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

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

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

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

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

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

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

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

Motion-robust free-running volumetric cardiovascular MRI.

PURPOSE: To present and assess an outlier mitigation method that makes free-running volumetric cardiovascular MRI (CMR) more robust to motion. METHODS: The proposed method, called compressive recovery with outlier rejection (CORe), models outliers in the measured data as an additive auxiliary variable. We enforce MR physics-guided group sparsity on the auxiliary variable, and jointly estimate it along with the image using an iterative algorithm. For evaluation, CORe is first compared to traditional compressed sensing (CS), robust regression (RR), and an existing outlier rejection method using two simulation studies. Then, CORe is compared to CS using seven three-dimensional (3D) cine, 12 rest four-dimensional (4D) flow, and eight stress 4D flow imaging datasets. RESULTS: Our simulation studies show that CORe outperforms CS, RR, and the existing outlier rejection method in terms of normalized mean square error and structural similarity index across 55 different realizations. The expert reader evaluation of 3D cine images demonstrates that CORe is more effective in suppressing artifacts while maintaining or improving image sharpness. Finally, 4D flow images show that CORe yields more reliable and consistent flow measurements, especially in the presence of involuntary subject motion or exercise stress. CONCLUSION: An outlier rejection method is presented and tested using simulated and measured data. This method can help suppress motion artifacts in a wide range of free-running CMR applications.

Anti-artifacts techniques for neural recording front-ends in closed-loop brain-machine interface ICs.

In recent years, thanks to the development of integrated circuits, clinical medicine has witnessed significant advancements, enabling more efficient and intelligent treatment approaches. Particularly in the field of neuromedical, the utilization of brain-machine interfaces (BMI) has revolutionized the treatment of neurological diseases such as amyotrophic lateral sclerosis, cerebral palsy, stroke, or spinal cord injury. The BMI acquires neural signals via recording circuits and analyze them to regulate neural stimulator circuits for effective neurological treatment. However, traditional BMI designs, which are often isolated, have given way to closed-loop brain-machine interfaces (CL-BMI) as a contemporary development trend. CL-BMI offers increased integration and accelerated response speed, marking a significant leap forward in neuromedicine. Nonetheless, this advancement comes with its challenges, notably the stimulation artifacts (SA) problem inherent to the structural characteristics of CL-BMI, which poses significant challenges on the neural recording front-ends (NRFE) site. This paper aims to provide a comprehensive overview of technologies addressing artifacts in the NRFE site within CL-BMI. Topics covered will include: (1) understanding and assessing artifacts; (2) exploring the impact of artifacts on traditional neural recording front-ends; (3) reviewing recent technological advancements aimed at addressing artifact-related issues; (4) summarizing and classifying the aforementioned technologies, along with an analysis of future trends.

Wearable EMG Measurement Device Using Polyurethane Foam for Motion Artifact Suppression.

We propose the use of a specially designed polyurethane foam with a plateau region in its mechanical characteristics-where stress remains nearly constant during deformation-between the electromyography (EMG) electrode and clothing to suppress motion artifacts in EMG measurement. Wearable EMG devices are receiving attention for monitoring muscle weakening due to aging. However, daily EMG measurement has been challenging due to motion artifacts caused by changes in the contact pressure between the bioelectrode and the skin. Therefore, this study aims to measure EMG signals in daily movement environments by controlling the contact pressure using polyurethane foam between the bioelectrode on the clothing and the skin. Through mechanical calculations and finite element method simulations of the polyurethane foam's effect, we clarified that the characteristics of the polyurethane foam significantly influence contact pressure control and that the contact pressure is adjustable through the polyurethane foam thickness. The optimization of the design successfully controlled the contact pressure between the bioelectrode and skin from 1.0 kPa to 2.0 kPa, effectively suppressing the motion artifact in EMG measurement.

Hammerstein-Wiener Motion Artifact Correction for Functional Near-Infrared Spectroscopy: A Novel Inertial Measurement Unit-Based Technique.

Participant movement is a major source of artifacts in functional near-infrared spectroscopy (fNIRS) experiments. Mitigating the impact of motion artifacts (MAs) is crucial to estimate brain activity robustly. Here, we suggest and evaluate a novel application of the nonlinear Hammerstein-Wiener model to estimate and mitigate MAs in fNIRS signals from direct-movement recordings through IMU sensors mounted on the participant's head (head-IMU) and the fNIRS probe (probe-IMU). To this end, we analyzed the hemodynamic responses of single-channel oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) signals from 17 participants who performed a hand tapping task with different levels of concurrent head movement. Additionally, the tapping task was performed without head movements to estimate the ground-truth brain activation. We compared the performance of our novel approach with the probe-IMU and head-IMU to eight established methods (PCA, tPCA, spline, spline Savitzky-Golay, wavelet, CBSI, RLOESS, and WCBSI) on four quality metrics: SNR, △AUC, RMSE, and R. Our proposed nonlinear Hammerstein-Wiener method achieved the best SNR increase (p < 0.001) among all methods. Visual inspection revealed that our approach mitigated MA contaminations that other techniques could not remove effectively. MA correction quality was comparable with head- and probe-IMUs.

An Ultralow-Power Real-Time Machine Learning Based fNIRS Motion Artifacts Detection.

Due to iterative matrix multiplications or gradient computations, machine learning modules often require a large amount of processing power and memory. As a result, they are often not feasible for use in wearable devices, which have limited processing power and memory. In this study, we propose an ultralow-power and real-time machine learning-based motion artifact detection module for functional near-infrared spectroscopy (fNIRS) systems. We achieved a high classification accuracy of 97.42%, low field-programmable gate array (FPGA) resource utilization of 38354 lookup tables and 6024 flip-flops, as well as low power consumption of 0.021 W in dynamic power. These results outperform conventional CPU support vector machine (SVM) methods and other state-of-the-art SVM implementations. This study has demonstrated that an FPGA-based fNIRS motion artifact classifier can be exploited while meeting low power and resource constraints, which are crucial in embedded hardware systems while keeping high classification accuracy.

Ultraconformal Skin-Interfaced Sensing Platform for Motion Artifact-Free Monitoring.

Capable of directly capturing various physiological signals from human skin, skin-interfaced bioelectronics has emerged as a promising option for human health monitoring. However, the accuracy and reliability of the measured signals can be greatly affected by body movements or skin deformations (e.g., stretching, wrinkling, and compression). This study presents an ultraconformal, motion artifact-free, and multifunctional skin bioelectronic sensing platform fabricated by a simple and user-friendly laser patterning approach for sensing high-quality human physiological data. The highly conductive membrane based on the room-temperature coalesced Ag/Cu@Cu core-shell nanoparticles in a mixed solution of polymers can partially dissolve and locally deform in the presence of water to form conformal contact with the skin. The resulting sensors to capture improved electrophysiological signals upon various skin deformations and other biophysical signals provide an effective means to monitor health conditions and create human-machine interfaces. The highly conductive and stretchable membrane can also be used as interconnects to connect commercial off-the-shelf chips to allow extended functionalities, and the proof-of-concept demonstration is highlighted in an integrated pulse oximeter. The easy-to-remove feature of the resulting device with water further allows the device to be applied on delicate skin, such as the infant and elderly.

A Wavelet-Based Approach for Motion Artifact Reduction in Ambulatory Seismocardiography.

Wearable sensing has become a vital approach to cardiac health monitoring, and seismocardiography (SCG) is emerging as a promising technology in this field. However, the applicability of SCG is hindered by motion artifacts, including those encountered in practice of which the strongest source is walking. This holds back the translation of SCG to clinical settings. We therefore investigated techniques to enhance the quality of SCG signals in the presence of motion artifacts. To simulate ambulant recordings, we corrupted a clean SCG dataset with real-walking-vibrational noise. We decomposed the signal using several empirical-mode-decomposition methods and the maximum overlap discrete wavelet transform (MODWT). By combining MODWT, time-frequency masking, and nonnegative matrix factorization, we developed a novel algorithm which leveraged the vertical axis accelerometer to reduce walking vibrations in dorsoventral SCG. The accuracy and applicability of our method was verified using heart rate estimation. We used an interactive selection approach to improve estimation accuracy. The best decomposition method for reduction of motion artifact noise was the MODWT. Our algorithm improved heart rate estimation from 0.1 to 0.8 r-squared at -15 dB signal-to-noise ratio (SNR). Our method reduces motion artifacts in SCG signals up to a SNR of -19 dB without requiring any external assistance from electrocardiography (ECG). Such a standalone solution is directly applicable to the usage of SCG in daily life, as a content-rich replacement for other wearables in clinical settings, and other continuous monitoring scenarios. In applications with higher noise levels, ECG may be incorporated to further enhance SCG and extend its usable range. This work addresses the challenges posed by motion artifacts, enabling SCG to offer reliable cardiovascular insights in more difficult scenarios, and thereby facilitating wearable monitoring in daily life and the clinic.

Evaluation of Motion Artifact Correction Technique for Cone-Beam Computed Tomography Image Considering Blood Vessel Geometry.

Background: In this study, we present a quantitative method to evaluate the motion artifact correction (MAC) technique through the morphological analysis of blood vessels in the images before and after MAC. Methods: Cone-beam computed tomography (CBCT) scans of 37 patients who underwent transcatheter chemoembolization were obtained, and images were reconstructed with and without the MAC technique. First, two interventional radiologists selected the blood vessels corrected by MAC. We devised a motion-corrected index (MCI) metric that analyzed the morphology of blood vessels in 3D space using information on the centerline of blood vessels, and the blood vessels selected by the interventional radiologists were quantitatively evaluated using MCI. In addition, these blood vessels were qualitatively evaluated by two interventional radiologists. To validate the effectiveness of the devised MCI, we compared the MCI values in a blood vessel corrected by MAC and one non-corrected by MAC. Results: The visual evaluation revealed that motion correction was found in the images of 23 of 37 patients (62.2%), and a performance evaluation of MAC was performed with 54 blood vessels in 23 patients. The visual grading analysis score was 1.56 ± 0.57 (radiologist 1) and 1.56 ± 0.63 (radiologist 2), and the proposed MCI was 0.67 ± 0.11, indicating that the vascular morphology was well corrected by the MAC. Conclusions: We verified that our proposed method is useful for evaluating the MAC technique of CBCT, and the MAC technique can correct the blood vessels distorted by the patient's movement and respiration.

Effects of prospective motion correction on perivascular spaces at 7T MRI evaluated using motion artifact simulation.

PURPOSE: The effectiveness of prospective motion correction (PMC) is often evaluated by comparing artifacts in images acquired with and without PMC (NoPMC). However, such an approach is not applicable in clinical setting due to unavailability of NoPMC images. We aim to develop a simulation approach for demonstrating the ability of fat-navigator-based PMC in improving perivascular space (PVS) visibility in T2-weighted MRI. METHODS: MRI datasets from two earlier studies were used for motion artifact simulation and evaluating PMC, including T2-weighted NoPMC and PMC images. To simulate motion artifacts, k-space data at motion-perturbed positions were calculated from artifact-free images using nonuniform Fourier transform and misplaced onto the Cartesian grid before inverse Fourier transform. The simulation's ability to reproduce motion-induced blurring, ringing, and ghosting artifacts was evaluated using sharpness at lateral ventricle/white matter boundary, ringing artifact magnitude in the Fourier spectrum, and background noise, respectively. PVS volume fraction in white matter was employed to reflect its visibility. RESULTS: In simulation, sharpness, PVS volume fraction, and background noise exhibited significant negative correlations with motion score. Significant correlations were found in sharpness, ringing artifact magnitude, and PVS volume fraction between simulated and real NoPMC images (p ≤ 0.006). In contrast, such correlations were reduced and nonsignificant between simulated and real PMC images (p ≥ 0.48), suggesting reduction of motion effects with PMC. CONCLUSIONS: The proposed simulation approach is an effective tool to study the effects of motion and PMC on PVS visibility. PMC may reduce the systematic bias of PVS volume fraction caused by motion artifacts.

Optimizing Coronary Computed Tomography Angiography Using a Novel Deep Learning-Based Algorithm.

Coronary computed tomography angiography (CCTA) is an essential part of the diagnosis of chronic coronary syndrome (CCS) in patients with low-to-intermediate pre-test probability. The minimum technical requirement is 64-row multidetector CT (64-MDCT), which is still frequently used, although it is prone to motion artifacts because of its limited temporal resolution and z-coverage. In this study, we evaluate the potential of a deep-learning-based motion correction algorithm (MCA) to eliminate these motion artifacts. 124 64-MDCT-acquired CCTA examinations with at least minor motion artifacts were included. Images were reconstructed using a conventional reconstruction algorithm (CA) and a MCA. Image quality (IQ), according to a 5-point Likert score, was evaluated per-segment, per-artery, and per-patient and was correlated with potentially disturbing factors (heart rate (HR), intra-cycle HR changes, BMI, age, and sex). Comparison was done by Wilcoxon-Signed-Rank test, and correlation by Spearman's Rho. Per-patient, insufficient IQ decreased by 5.26%, and sufficient IQ increased by 9.66% with MCA. Per-artery, insufficient IQ of the right coronary artery (RCA) decreased by 18.18%, and sufficient IQ increased by 27.27%. Per-segment, insufficient IQ in segments 1 and 2 decreased by 11.51% and 24.78%, respectively, and sufficient IQ increased by 10.62% and 18.58%, respectively. Total artifacts per-artery decreased in the RCA from 3.11 ± 1.65 to 2.26 ± 1.52. HR dependence of RCA IQ decreased to intermediate correlation in images with MCA reconstruction. The applied MCA improves the IQ of 64-MDCT-acquired images and reduces the influence of HR on IQ, increasing 64-MDCT validity in the diagnosis of CCS.

Motion Artifact Reduction Using U-Net Model with Three-Dimensional Simulation-Based Datasets for Brain Magnetic Resonance Images.

This study aimed to remove motion artifacts from brain magnetic resonance (MR) images using a U-Net model. In addition, a simulation method was proposed to increase the size of the dataset required to train the U-Net model while avoiding the overfitting problem. The volume data were rotated and translated with random intensity and frequency, in three dimensions, and were iterated as the number of slices in the volume data. Then, for every slice, a portion of the motion-free k-space data was replaced with motion k-space data, respectively. In addition, based on the transposed k-space data, we acquired MR images with motion artifacts and residual maps and constructed datasets. For a quantitative evaluation, the root mean square error (RMSE), peak signal-to-noise ratio (PSNR), coefficient of correlation (CC), and universal image quality index (UQI) were measured. The U-Net models for motion artifact reduction with the residual map-based dataset showed the best performance across all evaluation factors. In particular, the RMSE, PSNR, CC, and UQI improved by approximately 5.35×, 1.51×, 1.12×, and 1.01×, respectively, and the U-Net model with the residual map-based dataset was compared with the direct images. In conclusion, our simulation-based dataset demonstrates that U-Net models can be effectively trained for motion artifact reduction.

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, andPSNRwhen 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.

[Reduction of Motion Artifacts in Liver MRI Using Deep Learning with High-pass Filtering].

PURPOSE: To investigate whether deep learning with high-pass filtering can be used to effectively reduce motion artifacts in magnetic resonance (MR) images of the liver. METHODS: The subjects were 69 patients who underwent liver MR examination at our hospital. Simulated motion artifact images (SMAIs) were created from non-artifact images (NAIs) and used for deep learning. Structural similarity index measure (SSIM) and contrast ratio (CR) were used to verify the effect of reducing motion artifacts in motion artifact reduction image (MARI) output from the obtained deep learning model. In the visual assessment, reduction of motion artifacts and image sharpness were evaluated between motion artifact images (MAIs) and MARIs. RESULTS: The SSIM values were 0.882 on the MARIs and 0.869 on the SMAIs. There was no statistically significant difference in CR between NAIs and MARIs. The visual assessment showed that MARIs had reduced motion artifacts and improved sharpness compared to MAIs. CONCLUSION: The learning model in this study is indicated to be reduced motion artifacts without decreasing the sharpness of liver MR images.

Polymeric Conductive Adhesive-Based Ultrathin Epidermal Electrodes for Long-Term Monitoring of Electrophysiological Signals.

Electrophysiology, exploring vital electrical phenomena in living organisms, anticipates broader integration into daily life through wearable devices and epidermal electrodes. However, addressing the challenges of the electrode durability and motion artifacts is essential to enable continuous and long-term biopotential signal monitoring, presenting a hurdle for its seamless implementation in daily life. To address these challenges, an ultrathin polymeric conductive adhesive, poly(3,4-ethylenedioxythiophene): poly(styrenesulfonate)/polyvinyl alcohol/d-sorbitol (PPd) electrode with enhanced adhesion, stretchability, and skin conformability, is presented. The skin conformability and stability of electrodes is designed by theoretical criteria obtained by mechanical analysis. Thus, impedance stability is obtained over 1-week of daily life, and the PPd electrode addresses the challenges related to durability during prolonged usage. Proving stability in electromyography (EMG) signals during high-intensity exercise, the wireless PPd measurement system exhibits high signal-to-noise ratio (SNR) signals even in situations involving significant and repetitive skin deformation. Throughout continuous 1 week-long electrocardiogram (ECG) monitoring in daily life, the system consistently preserves signal quality, underscoring the heightened durability and applicability of the PPd measurement system.

Motion artifact correction in cardiac CT using cross-phase temporospatial information and synergistic attention gate and spatial transformer sub-networks.

Objectives.To improve quality of coronary CT angiography (CCTA) images using a generalizable motion-correction algorithm.Approach. A neural network with attention gate and spatial transformer (ATOM) was developed to correct coronary motion. Phantom and patient CCTA images (39 males, 32 females, age range 19-92, scan date 02/2020 to 10/2021) retrospectively collected from dual-source CT were used to create training, development, and testing sets corresponding to 140- and 75 ms temporal resolution, with 75 ms images as labels. To test generalizability, ATOM was deployed for locally adaptive motion-correction in both 140- and 75 ms patient images. Objective metrics were used to assess motion-corrupted and corrected phantom and patient images, including structural-similarity-index (SSIM), dice-similarity-coefficient (DSC), peak-signal-noise-ratio (PSNR), and normalized root-mean-square-error (NRMSE). In objective quality assessment, ATOM was compared with several baseline networks, including U-net, U-net plus attention gate, U-net plus spatial transformer, VDSR, and ResNet. Two cardiac radiologists independently interpreted motion-corrupted and -corrected images at 75 and 140 ms in a blinded fashion and ranked diagnostic image quality (worst to best: 1-4, no ties).Main results. ATOM improved quality metrics (p< 0.05) before/after correction: in phantom, SSIM 0.87/0.95, DSC 0.85/0.93, PSNR 19.4/22.5, NRMSE 0.38/0.27; in patient images, SSIM 0.82/0.88, DSC 0.88/0.90, PSNR 30.0/32.0, NRMSE 0.16/0.12. ATOM provided more consistent improvement of objective image quality, compared to the presented baseline networks. The motion-corrected images received better ranks than un-corrected at the same temporal resolution (p< 0.05): 140 ms images 1.65/2.25, and 75 ms images 3.1/3.2. The motion-corrected 75 ms images received the best rank in 65% of testing cases. A fair-to-good inter-reader agreement was observed (Kappa score 0.58).Significance. ATOM reduces motion artifacts, improving visualization of coronary arteries. This algorithm can be used to virtually improve temporal resolution in both single- and dual-source CT.

AI-based motion artifact severity estimation in undersampled MRI allowing for selection of appropriate reconstruction models.

BACKGROUND: Magnetic Resonance acquisition is a time consuming process, making it susceptible to patient motion during scanning. Even motion in the order of a millimeter can introduce severe blurring and ghosting artifacts, potentially necessitating re-acquisition. Magnetic Resonance Imaging (MRI) can be accelerated by acquiring only a fraction of k-space, combined with advanced reconstruction techniques leveraging coil sensitivity profiles and prior knowledge. Artificial intelligence (AI)-based reconstruction techniques have recently been popularized, but generally assume an ideal setting without intra-scan motion. PURPOSE: To retrospectively detect and quantify the severity of motion artifacts in undersampled MRI data. This may prove valuable as a safety mechanism for AI-based approaches, provide useful information to the reconstruction method, or prompt for re-acquisition while the patient is still in the scanner. METHODS: We developed a deep learning approach that detects and quantifies motion artifacts in undersampled brain MRI. We demonstrate that synthetically motion-corrupted data can be leveraged to train the convolutional neural network (CNN)-based motion artifact estimator, generalizing well to real-world data. Additionally, we leverage the motion artifact estimator by using it as a selector for a motion-robust reconstruction model in case a considerable amount of motion was detected, and a high data consistency model otherwise. RESULTS: Training and validation were performed on 4387 and 1304 synthetically motion-corrupted images and their uncorrupted counterparts, respectively. Testing was performed on undersampled in vivo motion-corrupted data from 28 volunteers, where our model distinguished head motion from motion-free scans with 91% and 96% accuracy when trained on synthetic and on real data, respectively. It predicted a manually defined quality label ('Good', 'Medium' or 'Bad' quality) correctly in 76% and 85% of the time when trained on synthetic and real data, respectively. When used as a selector it selected the appropriate reconstruction network 93% of the time, achieving near optimal SSIM values. CONCLUSIONS: The proposed method quantified motion artifact severity in undersampled MRI data with high accuracy, enabling real-time motion artifact detection that can help improve the safety and quality of AI-based reconstructions.