A novel algorithm for multiplicative speckle noise reduction in ex vivo human brain OCT images

Optical coherence tomography (OCT) images of ex vivo human brain tissue are corrupted by multiplicative speckle noise that degrades the contrast to noise ratio (CNR) of microstructural compartments. This work proposes a novel algorithm to reduce noise corruption in OCT images that minimizes the penalized negative log likelihood of gamma distributed speckle noise. The proposed method is formulated as a majorize-minimize problem that reduces to solving an iterative regularized least squares optimization. We demonstrate the usefulness of the proposed method by removing speckle in simulated data, phantom data and real OCT images of human brain tissue. We compare the proposed method with state of the art filtering and non-local means based denoising methods. We demonstrate that our approach removes speckle accurately, improves CNR between different tissue types and better preserves small features and edges in human brain tissue.

Improvement of diffusion weighted MRI by practical B0 homogenization for head & neck cancer patients undergoing radiation therapy

BACKGROUND

MRI is a frequently used tool in radiation therapy planning. For MR-based tumor segmentation, diffusion weighted imaging plays a major role, which can fail due to excessive image artifacts for head and neck cancer imaging. Here, an easy-to-use setup is presented for imaging of head and neck cancer patients in a radiotherapy thermoplastic fixation mask.

METHODS

In a prospective head and neck cancer study, MRI data of 29 patients has been acquired at 3 different time points during radiation treatment. The data was analyzed with respect to Nyquist ghosting artifacts in the diffusion images in conventional single shot and readout segmented EPI sequences. For 9 patients, an improved setup with water bags for B₀ homogenization was used, and the impact on artifact frequency was analyzed. Additionally, volunteer measurements with B₀ fieldmaps are presented.

RESULTS

The placement of water bags to the sides of the head during MRI measurements significantly reduces artefacts in diffusion MRI. The number of artifact-free images in readout segmented EPI increased from 74% to 95% of the cases. Volunteer measurements showed a significant increase in B₀ homogeneity across slices (head foot direction) as well as within each slice.

CONCLUSIONS

The placement of water bags for B₀ homogenization is easy to implement, cost-efficient and does not impact patient comfort. Therefore, if very sophisticated soft- or hardware solutions are not present at a given site, or cannot be implemented due to restrictions from the thermoplastic mask, this is an excellent alternative to reduce artifacts in diffusion weighted imaging.

Automatic classification of ICA components from infant EEG using MARA

Automated systems for identifying and removing non-neural ICA components are growing in popularity among EEG researchers of adult populations. Infant EEG data differs in many ways from adult EEG data, but there exists almost no specific system for automated classification of source components from paediatric populations. Here, we adapt one of the most popular systems for adult ICA component classification for use with infant EEG data. Our adapted classifier significantly outperformed the original adult classifier on samples of naturalistic free play EEG data recorded from 10 to 12-month-old infants, achieving agreement rates with the manual classification of over 75% across two validation studies (n = 44, n = 25). Additionally, we examined both classifiers’ ability to remove stereotyped ocular artifact from a basic visual processing ERP dataset compared to manual ICA data cleaning. Here, the new classifier performed on level with expert manual cleaning and was again significantly better than the adult classifier at removing artifact whilst retaining a greater amount of genuine neural signal operationalised through comparing ERP activations in time and space. Our new system (iMARA) offers developmental EEG researchers a flexible tool for automatic identification and removal of artifactual ICA components.

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Currently, few tools are available for ICA based data correction that are designed around infant EEG.

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ICA correction is necessary for naturalistic paradigms when movement artifacts covary with cognitive processes.

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We present a system for automated ICA classification designed around infant EEG data.

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This system offers increased performance compared to systems of automatic ICA classification designed around adult EEG.

Geodesic density regression for correcting 4DCT pulmonary respiratory motion artifacts

Pulmonary respiratory motion artifacts are common in four-dimensional computed tomography (4DCT) of lungs and are caused by missing, duplicated, and misaligned image data. This paper presents a geodesic density regression (GDR) algorithm to correct motion artifacts in 4DCT by correcting artifacts in one breathing phase with artifact-free data from corresponding regions of other breathing phases. The GDR algorithm estimates an artifact-free lung template image and a smooth, dense, 4D (space plus time) vector field that deforms the template image to each breathing phase to produce an artifact-free 4DCT scan. Correspondences are estimated by accounting for the local tissue density change associated with air entering and leaving the lungs, and using binary artifact masks to exclude regions with artifacts from image regression. The artifact-free lung template image is generated by mapping the artifact-free regions of each phase volume to a common reference coordinate system using the estimated correspondences and then averaging. This procedure generates a fixed view of the lung with an improved signal-to-noise ratio. The GDR algorithm was evaluated and compared to a state-of-the-art geodesic intensity regression (GIR) algorithm using simulated CT time-series and 4DCT scans with clinically observed motion artifacts. The simulation shows that the GDR algorithm has achieved significantly more accurate Jacobian images and sharper template images, and is less sensitive to data dropout than the GIR algorithm. We also demonstrate that the GDR algorithm is more effective than the GIR algorithm for removing clinically observed motion artifacts in treatment planning 4DCT scans. Our code is freely available at https://github.com/Wei-Shao-Reg/GDR.

Iterative stripe artifact correction framework for TOF-MRA

The purpose of this study is to develop a practical stripe artifacts correction framework on three-dimensional (3-D) time-of-flight magnetic resonance angiography (TOF-MRA) obtained by multiple overlapping thin slab acquisitions (MOTSA) technology. In this work, the stripe artifacts in TOF-MRA were considered as a part of image texture. To separate the image structure and the texture, the relative total variation (RTV) was firstly employed to smooth the TOF-MRA for generating the template image with fewer image textures. Then a residual image was generated, which was the difference between the template image and the raw TOF-MRA. The residual image was served as the image texture, which contained the image details and stripe artifacts. Then, we obtained the artifact image from the residual image via a filter in a specific direction since the image artifacts appeared as stripes. The image details were then produced from the difference between the artifact image and the image texture. To produce the corrected images, we finally compensated the image details to the RTV smoothing image. The proposed method was continued until the stripe artifacts during the iteration vary as little as possible. The digital phantom and the real patients' TOF-MRA were used to test the approach. The spatial uniformity was increased from 74% to 82% and the structural similarity was improved from 86% to 98% in the digital phantom test by using the proposed algorithm. Our approach proved to be highly successful in eliminating stripe artifacts in real patient data tests while retaining image details. The proposed iterative framework on TOF-MRA stripe artifact correction is effective and appealing for enhancing the imaging performance of multi-slab 3-D acquisitions.

Evaluation of algorithms for correction of transcranial magnetic stimulation-induced artifacts in electroencephalograms

Transcranial magnetic stimulation combined with electroencephalography (TMS-EEG) is widely used to study the reactivity and connectivity of brain regions for clinical or research purposes. The electromagnetic pulse of the TMS device generates at the instant of administration an artifact of large amplitude and a duration up to tens of milliseconds that overlaps with brain activity. Methods for TMS artifact correction have been developed to remove the artifact and recover the underlying, immediate response of the cerebral cortex to the magnetic stimulus. In this study, four such algorithms are evaluated. Since there is no ground truth for the masked brain activity, pilot data formed from the superposition of the isolated TMS artifact on EEG brain activity are used to evaluate the performance of the algorithms. Different scenarios of TMS-EEG experiments are considered for the evaluation: TMS at resting state, TMS inducing epileptiform discharges, and TMS administered during epileptiform discharges. We show that a proposed gap filling method is able to reproduce qualitative characteristics and, in many cases, closely resemble the hidden EEG signal. Finally, shortcomings of the TMS correction algorithms as well as the pilot data approach are discussed. Graphical abstract The transcranial magnetic stimulation (TMS) artifact on the electroencephalogram (EEG) and its correction.

Can heart rate variability parameters derived by a heart rate monitor differentiate between atrial fibrillation and sinus rhythm?

BACKGROUND

Heart rate variability (HRV) parameters, and especially RMSSD (root mean squared successive differences in RR interval), could distinguish atrial fibrillation (AF) from sinus rhythm(SR) in horses, as was demonstrated in a previous study. If heart rate monitors (HRM) automatically calculating RMSSD could also distinguish AF from SR, they would be useful for the monitoring of AF recurrence. The objective of the study was to assess whether RMSSD values obtained from a HRM can differentiate AF from SR in horses. Furthermore, the impact of artifact correction algorithms, integrated in the analyses software for HRV analyses was evaluated. Fourteen horses presented for AF treatment were simultaneously equipped with a HRM and an electrocardiogram (ECG). A two-minute recording at rest, walk and trot, before and after cardioversion, was obtained. RR intervals used were those determined automatically by the HRM and by the equine ECG analysis software, and those obtained after manual correction of QRS detection within the ECG software. RMSSD was calculated by the HRM software and by dedicated HRV software, using six different artifact filters. Statistical analysis was performed using the Wilcoxon signed-rank test and receiver operating curves.

RESULTS

The HRM, which applies a low level filter, produced high area under the curve (AUC) (> 0.9) and cut off values with high sensitivity and specificity. Similar results were obtained for the ECG, when low level artifact filtering was applied. When no artifact correction was used during trotting, an important decrease in AUC (0.75) occurred.

CONCLUSION

In horses treated for AF, HRMs with automatic RMSSD calculations distinguish between AF and SR. Such devices might be a useful aid to monitor for AF recurrence in horses.

Evaluation of the accuracy and precision of the diffusion parameter EStImation with Gibbs and NoisE removal pipeline

This work evaluates the accuracy and precision of the Diffusion parameter EStImation with Gibbs and NoisE Removal (DESIGNER) pipeline, developed to identify and minimize common sources of methodological variability including: thermal noise, Gibbs ringing artifacts, Rician bias, EPI and eddy current induced spatial distortions, and motion-related artifacts. Following this processing pipeline, iterative parameter estimation techniques were used to derive diffusion parameters of interest based on the diffusion tensor and kurtosis tensor. We evaluated accuracy using a software phantom based on 36 diffusion datasets from the Human Connectome project and tested the precision by analyzing data from 30 healthy volunteers scanned three times within one week. Preprocessing with both DESIGNER or a standard pipeline based on smoothing (instead of noise removal) improved parameter precision by up to a factor of 2 compared to preprocessing with motion correction alone. When evaluating accuracy, we report average decreases in bias (deviation from simulated parameters) over all included regions for fractional anisotropy, mean diffusivity, mean kurtosis, and axonal water fraction of 9.7%, 8.7%, 4.2%, and 7.6% using DESIGNER compared to the standard pipeline, demonstrating that preprocessing with DESIGNER improves accuracy compared to other processing methods.

Correction of scan line shift artifacts in scanning electron microscopy: An extended digital image correlation framework

High resolution scanning electron microscopy (HR-SEM) is nowadays very popular for different applications in different fields. However, SEM images may exhibit a considerable amount of imaging artifacts, which induce significant errors if the images are used to measure geometrical or kinematical fields. This error is most pronounced in case of full field deformation measurements, for instance by digital image correlation (DIC). One family of SEM artifacts result from positioning errors of the scanning electron beam, creating artifactual shifts in the images perpendicular to the scan lines (scan line shifts). This leads to localized distortions in the displacement fields obtained from such images, by DIC. This type of artifacts is corrected here using global DIC (GDIC). A novel GDIC framework, considering the nonlinear influence of artifacts in the imaging system, is introduced for this purpose. Using an enriched regularization in the global DIC scheme, based on an error function, the scan line shift artifacts are captured and eliminated. The proposed methodology is demonstrated in virtually generated and deformed images as well as real SEM micrographs. The results confirm the proper detection and elimination of this type of SEM artifacts.

The Importance of Properly Compensating for Head Movements During MEG Acquisition Across Different Age Groups

Unlike EEG sensors, which are attached to the head, MEG sensors are located outside the head surface on a fixed external device. Subject head movements during acquisition thus distort the magnetic field distributions measured by the sensors. Previous studies have looked at the effect of head movements, but no study has comprehensively looked at the effect of head movements across age groups, particularly in infants. Using MEG recordings from subjects ranging in age from 3 months through adults, here we first quantify the variability in head position as a function of age group. We then combine these measured head movements with brain activity simulations to determine how head movements bias source localization from sensor magnetic fields measured during movement. We find that large amounts of head movement, especially common in infant age groups, can result in large localization errors. We then show that proper application of head movement compensation techniques can restore localization accuracy to pre-movement levels. We also find that proper noise covariance estimation (e.g., during the baseline period) is important to minimize localization bias following head movement compensation. Our findings suggest that head position measurement during acquisition and compensation during analysis is recommended for researchers working with subject populations or age groups that could have substantial head movements. This is especially important in infant MEG studies.

Direction and viewing area-sensitive influence of EOG artifacts revealed in the EEG topographic pattern analysis

The influence of eye movement-related artifacts on electroencephalography (EEG) signals of human subjects, who were requested to perform a direction or viewing area dependent saccade task, was investigated by using a simultaneous recording with ocular potentials as electro-oculography (EOG). In the past, EOG artifact removals have been studied in tasks with a single fixation point in the screen center, with less attention to the sensitivity of cornea-retinal dipole orientations to the EEG head map. In the present study, we hypothesized the existence of a systematic EOG influence that differs according to coupling conditions of eye-movement directions with viewing areas including different fixation points. The effect was validated in the linear regression analysis by using 12 task conditions combining horizontal/vertical eye-movement direction and three segregated zones of gaze in the screen. In the first place, event-related potential topographic patterns were analyzed to compare the 12 conditions and propagation coefficients of the linear regression analysis were successively calculated in each condition. As a result, the EOG influences were significantly different in a large number of EEG channels, especially in the case of horizontal eye-movements. In the cross validation, the linear regression analysis using the appropriate dataset of the target direction/viewing area combination demonstrated an improved performance compared with the traditional methods using a single fixation at the center. This result may open a potential way to improve artifact correction methods by considering the systematic EOG influence that can be predicted according to the view angle such as using eye-tracker systems.

Correction of Non-Linear Propagation Artifact in Contrast-Enhanced Ultrasound Imaging of Carotid Arteries: Methods and in Vitro Evaluation

Non-linear propagation of ultrasound creates artifacts in contrast-enhanced ultrasound images that significantly affect both qualitative and quantitative assessments of tissue perfusion. This article describes the development and evaluation of a new algorithm to correct for this artifact. The correction is a post-processing method that estimates and removes non-linear artifact in the contrast-specific image using the simultaneously acquired B-mode image data. The method is evaluated on carotid artery flow phantoms with large and small vessels containing microbubbles of various concentrations at different acoustic pressures. The algorithm significantly reduces non-linear artifacts while maintaining the contrast signal from bubbles to increase the contrast-to-tissue ratio by up to 11 dB. Contrast signal from a small vessel 600 μm in diameter buried in tissue artifacts before correction was recovered after the correction.

Pupil size dynamics during fixation impact the accuracy and precision of video-based gaze estimation

Video-based eye tracking relies on locating pupil center to measure gaze positions. Although widely used, the technique is known to generate spurious gaze position shifts up to several degrees in visual angle because pupil centration can change without eye movement during pupil constriction or dilation. Since pupil size can fluctuate markedly from moment to moment, reflecting arousal state and cognitive processing during human behavioral and neuroimaging experiments, the pupil size artifact is prevalent and thus weakens the quality of the video-based eye tracking measurements reliant on small fixational eye movements. Moreover, the artifact may lead to erroneous conclusions if the spurious signal is taken as an actual eye movement. Here, we measured pupil size and gaze position from 23 human observers performing a fixation task and examined the relationship between these two measures. Results disclosed that the pupils contracted as fixation was prolonged, at both small (<16s) and large (∼4min) time scales, and these pupil contractions were accompanied by systematic errors in gaze position estimation, in both the ellipse and the centroid methods of pupil tracking. When pupil size was regressed out, the accuracy and reliability of gaze position measurements were substantially improved, enabling differentiation of 0.1° difference in eye position. We confirmed the presence of systematic changes in pupil size, again at both small and large scales, and its tight relationship with gaze position estimates when observers were engaged in a demanding visual discrimination task.

Quantitative electron tomography: the effect of the three-dimensional point spread function

The intensity levels in a three-dimensional (3D) reconstruction, obtained by electron tomography, can be influenced by several experimental imperfections. Such artifacts will hamper a quantitative interpretation of the results. In this paper, we will correct for artificial intensity variations by determining the 3D point spread function (PSF) of a tomographic reconstruction based on high angle annular dark field scanning transmission electron microscopy. The large tails of the PSF cause an underestimation of the intensity of smaller particles, which in turn hampers an accurate radius estimate. Here, the error introduced by the PSF is quantified and corrected a posteriori.