Study and analysis of motion artifacts for ambulatory electroencephalography

Motion artifacts contribute complexity in acquiring clean electroencephalography (EEG) data. It is one of the major challenges for ambulatory EEG. The performance of mobile health monitoring, neurological disorders diagnosis and surgeries can be significantly improved by reducing the motion artifacts. Although different papers have proposed various novel approaches for removing motion artifacts, the datasets used to validate those algorithms are questionable. In this paper, a unique EEG dataset was presented where ten different activities were performed. No such previous EEG recordings using EMOTIV EEG headset are available in research history that explicitly mentioned and considered a number of daily activities that induced motion artifacts in EEG recordings. Quantitative study shows that in comparison to correlation coefficient, the coherence analysis depicted a better similarity measure between motion artifacts and motion sensor data. Motion artifacts were characterized with very low frequency which overlapped with the Delta rhythm of the EEG. Also, a general wavelet transform based approach was presented to remove motion artifacts. Further experiment and analysis with more similarity metrics and longer recording duration for each activity is required to finalize the characteristics of motion artifacts and henceforth reliably identify and subsequently remove the motion artifacts in the contaminated EEG recordings.

An iterative image-based inter-frame motion compensation method for dynamic brain PET imaging

As a non-invasive imaging tool, Positron Emission Tomography (PET) plays an important role in brain science and disease research. Dynamic acquisition is one way of brain PET imaging. Its wide application in clinical research has often been hindered by practical challenges, such as patient involuntary movement, which could degrade both image quality and the accuracy of the quantification. This is even more obvious in scans of patients with neurodegeneration or mental disorders. Conventional motion compensation methods were either based on images or raw measured data, were shown to be able to reduce the effect of motion on the image quality. As for a dynamic PET scan, motion compensation can be challenging as tracer kinetics and relatively high noise can be present in dynamic frames. In this work, we propose an image-based inter-frame motion compensation approach specifically designed for dynamic brain PET imaging. Our method has an iterative implementation that only requires reconstructed images, based on which the inter-frame subject movement can be estimated and compensated. The method utilized tracer-specific kinetic modelling and can deal with simple and complex movement patterns. The synthesized phantom study showed that the proposed method can compensate for the simulated motion in scans with 18F-FDG, 18F-Fallypride and 18F-AV45. Fifteen dynamic 18F-FDG patient scans with motion artifacts were also processed. The quality of the recovered image was superior to the one of the non-corrected images and the corrected images with other image-based methods. The proposed method enables retrospective image quality control for dynamic brain PET imaging, hence facilitates the applications of dynamic PET in clinics and research.

Peak Space Motion Artifact Cancellation Applied to Textile Electrode Waist Electrocardiograms Recording During Outdoors Walking and Jogging

Background: Obtaining reliable rate heart estimates from waist based electrocardiograms (ECGs) poses a very challenging problem due to the presence of extreme motion artifacts. The literature reveals few, if any, attempts to apply motion artifact cancellation methods to waist based ECGs. This paper describes a new methodology for ameliorating the effects of motion artifacts in ECGs by specifically targeting ECG peaks for elimination that are determined to be correlated with accelerometer peaks. This peak space cancellation was applied to real world waist based ECGs. Algorithm Summary: The methodology includes successive applications of a previously described pattern-based heart beat detection scheme (Temporal Pattern Search, or TEPS) that can also detect patterns in other types of peak sequences. In the first application, TEPS is applied to accelerometer signals recorded contemporaneously with ECG signals to identify high-quality accelerometer peak sequences (SA) indicative of quasi-periodic motion likely to impair identification of peaks in a corresponding ECG signal. The process then performs ECG peak detection and locates the closest in time ECG peak to each peak in an SA. The differences in time between ECG and SA peaks are clustered. If the number of elements in a cluster of peaks in an SA exceeds a threshold, the ECG peaks in that cluster are removed from further processing. After this peak removal process, further QRS detection proceeds according to TEPS. Experiment: The above procedure was applied to data from real world experiments involving four sessions of walking and jogging on a dirt road for approximately 20-25 minutes. A compression shirt with textile electrodes served as the ground truth recording. A textile electrode based chest strap was worn around the waist to generate a single channel signal upon which to test peak space cancellation/TEPS. Results: Both walking and jogging heart rates were generally well tracked. In the four recordings, the percentage of 5 second segments within 10 beats/minute of reference was 96%, 99%, 92% and 96%. The percentage of segments within 5 beats/minute of reference was 86%, 90%, 82% and 78%. There was very good agreement between the RR intervals associated with the reference and waist recordings. For acceptable quality segments, the root mean square sum of successive RR interval differences (RMSSD) was calculated for both the reference and waist recordings. Next, the difference between waist and reference RMSSDs was calculated (∆RMSSD). The mean ∆RMSSD (over acceptable segments) was 4.6 m, 5.2 ms, 5.2 ms and 6.6 ms for the four recordings. Conclusion: Given that only one waist ECG channel was available, and that the strap used for the waist recording was not tailored for that purpose, the proposed methodology shows promise for waist based sinus rhythm QRS detection.

Blood Pressure Estimation from Photoplethysmography with Motion Artifacts Using Long Short Term Memory Network

Continuous measurement of the Blood Pressure (BP) is important in hypertensive patientsand elderly population. Traditional cuff based methods are difficult to use since it is uncomfortable towear a cuff throughout the day. A more suitable method is to estimate the BP using the Photoplethysmography(PPG) signal. However, it is difficult to estimate a BP when the PPG is corrupted withMotion Artifacts (MAs). In this paper, Long Short Term Memory (LSTM) an extension of RecurrentNeural Networks (RNN) is used used to improve the accuracy of the estimation of the BP from thecorrupted PPG. It shows that an accuracy of 97.86 is achieved.

Compressive Sensing Magnetic Resonance Image Reconstruction and Denoising using Convolutional Neural Network

Restoration of high-quality brain Magnetic Resonance Image (MRI) from the sparse under-sampled complex k-space signal is a widely studied ill-posed inverse transform problem. A deep learning-based data-adaptive and data-driven convolutional technique has been proposed for high-quality MRI recovery from its under-sampled complex domain k-space signal. The uniform subsampling process is very slow in phase-encoding to generate high-resolution images. The longer scan times degrade the perceptual image quality. Various factors contribute to image degradation during data acquisition such as the inception of body motion artifacts, the thermal energy effects of the body, and random noise artifacts due to voltage fluctuations. Keeping in view the patient’s critical condition and comfort, longer scan times are not preferred in practice. To reduce the image acquisition time, noise levels, and motion artifacts in the MR images, Compressive Sensing (CS) provides an accelerated way to reconstructs the high-quality MR image from very limited signal measurements acquired much below the Nyquist rate. However, such data acquisition strategies require advanced computer algorithms for the reconstruction of high-quality MRI from the undersampled MRI data. An improved CNN-based MRI reconstructed algorithm has been presented in this paper which shows better performance to reconstruct high-quality MRI than similar other MR image reconstruction algorithms. The performance of the proposed algorithm is measured by image quality checking tools such as normalized-MSE, PSNR, and SSIM.

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 in vivo. 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.

High-Resolution Simultaneous Multi-Slice Accelerated Turbo Spin-Echo Musculoskeletal Imaging: A Head-to-Head Comparison With Routine Turbo Spin-Echo Imaging

Background/Aim: The turbo spin-echo (TSE) sequence is widely used for musculoskeletal (MSK) imaging; however, its acquisition speed is limited and can be easily affected by motion artifacts. We aimed to evaluate whether the use of a simultaneous multi-slice TSE (SMS-TSE) sequence can accelerate MSK imaging while maintaining image quality when compared with the routine TSE sequence.Methods: We prospectively enrolled 71 patients [mean age, 37.43 ± 12.56 (range, 20–67) years], including 37 men and 34 women, to undergo TSE and SMS sequences. The total scanning times for the wrist, ankle and knee joint with routine sequence were 14.92, 13.97, and 13.48 min, respectively. For the SMS-TSE sequence, they were 7.52, 7.20, and 6.87 min. Quantitative parameters, including the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), were measured. Three experienced MSK imaging radiologists qualitatively evaluated the image quality of bone texture, cartilage, tendons, ligament, meniscus, and artifact using a 5-point evaluation system, and the diagnostic performance of the SMS-TSE sequences was evaluated.Results: Compared with the routine TSE sequences, the scanning time was lower by 49.60, 48.46, and 49.04% using SMS-TSE sequences for the wrist, ankle, and knee joints, respectively. For the SNR comparison, the SMS-TSE sequences were significantly higher than the routine TSE sequence for wrist (except for Axial-T2WI-FS), ankle, and knee joint MR imaging (all p < 0.05), but no statistical significance was obtained for the CNR measurement (all p > 0.05, except for Sag-PDWI-FS in ankle joint). For the wrist joint, the diagnostic sensitivity, specificity, and accuracy were 88.24, 100, and 92%. For the ankle joint, they were 100, 75, and 93.33%. For the knee joint, they were 87.50, 85.71, and 87.10%.Conclusion: The use of the SMS-TSE sequence in the wrist, ankle, and knee joints can significantly reduce the scanning time and show similar image quality when compared with the routine TSE sequence.

Comparison of static-fluid or excretory magnetic resonance urography with computed tomography urography for visualization of nondilated renal pelvises and ureters in healthy Beagles

OBJECTIVE To assess the usefulness of magnetic resonance urography (MRU) for the visualization of nondilated renal pelvises and ureters in dogs and to compare our findings for MRU versus CT urography (CTU). ANIMALS 9 healthy Beagles. PROCEDURES Dogs underwent CTU, static-fluid MRU, and excretory MRU, with ≥ 7 days between procedures. Contrast medium was administered IV during CTU and excretory MRU, whereas urine in the urinary tract was an intrinsic contrast medium for static-fluid MRU. For each procedure, furosemide (1 mg/kg, IV) was administered, and reconstructed dorsal plane images were acquired 3 minutes (n = 2) and 7 minutes (2) later. Images were scored for visualization of those structures and for image quality, diameters of renal pelvises and ureters were measured, and results were compared across imaging techniques. RESULTS Excretory MRU and CTU allowed good visualization of the renal pelvises and ureters, whereas static-fluid MRU provided lower visualization of the ureters. Distention of the renal pelvises and ureters was good in excretory MRU and CTU. Distention of the ureters in static-fluid MRU was insufficient compared with that in CTU and excretory MRU. Distinct artifacts were not observed in CTU and excretory MRU images. Static-fluid MRU images had several mild motion artifacts. CLINICAL RELEVANCE Our findings indicated that excretory MRU with furosemide administration was useful for visualizing nondilated renal pelvises and ureters of dogs in the present study. When performing MRU for the evaluation of dogs without urinary tract dilation, excretory MRU may be more suitable than static-fluid MRU.