at https://kunalmgupta.github.io/projects/DiFiR-CT.html.ConclusionsProjection data can be used to accurately estimate a temporally-evolving scene without the need for explicit motion estimation using a neural implicit representation and analysis-by-synthesis approach.

Abstract Brain age algorithms using data science and machine learning techniques show promise as biomarkers for neurodegenerative disorders and aging. However, head motion during MRI scanning may compromise image quality and influence brain age estimates. We examined the effects of motion on brain age predictions in adult participants with low, high, and no motion MRI scans (Original N = 148; Analytic N = 138). Five popular algorithms were tested: brainageR, DeepBrainNet, XGBoost, ENIGMA, and pyment. Evaluation metrics, intraclass correlations (ICCs), and Bland–Altman analyses assessed reliability across motion conditions. Linear mixed models quantified motion effects. Results demonstrated motion significantly impacted brain age estimates for some algorithms, with ICCs dropping as low as 0.609 and errors increasing up to 11.5 years for high motion scans. DeepBrainNet and pyment showed greatest robustness and reliability (ICCs = 0.956–0.965). XGBoost and brainageR had the largest errors (up to 13.5 RMSE) and bias with motion. Findings indicate motion artifacts influence brain age estimates in significant ways. Furthermore, our results suggest certain algorithms like DeepBrainNet and pyment may be preferable for deployment in populations where motion during MRI acquisition is likely. Further optimization and validation of brain age algorithms is critical to use brain age as a biomarker relevant for clinical outcomes.

Wearable EEG suffers from motion artifact contamination due to the subject's movement in an ambulatory environment. Signal processing techniques pose promising solutions for the detection and removal of motion artifacts from ambulatory EEG, but relevant open-access datasets are not available, which is detrimental to the development of wearable EEG applications. This article showcases open-access electroencephalography (EEG) recordings, while a subject is performing different upper-body, lower-body, and full-body movements. One healthy male subject volunteered to record his EEG data using a 14-channel EMOTIV EPOCH EEG headset device. This device's electrode placement is in accordance with the international 10–20 system, and the data was stored using the EMOTIV Pro application. We used the MATLAB software to visualize the captured brain waveforms. The venue of the data collection was the Biomedical Instrumentation and Signal Processing Laboratory (BISPL) at the Independent University, Bangladesh (IUB). The EMOTIV Pro application extracted the recorded EEG data in the CSV file format, while the MATLAB software converted it to a .mat extension file afterward. The first 14 columns of this file represent the 14-channel EEG data, and the subsequent nine columns are for the motion sensor data. The list of recorded movements includes blinking of eyes, eyebrow movement, and also horizontal and vertical eye movements. Afterward, the head shook and nodded. Later, the leg trembled, followed by listening to music, talking, walking, and standing and sitting down. Before the recording ended, the subject relaxed on a chair with both eyes open and closed. This dataset is one of its kind, allowing us to explore further research for wearable EEG while denoising motion artifacts arising from subject movement.

Background: 4D CT imaging is an essential component of radiotherapy of thoracic and abdominal tumors. 4D CT images are, however, often affected by artifacts that compromise treatment planning quality and image information reliability. Purpose: In this work, deep learning (DL)‐based conditional inpainting is proposed to restore anatomically correct image information of artifact‐affected areas. Methods: The restoration approach consists of a two‐stage process: DL‐based detection of common interpolation (INT) and double structure (DS) artifacts, followed by conditional inpainting applied to the artifact areas. In this context, conditional refers to a guidance of the inpainting process by patient‐specific image data to ensure anatomically reliable results. The study is based on 65 in‐house 4D CT images of lung cancer patients (48 with only slight artifacts, 17 with pronounced artifacts) and two publicly available 4D CT data sets that serve as independent external test sets. Results: Automated artifact detection revealed a ROC‐AUC of 0.99 for INT and of 0.97 for DS artifacts (in‐house data). The proposed inpainting method decreased the average root mean squared error (RMSE) by 52 % (INT) and 59 % (DS) for the in‐house data. For the external test data sets, the RMSE improvement is similar (50 % and 59 %, respectively). Applied to 4D CT data with pronounced artifacts (not part of the training set), 72 % of the detectable artifacts were removed. Conclusions: The results highlight the potential of DL‐based inpainting for restoration of artifact‐affected 4D CT data. Compared to recent 4D CT inpainting and restoration approaches, the proposed methodology illustrates the advantages of exploiting patient‐specific prior image information. [ABSTRACT FROM AUTHOR]

Brain age algorithms using data science and machine learning techniques show promise as biomarkers for neurodegenerative disorders and aging. However, head motion during MRI scanning may compromise image quality and influence brain age estimates. We examined the effects of motion on brain age predictions in adult participants with low, high, and no motion MRI scans (Original N = 148; Analytic N = 138). Five popular algorithms were tested: brainageR, DeepBrainNet, XGBoost, ENIGMA, and pyment. Evaluation metrics, intraclass correlations (ICCs), and Bland–Altman analyses assessed reliability across motion conditions. Linear mixed models quantified motion effects. Results demonstrated motion significantly impacted brain age estimates for some algorithms, with ICCs dropping as low as 0.609 and errors increasing up to 11.5 years for high motion scans. DeepBrainNet and pyment showed greatest robustness and reliability (ICCs = 0.956–0.965). XGBoost and brainageR had the largest errors (up to 13.5 RMSE) and bias with motion. Findings indicate motion artifacts influence brain age estimates in significant ways. Furthermore, our results suggest certain algorithms like DeepBrainNet and pyment may be preferable for deployment in populations where motion during MRI acquisition is likely. Further optimization and validation of brain age algorithms is critical to use brain age as a biomarker relevant for clinical outcomes. [ABSTRACT FROM AUTHOR]

Recent years have witnessed the publication of many research articles regarding the contactless measurement and monitoring of heart rate signals deduced from facial video recordings. The techniques presented in these articles, such as examining the changes in the heart rate of an infant, provide a noninvasive assessment in many cases where the direct placement of any hardware equipment is undesirable. However, performing accurate measurements in cases that include noise motion artifacts still presents an obstacle to overcome. In this research article, a two-stage method for noise reduction in facial video recording is proposed. The first stage of the system consists of dividing each (30) seconds of the acquired signal into (60) partitions and then shifting each partition to the mean level before recombining them to form the estimated heart rate signal. The second stage utilizes the wavelet transform for denoising the signal obtained from the first stage. The denoised signal is compared to a reference signal acquired from a pulse oximeter, resulting in the mean bias error (0.13), root mean square error (3.41) and correlation coefficient (0.97). The proposed algorithm is applied to (33) individuals being subjected to a normal webcam for acquiring their video recording, which can easily be performed at homes, hospitals, or any other environment. Finally, it is worth noting that this noninvasive remote technique is useful for acquiring the heart signal while preserving social distancing, which is a desirable feature in the current period of COVID-19. [ABSTRACT FROM AUTHOR]

Objective: To investigate the feasibility and image quality of high-pitch CT pulmonary angiography (CTPA) with reduced iodine volume in normal weight patients. Methods: In total, 81 normal weight patients undergoing CTPA for suspected pulmonary arterial embolism were retrospectively included: 41 in high-pitch mode with 20 mL of contrast medium (CM); and 40 with normal pitch and 50 mL of CM. Subjective image quality was assessed and rated on a 3-point scale. For objective image quality, attenuation and noise values were measured in all pulmonary arteries from the trunk to segmental level. Contrast-to-noise ratio (CNR) was calculated. Radiation dose estimations were recorded. Results: There were no statistically significant differences in patient and scan demographics between high-pitch and standard CTPA. Subjective image quality was rated good to excellent in over 90% of all exams with no significant group differences (p = 0.32). Median contrast opacification was lower in high-pitch CTPA (283.18 [216.06–368.67] HU, 386.81 [320.57–526.12] HU; p = 0.0001). CNR reached a minimum of eight in all segmented arteries, but was lower in high-pitch CTPA (8.79 [5.82–12.42], 11.01 [9.19–17.90]; p = 0.005). Median effective dose of high-pitch CTPA was lower (1.04 [0.72–1.27] mSv/mGy·cm; 1.49 [1.07–2.05] mSv/mGy·cm; p < 0.0001). Conclusion: High-pitch CTPA using ultra-low contrast volume (20 mL) rendered diagnostic images for the detection of pulmonary arterial embolism in most instances. Compared to standard CTPA, the high-pitch CTPA exams with drastically reduced contrast medium volume had also concomitantly reduced radiation exposure. However, objective image quality of high-pitch CTPA was worse, though likely still within acceptable limits for confident diagnosis. Clinical relevance: This study provides valuable insights on the performance of a high-pitch dual-source CTPA protocol, offering potential benefits in reducing contrast medium and radiation dose while maintaining sufficient image quality for accurate diagnosis in patients suspected of pulmonary embolism. Key Points: • High-pitch CT pulmonary angiography (CTPA) with ultra-low volume of contrast medium and reduced radiation dose renders diagnostic examinations with comparable subjective image quality to standard CTPA in most patients. • Objective image quality of high-pitch CTPA is reduced compared to standard CTPA, but contrast opacification and contrast-to-noise ratio remain above diagnostic thresholds. • Challenges of high-pitch CTPA may potentially be encountered in patients with severe heart failure or when performing a Valsalva maneuver during the examination. [ABSTRACT FROM AUTHOR]

The breathing and suspended inspiration techniques are often used interchangeably for spine X-ray examinations. However, these techniques are not always adequately supported by clinical evidence. This study aimed to determine the two techniques' diagnostic value and adverse image outcomes. A total of 400 participants were examined on a Siemens Ysio Max system and randomized into four examination groups: suspended inspiration or breathing techniques with exposure times of 1, 2, and 3.2 s, respectively. Two consultant radiologists conducted the evaluation of the X-ray images. If disagreement was present, the radiologists collaboratively reviewed the X-ray images until a consensus was reached. The final 394 study population comprised 275 women and 119 men with a mean age of 64 years (range:18–96 years). The proportions of visually sharp reproduction of the endplates and trabecular structures did not differ significantly with regards to differences in exposure times between groups. The breathing technique groups had significantly higher proportions of blurring and motion artifacts (p < 0.001). However, adverse image outcomes (motions artifacts) were significantly lower in the 1-s exposure group. The suspended inspiration and breathing techniques performed equally well regarding visually sharp reproduction. However, the suspended inspiration technique was superior to the breathing technique. regarding adverse image outcomes, although the latter could be improved by using a shorter exposure time. The suspended inspiration and breathing technique appeared to perform at equal diagnostic levels. The suspended inspiration technique should be preferred due to its reduced risk of adverse image outcomes. However, the risk could also be reduced using a short exposure time with the breathing technique. [ABSTRACT FROM AUTHOR]

Among its main benefits, telemonitoring enables personalized management of chronic diseases by means of biomarkers extracted from signals. In these applications, a thorough quality assessment is required to ensure the reliability of the monitored parameters. Motion artifacts are a common problem in recordings with wearable devices. In this work, we propose a fully automated and personalized method to detect motion artifacts in multimodal recordings devoted to the monitoring of the Cardiac Time Intervals (CTIs). The detection of motion artifacts was carried out by using template matching with a personalized template. The method yielded a balanced accuracy of 86%. Moreover, it proved effective to decrease the variability of the estimated CTIs by at least 17%. Our preliminary results show that personalized detection of motion artifacts improves the robustness of the assessment CTIs and opens to the use in wearable systems. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the application of label propagation techniques to propagate labels among photoplethysmogram (PPG) signals, particularly in imbalanced class scenarios and limited data availability scenarios, where clean PPG samples are significantly outnumbered by artifact-contaminated samples. We investigated a dataset comprising PPG recordings from 1571 patients, wherein approximately 82% of the samples were identified as clean, while the remaining 18% were contaminated by artifacts. Our research compares the performance of supervised classifiers, such as conventional classifiers and neural networks (Multi-Layer Perceptron (MLP), Transformers, Fully Convolutional Network (FCN)), with the semi-supervised Label Propagation (LP) algorithm for artifact classification in PPG signals. The results indicate that the LP algorithm achieves a precision of 91%, a recall of 90%, and an F1 score of 90% for the “artifacts” class, showcasing its effectiveness in annotating a medical dataset, even in cases where clean samples are rare. Although the K-Nearest Neighbors (KNN) supervised model demonstrated good results with a precision of 89%, a recall of 95%, and an F1 score of 92%, the semi-supervised algorithm excels in artifact detection. In the case of imbalanced and limited pediatric intensive care environment data, the semi-supervised LP algorithm is promising for artifact detection in PPG signals. The results of this study are important for improving the accuracy of PPG-based health monitoring, particularly in situations in which motion artifacts pose challenges to data interpretation.

Despite the progress in the development of innovative EEG acquisition systems, their use in dynamic applications is still limited by motion artifacts compromising the interpretation of the collected signals. Therefore, extensive research on the genesis of motion artifacts in EEG recordings is still needed to optimize existing technologies, shedding light on possible solutions to overcome the current limitations. We identified three potential sources of motion artifacts occurring at three different levels of a traditional biopotential acquisition chain: the skin-electrode interface, the connecting cables between the detection and the acquisition systems, and the electrode-amplifier system. The identified sources of motion artifacts were modelled starting from experimental observations carried out on EEG signals. Consequently, we designed customized EEG electrode systems aiming at experimentally disentangling the possible causes of motion artifacts. Both analytical and experimental observations indicated two main residual sites responsible for motion artifacts: the connecting cables between the electrodes and the amplifier and the sudden changes in electrode-skin impedance due to electrode movements. We concluded that further advancements in EEG technology should focus on the transduction stage of the biopotentials amplification chain, such as the electrode technology and its interfacing with the acquisition system.

Monitoring heart rate (HR) through photoplethysmography (PPG) signals is a challenging task due to the complexities involved, even during routine daily activities. These signals can indeed be heavily contaminated by significant motion artifacts resulting from the subjects’ movements, which can lead to inaccurate heart rate estimations. In this paper, our objective is to present an innovative necklace sensor that employs low-computational-cost algorithms for heart rate estimation in individuals performing non-abrupt movements, specifically drivers. Our solution facilitates the acquisition of signals with limited motion artifacts and provides acceptable heart rate estimations at a low computational cost. More specifically, we propose a wearable sensor necklace for assessing a driver’s well-being by providing information about the driver’s physiological condition and potential stress indicators through HR data. This innovative necklace enables real-time HR monitoring within a sleek and ergonomic design, facilitating seamless and continuous data gathering while driving. Prioritizing user comfort, the necklace’s design ensures ease of wear, allowing for extended use without disrupting driving activities. The collected physiological data can be transmitted wirelessly to a mobile application for instant analysis and visualization. To evaluate the sensor’s performance, two algorithms for estimating the HR from PPG signals are implemented in a microcontroller: a modified version of the mountaineer’s algorithm and a sliding discrete Fourier transform. The goal of these algorithms is to detect meaningful peaks corresponding to each heartbeat by using signal processing techniques to remove noise and motion artifacts. The developed design is validated through experiments conducted in a simulated driving environment in our lab, during which drivers wore the sensor necklace. These experiments demonstrate the reliability of the wearable sensor necklace in capturing dynamic changes in HR levels associated with driving-induced stress. The algorithms integrated into the sensor are optimized for low computational cost and effectively remove motion artifacts that occur when users move their heads.

Optical coherence tomography angiography (OCTA) is a popular technique for imaging microvascular networks, but OCTA image quality is commonly affected by motion artifacts. Deep learning (DL) has been used to generate OCTA images from structural OCT images, yet limitations persist, such as low label image quality caused by motion and insufficient use of neighborhood information. In this study, an attention-based U-Net incorporating both repeated and adjacent structural OCT images in network input and high-quality label OCTA images in training was proposed to generate high-quality OCTA images with motion artifact suppression. A sliding-window correlation-based adjacent position (SWCB-AP) image fusion method was proposed to generate high-quality OCTA label images with suppressed motion noise. Six different DL schemes with various configurations of network inputs and label images were compared to demonstrate the superiority of the proposed method. Motion artifact severity was evaluated by a motion noise index in B-scan (MNI-B) and in en-face (MNI-C) OCTA images, which were specifically defined in this study for the purpose of evaluating various DL models' capability in motion noise suppression. Experimental results on a nailfold OCTA image dataset showed that the proposed DL method generated the best results with a peak signal-to-noise ratio (PSNR) of 32.666 ± 7.010 dB, structural similarity (SSIM) of 0.926 ± 0.051, mean absolute error (MAE) of 1.798 ± 1.575, and MNI-B of 0.528 ± 0.124 in B-scan OCTA images and a contrast-to-noise ratio (CNR) of 1.420 ± 0.291 and MNI-C of 0.156 ± 0.057 in en-face OCTA images. Our proposed DL approach generated OCTA images with improved blood flow contrast and reduced motion artifacts, which could be used as a fundamental signal processing module in generating high-quality OCTA images from structural OCT images. [ABSTRACT FROM AUTHOR]

Four‐dimensional computed tomography (4DCT), which relies on breathing‐induced motion, requires realistic surrogate information of breathing variations to reconstruct the tumor trajectory and motion variability of normal tissues accurately. Therefore, the SimRT surface‐guided respiratory monitoring system has been installed on a Siemens CT scanner. This work evaluated the temporal and spatial accuracy of SimRT versus our commonly used pressure sensor, AZ‐733 V. A dynamic thorax phantom was used to reproduce regular and irregular breathing patterns acquired by SimRT and Anzai. Various parameters of the recorded breathing patterns, including mean absolute deviations (MAD), Pearson correlations (PC), and tagging precision, were investigated and compared to ground‐truth. Furthermore, 4DCT reconstructions were analyzed to assess the volume discrepancy, shape deformation and tumor trajectory. Compared to the ground‐truth, SimRT more precisely reproduced the breathing patterns with a MAD range of 0.37 ± 0.27 and 0.92 ± 1.02 mm versus Anzai with 1.75 ± 1.54 and 5.85 ± 3.61 mm for regular and irregular breathing patterns, respectively. Additionally, SimRT provided a more robust PC of 0.994 ± 0.009 and 0.936 ± 0.062 for all investigated breathing patterns. Further, the peak and valley recognition were found to be more accurate and stable using SimRT. The comparison of tumor trajectories revealed discrepancies up to 7.2 and 2.3 mm for Anzai and SimRT, respectively. Moreover, volume discrepancies up to 1.71 ± 1.62% and 1.24 ± 2.02% were found for both Anzai and SimRT, respectively. SimRT was validated across various breathing patterns and showed a more precise and stable breathing tracking, (i) independent of the amplitude and period, (ii) and without placing any physical devices on the patient's body. These findings resulted in a more accurate temporal and spatial accuracy, thus leading to a more realistic 4DCT reconstruction and breathing‐adapted treatment planning. [ABSTRACT FROM AUTHOR]

Epilepsy, a prevalent neurological disorder, necessitates precise and reliable electrophysiological recording for accurate study and diagnosis. Although traditional stereo‐electroencephalography and micro‐wire probes are widely used in epilepsy research, they are limited by recording site numbers and mechanical properties. This study explores the use of high‐density, ultra‐flexible neural probes in epilepsy monitoring, highlighting their advantages in terms of recording performance, scalability, and tissue compatibility. This work validates the effectiveness of the probes in detecting characteristic epileptic signals across various stages, including resting, preictal, and ictal stages, in two different mouse models of epilepsy. Additionally, the high spatial resolution of the probes allows to capture fine spatial propagation of epilepsy‐associated high‐frequency oscillations. Furthermore, the flexible probes exhibit superior biocompatibility, reducing inflammation and neuronal apoptosis compared to rigid electrodes. The results underscore the promising potential of ultra‐flexible neural probes for advancing epilepsy research, providing a powerful technological platform for future studies in the field. [ABSTRACT FROM AUTHOR]

In-vivo bone microstructure measured by high-resolution peripheral quantitative computed tomography (HR-pQCT) is gaining importance in research and clinical practice. Second-generation HR-pQCT (XCT2) shows improved image quality and shorter measurement duration compared to the first generation (XCT1). Predicting and understanding the occurrence of motion artifacts is crucial for clinical practice. We retrospectively analyzed data from HR-pQCT measurements at the distal radius and tibia of 1,000 patients (aged 20 to 89) evenly distributed between both generations of HR-pQCT. Motion artifacts were graded between 1 (no motion) and 5 (severe motion), with grades greater 3 considered unusable. Additionally, baseline characteristics and patients' muscle performance and balance were measured. Various group comparisons between the two generations of HR-pQCT and regression analyses between patient characteristics and motion grading were performed. The study groups of XCT1 and XCT2 did not differ by age (XCT1: 64.9 vs. XCT2: 63.8 years, p = 0.136), sex (both 74.5% females, p > 0.999), or BMI (both 24.2 kg/m2, p = 0.911) after propensity score matching. XCT2 scans exhibited significantly lower motion grading in both extremities compared to XCT1 (Radius: p < 0.001; Tibia: p = 0.002). In XCT2 motion-corrupted scans were more than halved at the radius (XCT1: 35.3% vs. XCT2: 15.5%, p < 0.001), and at the tibia the frequency of best image quality scans was increased (XCT1: 50.2% vs. XCT2: 63.7%, p < 0.001). The strongest independent predictor for motion-corrupted images is the occurrence of high motion grading at the other scanning site during the same consultation. The association between high motion grading in one scan and a corresponding high motion grading in another scan within the same session suggests a non-resting patient. Additionally, aged, female, and patients with smaller stature tend towards higher motion grading, requiring special attention to a correct extremity fixation. [ABSTRACT FROM AUTHOR]

Accurate peak determination from noise-corrupted photoplethysmogram (PPG) signal is the basis for further analysis of physiological quantities such as heart rate. Conventional methods are designed for noise-free PPG signals and are insufficient for PPG signals with low signal-to-noise ratio (SNR). This paper focuses on enhancing PPG noise-resiliency and proposes a robust peak detection algorithm for PPG signals distorted due to noise and motion artifact. Our algorithm is based on convolutional neural networks (CNNs) with dilated convolutions. We train and evaluate the proposed method using a dataset collected via smartwatches under free-living conditions in a home-based health monitoring application. A data generator is also developed to produce noisy PPG data used for model training and evaluation. The method performance is compared against other state-of-the-art methods and is tested with SNRs ranging from 0 to 45 dB. Our method outperforms the existing adaptive threshold, transform-based, and machine learning methods. The proposed method shows overall precision, recall, and F1-score of 82%, 80%, and 81% in all the SNR ranges. In contrast, the best results obtained by the existing methods are 78%, 80%, and 79%. The proposed method proves to be accurate for detecting PPG peaks even in the presence of noise.

Optical tracking of head pose via fiducial markers has been proven to enable effective correction of motion artifacts in the brain during magnetic resonance imaging but remains difficult to implement in the clinic due to lengthy calibration and set up times. Advances in deep learning for markerless head pose estimation have yet to be applied to this problem because of the sub-millimetre spatial resolution required for motion correction. In the present work, two optical tracking systems are described for the development and training of a neural network: one marker-based system (a testing platform for measuring ground truth head pose) with high tracking fidelity to act as the training labels, and one markerless deep-learning-based system using images of the markerless head as input to the network. The markerless system has the potential to overcome issues of marker occlusion, insufficient rigid attachment of the marker, lengthy calibration times, and unequal performance across degrees of freedom (DOF), all of which hamper the adoption of marker-based solutions in the clinic. Detail is provided on the development of a custom moiré-enhanced fiducial marker for use as ground truth and on the calibration procedure for both optical tracking systems. Additionally, the development of a synthetic head pose dataset is described for the proof of concept and initial pre-training of a simple convolutional neural network. Results indicate that the ground truth system has been sufficiently calibrated and can track head pose with an error of

Wearable bioelectronics for continuous and reliable pulse wave monitoring against body motion and perspiration remains a great challenge and highly desired. Here, a low-cost, lightweight, and mechanically durable textile triboelectric sensor that can convert subtle skin deformation caused by arterial pulsatility into electricity for high-fidelity and continuous pulse waveform monitoring in an ambulatory and sweaty setting is developed. The sensor holds a signal-to-noise ratio of 23.3 dB, a response time of 40 ms, and a sensitivity of 0.21 µA kPa-1 . With the assistance of machine learning algorithms, the textile triboelectric sensor can continuously and precisely measure systolic and diastolic pressure, and the accuracy is validated via a commercial blood pressure cuff at the hospital. Additionally, a customized cellphone application (APP) based on built-in algorithm is developed for one-click health data sharing and data-driven cardiovascular diagnosis. The textile triboelectric sensor enabled wireless biomonitoring system is expected to offer a practical paradigm for continuous and personalized cardiovascular system characterization in the era of the Internet of Things.

This paper provides a concise review of learning-based motion artifacts (MA) processing methods in functional near-infrared spectroscopy (fNIRS), highlighting the challenges of maintaining optimal contact during subject movement, which can lead to MA and compromise data integrity. Traditional strategies often result in reduced reliability of the hemodynamic response and statistical power. Recognizing the limited number of studies focusing on learning-based MA removal, we examine 315 studies, identifying seven pertinent to our focus area. We discuss the current landscape of learning-based MA correction methods and highlight research gaps. Noting the absence of standard evaluation metrics for quality assessment of MA correction, we suggest a novel framework, integrating signal and model quality considerations and employing metrics like ΔSignal-to-Noise Ratio (ΔSNR), confusion matrix, and Mean Squared Error. This work aims to facilitate the application of learning-based methodologies to fNIRS and improve the accuracy and reliability of neurovascular studies. [ABSTRACT FROM AUTHOR]

This manuscript proposes an automated artifacts detection and multimodal classification system for human emotion analysis from human physiological signals. First, multimodal physiological data, including the Electrodermal Activity (EDA), electrocardiogram (ECG), Blood Volume Pulse (BVP) and respiration rate signals are collected. Second, a Modified Compressed Sensing-based Decomposition (MCSD) is used to extract the informative Skin Conductance Response (SCR) events of the EDA signal. Third, raw features (edge and sharp variations), statistical and wavelet coefficient features of EDA, ECG, BVP, respiration and SCR signals are obtained. Fourth, the extracted raw features, statistical and wavelet coefficient features from all physiological signals are fed into the parallel Deep Convolutional Neural Network (DCNN) to reduce the dimensionality of feature space by removing artifacts. Fifth, the fused artifact-free feature vector is obtained for neutral, stress and pleasure emotion classes. Sixth, an artifact-free feature vector is used to train the Random Forest Deep Neural Network (RFDNN) classifier. Then, a trained RFDNN classifier is applied to classify the test signals into different emotion classes. Thus, leveraging the strengths of both RF and DNN algorithms, more comprehensive feature learning using multimodal psychological data is achieved, resulting in robust and accurate classification of human emotional activities. Finally, an extensive experiment using the Wearable Stress and Affect Detection (WESAD) dataset shows that the proposed system outperforms other existing human emotion classification systems using physiological data. [ABSTRACT FROM AUTHOR]

Purpose: To improve the image reconstruction for prospective motion correction (PMC) of simultaneous multislice (SMS) EPI of the brain, an update of receiver phase and resampling of coil sensitivities are proposed and evaluated. Methods: A camera‐based system was used to track head motion (3 translations and 3 rotations) and dynamically update the scan position and orientation. We derived the change in receiver phase associated with a shifted field of view (FOV) and applied it in real‐time to each k‐space line of the EPI readout trains. Second, for the SMS reconstruction, we adapted resampled coil sensitivity profiles reflecting the movement of slices. Single‐shot gradient‐echo SMS‐EPI scans were performed in phantoms and human subjects for validation. Results: Brain SMS‐EPI scans in the presence of motion with PMC and no phase correction for scan plane shift showed noticeable artifacts. These artifacts were visually and quantitatively attenuated when corrections were enabled. Correcting misaligned coil sensitivity maps improved the temporal SNR (tSNR) of time series by 24% (p = 0.0007) for scans with large movements (up to ˜35 mm and 30°). Correcting the receiver phase improved the tSNR of a scan with minimal head movement by 50% from 50 to 75 for a United Kingdom biobank protocol. Conclusion: Reconstruction‐induced motion artifacts in single‐shot SMS‐EPI scans acquired with PMC can be removed by dynamically adjusting the receiver phase of each line across EPI readout trains and updating coil sensitivity profiles during reconstruction. The method may be a valuable tool for SMS‐EPI scans in the presence of subject motion. [ABSTRACT FROM AUTHOR]

The goal of this study was to test a novel approach (iCanClean) to remove non-brain sources from scalp EEG data recorded in mobile conditions. We created an electrically conductive phantom head with 10 brain sources, 10 contaminating sources, scalp, and hair. We tested the ability of iCanClean to remove artifacts while preserving brain activity under six conditions: Brain, Brain + Eyes, Brain + Neck Muscles, Brain + Facial Muscles, Brain + Walking Motion, and Brain + All Artifacts. We compared iCanClean to three other methods: Artifact Subspace Reconstruction (ASR), Auto-CCA, and Adaptive Filtering. Before and after cleaning, we calculated a Data Quality Score (0–100%), based on the average correlation between brain sources and EEG channels. iCanClean consistently outperformed the other three methods, regardless of the type or number of artifacts present. The most striking result was for the condition with all artifacts simultaneously present. Starting from a Data Quality Score of 15.7% (before cleaning), the Brain + All Artifacts condition improved to 55.9% after iCanClean. Meanwhile, it only improved to 27.6%, 27.2%, and 32.9% after ASR, Auto-CCA, and Adaptive Filtering. For context, the Brain condition scored 57.2% without cleaning (reasonable target). We conclude that iCanClean offers the ability to clear multiple artifact sources in real time and could facilitate human mobile brain-imaging studies with EEG. [ABSTRACT FROM AUTHOR]

Background: Cone beam computed tomography (CBCT) is often employed on radiation therapy treatment devices (linear accelerators) used in image‐guided radiation therapy (IGRT). For each treatment session, it is necessary to obtain the image of the day in order to accurately position the patient and to enable adaptive treatment capabilities including auto‐segmentation and dose calculation. Reconstructed CBCT images often suffer from artifacts, in particular those induced by patient motion. Deep‐learning based approaches promise ways to mitigate such artifacts. Purpose: We propose a novel deep‐learning based approach with the goal to reduce motion induced artifacts in CBCT images and improve image quality. It is based on supervised learning and includes neural network architectures employed as pre‐ and/or post‐processing steps during CBCT reconstruction. Methods: Our approach is based on deep convolutional neural networks which complement the standard CBCT reconstruction, which is performed either with the analytical Feldkamp‐Davis‐Kress (FDK) method, or with an iterative algebraic reconstruction technique (SART‐TV). The neural networks, which are based on refined U‐net architectures, are trained end‐to‐end in a supervised learning setup. Labeled training data are obtained by means of a motion simulation, which uses the two extreme phases of 4D CT scans, their deformation vector fields, as well as time‐dependent amplitude signals as input. The trained networks are validated against ground truth using quantitative metrics, as well as by using real patient CBCT scans for a qualitative evaluation by clinical experts. Results: The presented novel approach is able to generalize to unseen data and yields significant reductions in motion induced artifacts as well as improvements in image quality compared with existing state‐of‐the‐art CBCT reconstruction algorithms (up to +6.3 dB and +0.19 improvements in peak signal‐to‐noise ratio, PSNR, and structural similarity index measure, SSIM, respectively), as evidenced by validation with an unseen test dataset, and confirmed by a clinical evaluation on real patient scans (up to 74% preference for motion artifact reduction over standard reconstruction). Conclusions: For the first time, it is demonstrated, also by means of clinical evaluation, that inserting deep neural networks as pre‐ and post‐processing plugins in the existing 3D CBCT reconstruction and trained end‐to‐end yield significant improvements in image quality and reduction of motion artifacts. [ABSTRACT FROM AUTHOR]

Facial recognition technology, utilizing short-duration video, offers an accurate access control mechanism, capable of distinguishing genuine faces from fraudulent representations. However, the ability to detect concealed or artificially altered faces, as well as printed spoofs, remains a considerable challenge, even with sophisticated biometric recognition algorithms. This limitation can significantly undermine the performance of access control systems, rendering them susceptible to potential security breaches. In this study, we present an innovative technique that leverages the separation of Red, Green, and Blue (RGB) channels to discern between authentic faces and printed spoofs in color video recordings. This technique is deployed over a 20-second duration to effectively intercept and preclude security violations in access control systems. The proposed method was evaluated with a dataset from 20 participants, demonstrating commendable accuracy in detecting both real and spoof faces. Specifically, the technique exhibited remarkable precision in real face detection and counterfeit face detection. When applied to a 4-second video dataset from the same participants, comparable results were obtained. This method provides a significant advancement in the realm of face-controlled access systems by facilitating precise real-face detection via cost-effective video imaging, predicated on cardiac pulse rhythm signals. This methodology holds the potential to enhance the reliability of facial recognition systems, particularly in high-security environments. [ABSTRACT FROM AUTHOR]

We intended to assess the ability of current-generation 256-slice coronary CT angiography (CCTA) to measure left atrial volume (LAV), comparing patients with a high heart rate (HiHR) of at least 70 bpm and patients with heart rate variability such as atrial fibrillation (AFib). Methods: Using the prospective Converge Registry of patients undergoing 256-detector CCTA on a Revolution scanner, we enrolled 121 HiHR patients (74 men; mean age, 62.7 ± 12.5 y) and 102 AFib patients (72 men; mean age, 60.5 ± 11.0 y) after obtaining informed consent. Quantitative data analysis of LAV was performed using automated methods, and end-systolic phases were chosen for measurements from CCTA. A Student t test, Wilcoxon rank-sum test, or χ2 test assessed baseline parameters. Univariate and multivariate linear regression analysis was used to assess LAV and LAV index (LAVI) while adjusting potentially confounding variables. Results: Mean LAV was significantly higher in AFib subjects (148.6 ± 57.2 mL) than in HiHR subjects (102.1 ± 36.5 mL) (P < 0.0001). Similarly, mean LAVI was significantly higher in AFib subjects (72.4 ± 28.1 mL/m2) than in HiHR subjects (51.5 ± 19.0 mL/m2) (P < 0.0001). After adjusting for age, body mass index, sex, diabetes, hypertension, hyperlipidemia, and smoking, subjects with AFib had, on average, LAV measures higher by 41.2 ± 6.7 mL and LAVI values higher by 23.1 ± 3.4 mL/m2 (P < 0.0001). Conclusion: Misalignment and motion artifacts in CCTA images affect diagnostic CT performance, especially in patients with elevated heart rates or profound arrhythmia. However, the new-generation Revolution CCTA provides detailed information on left-atrium-complex morphology and function, in addition to coronary anatomy, in HiHR and AFib patients without additional radiation, scanning, or contrast requirements.

Phase-shift profilometry (PSP) holds great promise for high-precision 3D shape measurements. However, in the case of measuring moving objects, as PSP requires multiple images to calculate the phase, the movement of the object causes artifacts in the measurement, which in turn has a significant impact on the accuracy of the 3D surface measurement. Therefore, we propose a method to reduce motion artifacts using feature information in the image and simulate it using the six-step term shift method as a case study. The simulation results show that the phase of the object is greatly affected when the object is in motion and that the phase shift due to motion can be effectively reduced using this method. Finally, artifact optimization was carried out by way of specific copper tube vibration experiments at a measurement frequency of 320 Hz. The experimental results prove that the method is well implemented. [ABSTRACT FROM AUTHOR]

A compact OCT device and a method for image reconstruction are described. The proposed algorithm contains a novel procedure for motion artifact suppression based on a correction of the phase of the original interferometric signal due to the mutual correlation of adjacent A-scans. This procedure corrects distortions caused by unwanted displacements of the probe relative to the object in real time at a speed of up to 3 mm/s and an image acquisition rate of 20 B-scans per second. All processing is performed in real time using only the CPU, allowing the device to be controlled from a consumer-grade laptop or compact PC without the need for a discrete GPU. Due to its compact size, the device can be used in the conditions of an ENT examination room or operating room and can be freely moved to another room without the help of additional personnel, if necessary. [ABSTRACT FROM AUTHOR]

The heart rate (HR) is a widely used clinical variable that provides important information on a physical user's state. One of the most commonly used methods for ambulatory HR monitoring is photoplethysmography (PPG). The PPG signal retrieved from wearable devices positioned on the user's wrist can be corrupted when the user is performing tasks involving the motion of the arms, wrist, and fingers. In these cases, the obtained HR is altered as well. This problem increases when trying to monitor people with autism spectrum disorder (ASD), who are very reluctant to use foreign bodies, notably hindering the adequate attachment of the device to the user. This work presents a machine learning approach to reconstruct the user's HR signal using an own monitoring wristband especially developed for people with ASD. An experiment is carried out, with users performing different daily life activities in order to build a dataset with the measured signals from the monitoring wristband. From these data, an algorithm is applied to obtain a reliable HR value when these people are performing skill improvement activities where intensive wrist movement may corrupt the PPG. [ABSTRACT FROM AUTHOR]

Abstract This review paper aims to summarize cardiac CT blooming artifacts, how they present clinically and what their root causes and potential solutions are. A literature survey was performed covering any publications with a specific interest in calcium blooming and stent blooming in cardiac CT. The claims from literature are compared and interpreted, aiming at narrowing down the root causes and most promising solutions for blooming artifacts. More than 30 journal publications were identified with specific relevance to blooming artifacts. The main reported causes of blooming artifacts are the partial volume effect, motion artifacts and beam hardening. The proposed solutions are classified as high-resolution CT hardware, high-resolution CT reconstruction, subtraction techniques and post-processing techniques, with a special emphasis on deep learning (DL) techniques. The partial volume effect is the leading cause of blooming artifacts. The partial volume effect can be minimized by increasing the CT spatial resolution through higher-resolution CT hardware or advanced high-resolution CT reconstruction. In addition, DL techniques have shown great promise to correct for blooming artifacts. A combination of these techniques could avoid repeat scans for subtraction techniques.