Eliminate Motion Artifacts Research Articles

A deep learning method for contactless emotion recognition from ballistocardiogram

Emotion recognition is a major research point in the field of affective computing. Existing research on the application of physiological signals to emotion recognition mainly focuses on the processing of contact signals. However, there are issues with contact signal acquisition equipment, such as limited portability and poor user compliance, which make it difficult to promote its use. To explore a new method for emotion recognition based on contactless ballistocardiogram (BCG), we proposed a SE-CNN model with a multi-class focal loss function. To construct the dataset, we used audio-visual stimuli to evoke the subjects’ emotions and collected data on the subjects’ three discrete emotions, positive, neutral, and negative, through our established BCG signal acquisition system based on a piezoelectric ceramics sensor. Root mean square filter and thresholding were used to detect and eliminate motion artifacts of BCG signals. We did two kinds of preprocessing on BCG signals: wavelet transform and bandpass filtering, to explore the effect of different components of BCG on emotion recognition. Subsequently, we verified the model’s performance and cross-time working ability through traditional K-Fold and our proposed K-Session cross-validation methods. The results showed that the band-pass filtering method was more beneficial to the current classification task. Under K-Fold cross-validation, the model’s accuracy, precision, and recall were 97.21%, 97.00%, and 97.11%. Under K-Session cross-validation, the model’s accuracy, precision, and recall were 94.66%, 93.92%, and 94.86%, respectively, all of which were better than the classification effect of synchronous ECG. The reliability of BCG in contactless emotion recognition was proved.

Antispasmodic Agents in Magnetic Resonance Imaging of the Urinary Bladder-A Narrative Review.

Accurate assessment of muscular layer infiltration of the urinary bladder wall is crucial for diagnostic precision and is significantly influenced, among other factors, by the elimination of motion artifacts. This review explores the potential benefits of using spasmolytic agents to achieve improved imaging results. Specifically, it examines two commonly available pharmaceutical preparations: butylscopolamine (buscolysin) and glucagon. The review highlights the similarities and differences between these agents and discusses the optimal methods of administration to enhance urinary bladder imaging. By addressing these factors, the article aims to provide insights into improving diagnostic accuracy in clinical practice.

Subpixel motion artifacts correction and motion estimation for 3D-OCT.

A number of hardware-based and software-based strategies have been suggested to eliminate motion artifacts for improvement of 3D-optical coherence tomography (OCT) image quality. However, the hardware-based strategies have to employ additional hardware to record motion compensation information. Many software-based strategies have to need additional scanning for motion correction at the expense of longer acquisition time. To address this issue, we propose a motion artifacts correction and motion estimation method for OCT volumetric imaging of anterior segment, without requirements of additional hardware and redundant scanning. The motion correction effect with subpixel accuracy for in vivo 3D-OCT has been demonstrated in experiments. Moreover, the physiological information of imaging object, including respiratory curve and respiratory rate, has been experimentally extracted using the proposed method. The proposed method offers a powerful tool for scientific research and clinical diagnosis in ophthalmology and may be further extended for other biomedical volumetric imaging applications.

On‐Skin Paintable Water‐Resistant Biohydrogel for Wearable Bioelectronics

AbstractTo achieve accurate monitoring of bioelectrical signals, it is essential to use customizable bioelectrodes that can self‐adapt to the skin's surface topography. Ion‐conducting hydrogel has received significant attention in this field due to its softness, adhesion, and skin‐like mechanical properties. However, these bioelectrodes currently suffer from degradation of adhesion, electrical conductivity, and skin‐compliance when exposed to aqueous environments. This significantly limits the application of bioelectrodes. Herein, a customizable biohydrogel that can be applied on skin or fabric by solvent volatilization for liquid ink‐gel film conversion is reported. The biohydrogel's distinct characteristic of transitioning between a liquid and hydrogel phase establishes superb conformal contact and dynamic compliance with the epidermis. This effectively eliminates motion artifacts and results in lower contact impedance and noise in both static and dynamic states when compared to existing bioelectrodes. The biohydrogel is applied to the cotton fabric to create electrocardiogram (ECG) monitoring garments. These garments enable the acquisition of ECG signals with high accuracy in aqueous environment for over 72 h. Besides, the biohydrogel‐based garments outperform the commercial gel electrodes by 83.5% in signal‐to‐noise ratio. Additionally, the cotton/biohydrogel electrode facilitates multi‐channel, high‐fidelity recording of ECG signals, enabling high‐performance capture and classification of ECG waveforms across multiple channels.

Efficient EEG motion artifact elimination framework for ambulatory epileptic seizure detection application

Motion artifacts are a pervasive challenge in EEG ambulatory monitoring, often obscuring critical neurological signals and impeding accurate seizure detection. In this study, we propose a new approach of outlier based grouping of two level Singular Spectrum Analysis (SSA) decomposition combined with Relative Total Variation (RTV) filter for the effective removal of motion-induced noise from ambulatory EEG data. A two-stage SSA method was employed to decompose single-channel EEG signal, which had been interfered with, into various fre quency bands. The affected sub-band signal was then subjected to an RTV filter to estimate the artifact signal. Subtracting this estimated artifact signal from the contaminated sub-band signal yielded the filtered sub-band signal. Subse quently, the filtered sub-band signal was reintegrated with the other decomposed components from noise-free bands, culminating in the generation of the ultimate denoised EEG signal. Based on the comprehensive set of simulation results, it can be deduced that the algorithm described in the paper outperforms existing methods. It demonstrates superior metrics evaluation in terms of ΔSNR, η , MAE, and PSNR when compared to these alternatives. Our framework sig- nificantly enhances the quality of EEG data by successfully eliminating motion artifacts while preserving crucial brainwave information. To evaluate the prac tical impact of this noise reduction technique, we assess its performance in the context of seizure detection. The results reveal a substantial improvement in the accuracy and reliability of seizure detection algorithms when applied to EEG data preprocessed with proposed method.

Automating quality control in cardiac magnetic resonance: Artificial intelligence for discriminative assessment of planning and motion artifacts and real-time reacquisition guidance

BackgroundAccurate measurements from cardiovascular magnetic resonance (CMR) images require precise positioning of scan planes and elimination of motion artifacts from arrhythmia or breathing. Unidentified or incorrectly managed artifacts degrade image quality, invalidate clinical measurements, and decrease diagnostic confidence. Currently, radiographers must manually inspect each acquired image to confirm diagnostic quality and decide whether reacquisition or a change in sequences is warranted. We aimed to develop artificial intelligence (AI) to provide continuous quality scores across different quality domains, and from these, determine whether cines are clinically adequate, require replanning, or warrant a change in protocol. MethodsA three-dimensional convolutional neural network was trained to predict cine quality graded on a continuous scale by a level 3 CMR expert, focusing separately on planning and motion artifacts. It incorporated four distinct output heads for the assessment of image quality in terms of (a, b, c) 2-, 3- and 4-chamber misplanning, and (d) long- and short-axis arrhythmia/breathing artifact. Backpropagation was selectively performed across these heads based on the labels present for each cine. Each image in the testing set was reported by four level 3 CMR experts, providing a consensus on clinical adequacy. The AI's assessment of image quality and ability to identify images requiring replanning or sequence changes were evaluated with Spearman’s rho and the area under receiver operating characteristic curve (AUROC), respectively. ResultsA total of 1940 cines across 1387 studies were included. On the test set of 383 cines, AI-judged image quality correlated strongly with expert judgment, with Spearman’s rho of 0.84, 0.84, 0.81, and 0.81 for 2-, 3- and 4-chamber planning quality and the extent of arrhythmia or breathing artifacts, respectively. The AI also showed high efficacy in flagging clinically inadequate cines (AUROC 0.88, 0.93, and 0.93 for identifying misplanning of 2-, 3- and 4-chamber cines, and 0.90 for identifying movement artifacts). ConclusionAI can assess distinct domains of CMR cine quality and provide continuous quality scores that correlate closely with a consensus of experts. These ratings could be used to identify cases where reacquisition is warranted and guide corrective actions to optimize image quality, including replanning, prospective gating, or real-time imaging.

Evaluation of a Large Area, 83 μm Pixel Pitch Amorphous Selenium Indirect Flat Panel Detector.

Dual-layer detectors provide a low-cost solution to improved material decomposition and lesion differentiation in X-ray imaging, while eliminating motion artifacts from multiple exposures. Most designs utilize two indirect detectors with scintillators designed for low-energy and higher-energy detection and separated by a copper filter to harden the beam for high energy detection. To improve the performance of the bottom detector and lower dose requirements, we have previously proposed an alloyed amorphous selenium photodetector to achieve improved resolution and absorption at green wavelengths, better suited to high-performance scintillators such as CsI:Tl. In this work, we demonstrate a baseline prototype for the bottom layer-a continuous, large area 83 μm pixel pitch flat panel indirect detector with well-established amorphous selenium as the photodetector-and verify the architecture's performance and detector design. We characterize lag, noise-power spectrum, detective quantum efficiency, and modular transfer function of the detector, and show resolution up to 6 lp/mm when operated at an applied bias of 150 V. This provides a starting point for evaluating the alloyed selenium materials, and shows promise for this detector in the future dual-layer design.

Improvement of motion artifacts using dynamic whole-body 18F-FDG PET/CT imaging

PurposeSerial dynamic whole-body PET imaging is valuable for assessing serial changes in tracer uptake. The purpose of this study was to evaluate the improvement of motion artifacts in patients using serial dynamic whole-body 18F-fluorodeoxyglyucose (FDG) PET/CT imaging.Materials and methodsIn 797 consecutive patients, serial 3-min dynamic whole-body FDG PET imaging was performed seven times, at 60 or 90 min after FDG administration. In cases with large body motion during imaging, we tried to improve the images by summing the images before body motion. An image quality study was performed on another 50 patients without obvious body motion using the same acquisition mode.ResultsObvious body movement was observed in 106 of 797 cases (13.3%), and severe motion artifacts which interfered image interpretation were observed in 18 (2.3%). In these 18 cases, summation of the images before the body movement enabled us to obtain images that excluded the effect of the body motion. In the visual evaluation of the image quality in another 50 patients studied, acceptable image quality was obtained when 2 or more times the serial 3-min image data were added.ConclusionSerial dynamic whole-body FDG PET imaging can minimize body motion artifacts by summation of the images before the body motion. Such serial dynamic study may be a choice for PET imaging to eliminate motion artifacts.

JAMSNet: A Remote Pulse Extraction Network Based on Joint Attention and Multi-Scale Fusion

Remote photoplethysmography (rPPG) has been an active research topic in recent years. While most existing methods are focusing on eliminating motion artifacts in the raw traces obtained from single-scale region-of-interest (ROI), it is worth noting that there are some noise signals that cannot be effectively separated in single-scale space but can be separated more easily in multi-scale space. In this paper, we analyze the distribution of pulse signal and motion artifacts in different layers of a Gaussian pyramid. We propose a method that combines multi-scale analysis and neural network for pulse extraction in different scales, and a layer-wise attention mechanism to adaptively fuse the features according to signal strength. In addition, we propose spatial-temporal joint attention module and channel-temporal joint attention module to learn and exaggerate pulse features in the joint spaces, respectively. The proposed remote pulse extraction network is called Joint Attention and Multi-Scale fusion Network (JAMSNet). Extensive experiments have been conducted on two publicly available datasets and one self-collected dataset. The results show that the proposed JAMSNet shows better performance than state-of-the-art methods.

Three-material decomposition using a dual-layer flat panel detector in the presence of soft tissue motion.

Digital subtraction angiography (DSA) is a widely used technique for the visualization of contrast-enhanced structures. However, temporal subtraction DSA is challenged by misregistration artifacts due to patient motion and incomplete separation of iodine contrast agent from background soft tissue and bone. In this work, we propose an approach that allows three-material decomposition using a dual-layer flat panel detector in the presence of soft tissue motion. We assume the calcium signal (bone) remains stationary in the pre- and post-contrast images but allow soft tissues to move freely (e.g. cardiac motion). The dual-layer pre- and post-injection images form and ensemble of four measurements that permits material decomposition of four bases (pre- and post-injection soft tissue, calcium, and iodine). We apply two different processing techniques: 1) a modified lookup table and; 2) a model-based material estimation. These are compared with previously proposed DSA methods using temporal subtraction and hybrid (dual-energy) subtraction. Investigations were performed using an XCAT thorax phantom simulating a breath-hold. The pre- and post-contrast measurements were simulated at different time points within a cardiac cycle. Both the lookup table and model-based algorithms eliminate motion artifact as a result of soft tissue motion and allow good separation of iodine, bone, and soft tissue. While the lookup table algorithm contains high noise at the simulated dose level, the model-based algorithm produced iodine images that allow the visualization of major vessels around the heart. In contrast, traditional temporal DSA is susceptible to subtraction artifacts and hybrid DSA shows increased noise.

An Accurate Non-accelerometer-based PPG Motion Artifact Removal Technique using CycleGAN

A photoplethysmography (PPG) is an uncomplicated and inexpensive optical technique widely used in the healthcare domain to extract valuable health-related information, e.g., heart rate variability, blood pressure, and respiration rate. PPG signals can easily be collected continuously and remotely using portable wearable devices. However, these measuring devices are vulnerable to motion artifacts caused by daily life activities. The most common ways to eliminate motion artifacts use extra accelerometer sensors, which suffer from two limitations: (i) high power consumption, and (ii) the need to integrate an accelerometer sensor in a wearable device (which is not required in certain wearables). This paper proposes a low-power non-accelerometer-based PPG motion artifacts removal method outperforming the accuracy of the existing methods. We use Cycle Generative Adversarial Network to reconstruct clean PPG signals from noisy PPG signals. Our novel machine-learning-based technique achieves 9.5 times improvement in motion artifact removal compared to the state-of-the-art without using extra sensors such as an accelerometer, which leads to 45% improvement in energy efficiency.

MLMRS-Net: Electroencephalography (EEG) motion artifacts removal using a multi-layer multi-resolution spatially pooled 1D signal reconstruction network

Electroencephalogram (EEG) signals suffer substantially from motion artifacts when recorded in ambulatory settings utilizing wearable sensors. Because the diagnosis of many neurological diseases is heavily reliant on clean EEG data, it is critical to eliminate motion artifacts from motion-corrupted EEG signals using reliable and robust algorithms. Although a few deep learning-based models have been proposed for the removal of ocular, muscle, and cardiac artifacts from EEG data to the best of our knowledge, there is no attempt has been made in removing motion artifacts from motion-corrupted EEG signals: In this paper, a novel 1D convolutional neural network (CNN) called multi-layer multi-resolution spatially pooled (MLMRS) network for signal reconstruction is proposed for EEG motion artifact removal. The performance of the proposed model was compared with ten other 1D CNN models: FPN, LinkNet, UNet, UNet+, UNetPP, UNet3+, AttentionUNet, MultiResUNet, DenseInceptionUNet, and AttentionUNet++ in removing motion artifacts from motion-contaminated single-channel EEG signal. All the eleven deep CNN models are trained and tested using a single-channel benchmark EEG dataset containing 23 sets of motion-corrupted and reference ground truth EEG signals from PhysioNet. Leave-one-out cross-validation method was used in this work. The performance of the deep learning models is measured using three well-known performance matrices viz. mean absolute error (MAE)-based construction error, the difference in the signal-to-noise ratio (ΔSNR), and percentage reduction in motion artifacts (η). The proposed MLMRS-Net model has shown the best denoising performance, producing an average ΔSNR, η, and MAE values of 26.64 dB, 90.52%, and 0.056, respectively, for all 23 sets of EEG recordings. The results reported using the proposed model outperformed all the existing state-of-the-art techniques in terms of average η improvement.

Role of advanced imaging techniques in cardiac surgery: Aortic dissection.

Collaboration among cardiac surgeons and radiologists is essential to fully leverage advanced imaging technologies and improve the care of cardiac surgery patients. In this review, a cardiac surgeon and cardiovascular radiologist discuss imaging pearls and considerations in aortic dissection cases. The surgeon and the radiologist discuss imaging considerations in two aortic dissection cases. It is essential to obtain and review all phases of a CTA when diagnosing acute aortic pathology. Optimizing scan parameters and careful multiplanar image review is necessary for adept interpretation. Current CT technology allows ECG gating to eliminate motion artifact and allow for dynamic assessment of the aortic pathology. Concurrent evaluation of thoracic aorta and coronary arteries is feasible. A systematic review of the scan using landmarks is critical for appropriate diagnosis and reporting. As TEVAR is increasingly used for arch repair, collaboration with radiologists is essential for preoperative planning in redo cases. Collaboration among cardiac surgeons and radiologists is mutually beneficial for surgeons, radiologists, and their patients.

Elimination of Motion Artifacts in Signal of Photoplethysmography Sensor

Elimination of Motion Artifacts in Signal of Photoplethysmography Sensor

STSR: Spectro-Temporal Super-Resolution Analysis of a Reference Signal Less Photoplethysmogram for Heart Rate Estimation During Physical Activity

The increasing demand for intelligent, wearable health monitoring devices with a Photoplethysmogram (PPG) sensor to monitor real-time heart rate (HR) in a subject attracts great attention from researchers. However, the PPG-enabled wearable devices are sensitive to motion artifacts, which results in inaccurate HR estimation. The accuracy of HR estimation is possible only by eliminating motion artifacts. A method based on multi-resolution Spectro-Temporal Super Resolution (STSR) and superlet transform (SLT) is proposed to improve the estimation accuracy of a motion artifact corrupted PPG signal. This proposed methodology does not require a reference accelerometer signal to suppress motion artifacts to estimate HR in real-time during physical activities. Hence, the proposed method is a computationally efficient algorithm. In the first step, a multi-order SLT separates the motion artifacts from the acquired PPG signal in the proposed method. In the next step, a high-intensity lobe in the spectro-temporal representation of the SLT spectrogram is detected to compute the HR. In the final step, an HR smoothing algorithm that uses HR from the preceding windows is proposed to accurately estimate HR in a motion artifact effected signal. The performance of the SLT-based HR estimation algorithm is evaluated using the publicly available IEEE SPC dataset. The average HR estimation error is 1.01 beats per minute, and the Pearson correlation is 0.997. Low estimation error, short computational time, and fast HR tracking make the proposed method an ideal choice to implement in wearable devices for real-time HR estimation.

Real-time X-ray imaging of mouse cerebral microvessels in vivo using a pixel temporal averaging method.

Rodents are used extensively as animal models for the preclinical investigation of microvascular-related diseases. However, motion artifacts in currently available imaging methods preclude real-time observation of microvessels invivo. In this paper, a pixel temporal averaging (PTA) method that enables real-time imaging of microvessels in the mouse brain in vivo is described. Experiments using live mice demonstrated that PTA efficiently eliminated motion artifacts and random noise, resulting in significant improvements in contrast-to-noise ratio. The time needed for image reconstruction using PTA with a normal computer was 250 ms, highlighting the capability of the PTA method for real-time angiography. In addition, experiments with less than one-quarter of photon flux in conventional angiography verified that motion artifacts and random noise were suppressed and microvessels were successfully identified using PTA, whereas conventional temporal subtraction and averaging methods were ineffective. Experiments performed with an X-ray tube verified that the PTA method could also be successfully applied to microvessel imaging of the mouse brain using a laboratory X-ray source. In conclusion, the proposed PTA method may facilitate the real-time investigation of cerebral microvascular-related diseases using small animal models.

A deep learning method for eliminating head motion artifacts in computed tomography.

Involuntary patient movement results in data discontinuities during computed tomography (CT) scans which lead to a serious degradation in the image quality. In this paper, we specifically address artifacts induced by patient motion during a head scan. Instead of trying to solve an inverse problem, we developed a motion simulation algorithm to synthesize images with motion-induced artifacts. The artifacts induced by rotation, translation, oscillation and any possible combination are considered. Taking advantage of the powerful learning ability of neural networks, we designed a novel 3D network structure with both a large reception field and a high image resolution to map the artifact-free images from artifact-contaminated images. Quantitative results of the proposed method were evaluated against the results of U-Net and proposed networks without dilation structure. Thirty sets of motion contaminated images from two hospitals were selected to do a clinical evaluation. Facilitating the training dataset with artifacts induced by variable motion patterns and the neural network, the artifact can be removed with good performance. Validation dataset with simulated random motion pattern showed outperformed image correction, and quantitative results showed the proposed network had the lowest normalized root-mean-square error, highest peak signal-to-noise ratio and structure similarity, indicating our network gave the best approximation of gold standard. Clinical image processing results further confirmed the effectiveness of our method. We proposed a novel deep learning-based algorithm to eliminate motion artifacts. The convolutional neural networks trained with synthesized image pairs achieved promising results in artifacts reduction. The corrected images increased the diagnostic confidence compared with artifacts contaminated images. We believe that the correction method can restore the ability to successfully diagnose and avoid repeated CT scans in certain clinical circumstances.

Elimination of Motion Artifacts in Photoplethysmography Signals by Analytical Method

Elimination of Motion Artifacts in Photoplethysmography Signals by Analytical Method

Stop the beat to see the rhythm: excitation-contraction uncoupling in cardiac research

Optical mapping is an imaging technique that is extensively used in cardiovascular research, wherein parameter-sensitive fluorescent indicators are used to study the electrophysiology and excitation-contraction coupling of cardiac tissues. Despite many benefits of optical mapping, eliminating motion artifacts within the optical signals is a major challenge, as myocardial contraction interferes with the faithful acquisition of action potentials and intracellular calcium transients. As such, excitation-contraction uncoupling agents are frequently used to reduce signal distortion by suppressing contraction. When compared with other uncoupling agents, blebbistatin is the most frequently used, as it offers increased potency with minimal direct effects on cardiac electrophysiology. Nevertheless, blebbistatin may exert secondary effects on electrical activity, metabolism, and coronary flow, and the incorrect administration of blebbistatin to cardiac tissue can prove detrimental, resulting in erroneous interpretation of optical mapping results. In this "Getting It Right" perspective, we briefly review the literature regarding the use of blebbistatin in cardiac optical mapping experiments, highlight potential secondary effects of blebbistatin on cardiac electrical activity and metabolic demand, and conclude with the consensus of the authors on best practices for effectively using blebbistatin in optical mapping studies of cardiac tissue.

Motion-compensated noninvasive periodontal health monitoring using handheld and motor-based photoacoustic-ultrasound imaging systems.

Simultaneous visualization of the teeth and periodontium is of significant clinical interest for image-based monitoring of periodontal health. We recently reported the application of a dual-modality photoacoustic-ultrasound (PA-US) imaging system for resolving periodontal anatomy and periodontal pocket depths in humans. This work utilized a linear array transducer attached to a stepper motor to generate 3D images via maximum intensity projection. This prior work also used a medical head immobilizer to reduce artifacts during volume rendering caused by motion from the subject (e.g., breathing, minor head movements). However, this solution does not completely eliminate motion artifacts while also complicating the imaging procedure and causing patient discomfort. To address this issue, we report the implementation of an image registration technique to correctly align B-mode PA-US images and generate artifact-free 2D cross-sections. Application of the deshaking technique to PA phantoms revealed 80% similarity to the ground truth when shaking was intentionally applied during stepper motor scans. Images from handheld sweeps could also be deshaken using an LED PA-US scanner. In ex vivo porcine mandibles, pigmentation of the enamel was well-estimated within 0.1 mm error. The pocket depth measured in a healthy human subject was also in good agreement with our prior study. This report demonstrates that a modality-independent registration technique can be applied to clinically relevant PA-US scans of the periodontium to reduce operator burden of skill and subject discomfort while showing potential for handheld clinical periodontal imaging.