Signal-to-noise Research Articles

Switchable emissions of an Erbium-doped fiber laser using cascaded MZIs based on CHCF

In this work, a single and dual-wavelength erbium-doped fiber laser (EDFL) based on two Mach-Zehnder interferometers (MZIs) in cascade structure was experimentally validated. MZIs were assembled by joining a capillary hollow-core fiber (CHCF) piece between two multimode fibers (MMFs) sections. The switchable operation is reached by moving the spectrum of one MZI when the temperature is increased. The maximum measured signal noise to ratio (SNR) was more than 50 dB for the single and dual-wavelength laser lines. Besides, stable output is shown since no power and wavelength variations were noticed. It is important to mention that emissions are obtained at precise wavelength positions and not arbitrarily as described by other investigations. This EDFL can be used in applications of optical fiber communications systems and fiber sensing.

Spatiotemporal energy regulation strategy for enhancing SNR and spatial resolution in the damage detection with SH0 waves

The fundamental shear horizontal (SH0) wave, which can be generated by electromagnetic acoustic transducers (EMATs), is widely used in the structural health monitoring fields owing to its non-dispersive nature and the ability to detect over long distances. However, current SH0 wave methods hardly achieve the excellent signal-to-noise ratio (SNR) of the echo signals and satisfactory spatial resolution to defects due to the co-restriction between them. To address these issues, a spatiotemporal energy regulation strategy (STERS) is proposed in this paper to optimize energy distribution in the temporal and spatial dimensions. First, spatiotemporal pulse compression technology (STPCT) is proposed to improve both SNR and spatial range resolution for defect detection. Second, a staggered dual-exciter employing two sets of excitation signals is designed to improve the spatial azimuth resolution. Besides, a double sidelobe suppression algorithm (DSSA) is proposed to suppress sidelobe issues arising from the application of STPCT, thereby further improving the performance of STPCT. Finally, multiple simulations and experiments are executed to demonstrate the advantages of the proposed damage detection system. Results show that our methods can achieve comprehensive improvement in SNR, spatial range resolution, spatial azimuth resolution, and sidelobe suppression ability.

Motion robust coronary MR angiography using zigzag centric ky-kz trajectory and high-resolution deep learning reconstruction.

To develop a new MR coronary angiography (MRCA) technique by employing a zigzag fan-shaped centric ky-kz k-space trajectory combined with high-resolutiondeep learning reconstruction (HR-DLR). All imaging data were acquired from 12 healthy subjects and 2 patients using two clinical 3-T MR imagers, with institutional review board approval. Ten healthy subjects underwent both standard 3D fast gradient echo (sFGE) and centric ky-kz k-space trajectory FGE (cFGE) acquisitions to compare the scan time and image quality. Quantitative measures were also performed for signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) as well as sharpness of the vessel. Furthermore, the feasibility of the proposed cFGE sequence was assessed in two patients. For assessing the feasibility of the centric ky-kz trajectory, the navigator-echo window of a 30-mm threshold was applied in cFGE, whereas sFGE was applied using a standard 5-mm threshold. Image quality of MRCA using cFGE with HR-DLR and sFGE without HR-DLR was scored in a 5-point scale (non-diagnostic = 1, fair = 2, moderate = 3, good = 4, and excellent = 5). Image evaluation of cFGE, applying HR-DLR, was compared with sFGE without HR-DLR. Friedman test, Wilcoxon signed-rank test, or paired t tests were performed for the comparison of related variables. The actual MRCA scan time of cFGE with a 30-mm threshold was acquired in less than 5min, achieving nearly 100% efficiency, showcasing its expeditious and robustness. In contrast, sFGE was acquired with a 5-mm threshold and had an average scan time of approximately 15min. Overall image quality for MRCA was scored 3.3 for sFGE and 2.7 for cFGE without HR-DLR but increased to 3.6 for cFGE with HR-DLR and (p < 0.05). The clinical result of patients obtained within 5min showed good quality images in both patients, even with a stent, without artifacts. Quantitative measures of SNR, CNR, and sharpness of vessel presented higher in cFGE with HR-DLR. Our findings demonstrate a robust, time-efficient solution for high-quality MRCA, enhancing patient comfort and increasing clinical throughput.

Seismic net-to-gross estimation for a geologic model update: A case study from a turbidite lobe reservoir in the deepwater of the Niger Delta

Geologic model updates are routinely performed in mature fields to obtain improved descriptions of facies distributions and reservoir properties such as volume of shale, net to gross (NTG), and porosity, for better understanding of the static and dynamic behaviors of reservoirs for effective well placement, improved production, and monitoring. A simple but integrated seismic NTG estimation approach, using detuned seismic amplitudes is used in guiding NTG modeling during the geologic model update of a thin turbidite lobe reservoir in a mature oil field in the deep offshore Niger Delta. The objective is to address NTG overestimation and gross rock volume (GRV) uncertainty in a previous model arising from seismic amplitude tuning effects. A seismic NTG approach is chosen relative to sophisticated deterministic or stochastic inversion techniques to avoid tuning effects, which usually bias NTG estimates in thin turbidite reservoirs. The primary data set is a 2019 reprocessed prestack depth-migrated (PSDM) seismic data vintage, which had better resolution, higher signal-to-noise ratio, and more appropriate angle-stack apertures for amplitude variation with angle fidelity, than the older 2011 PSDM seismic data that are used in the previous geologic model. The methodology involved rock-physics analysis, seismic data quality checks (QCs), tuned area determination, detuning of composite seismic amplitudes of the top and base reservoir, and their direct calibration to NTG at wells. Good correlations are obtained between the detuned composite seismic amplitudes and NTG at wells. The seismic NTG map shows good calibrations at wells and provides a robust trend for net sand modeling in the oil pool and aquifer. Static model QCs and dynamic simulations prove that the seismic NTG attribute addressed the GRV uncertainty in the earlier model, thus giving confidence for using the updated model for the planning and geosteering of infill wells, sand completions, reservoir monitoring, and production.

Noise2Variance: Dual networks with variance constraint for self‐supervised real‐world image denoising

AbstractImage denoising aims to restore a clean image from a noisy image. Traditional methods utilizing convolutional neural networks (CNN) for denoising are trained using pairs of noisy and clean images to comprehend the transformation from a noisy image to a clean one. However, the acquisition of such image pairs in real‐world scenarios presents a challenge. Hence, numerous self‐supervised denoising techniques have been developed that do not require clean images for training. This study demonstrates that a straightforward loss design, concentrating on variance, can effectively train a standard CNN denoiser in a self‐supervised fashion. A novel theoretical framework is introduced for training a basic CNN denoising model using three constraints: mean, variance, and augmentation. The variance constraint is crucial as it prevents the trained model from converging to trivial solutions such as identity or zero mapping. This theory provides valuable insights for the development of new self‐supervised denoising methods. Furthermore, a method that applies this theory to proposed dual networks is developed, which consist of two standard CNN models predicting both the clean image and the noise. This approach enhances model capacity during training while minimizing computational costs during inference. This method exemplifies the implementation of the variance constraint and introduces a data constraint for dual networks. Notably, the proposed method only assumes the presence of additive white noise, irrespective of the noise distribution. This minimal assumption enhances the model's robustness against noise with complex or unknown distributions in real‐world distorted images. Experimental results indicate that the proposed Noise2Variance method exhibits commendable performance on peak signal noise ratio and structural similarity metrics compared to existing self‐supervised denoising techniques. Visual comparison of results further substantiates the efficacy of the proposed method. A comparison of model complexity reveals that the method is efficient among the compared CNN‐based techniques.

Subjective Speech Intelligibility Drives Noise-Tolerance Domain Use During the Tracking of Noise-Tolerance Test.

Recently, the Noise-Tolerance Domains Test (NTDT) was applied to study the noise-tolerance domains used by young normal-hearing (NH) listeners during noise acceptance decisions. In this study, we examined how subjective speech intelligibility may drive noise acceptance decisions by applying the NTDT on NH and hearing-impaired (HI) listeners at the signal to noise ratios (SNRs) around the Tracking of Noise-Tolerance (TNT) thresholds. A single-blind, within-subjects design with 22 NH and 17 HI older adults was followed. Listeners completed the TNT to determine the average noise acceptance threshold (TNTAve). Then, listeners completed the NTDT at the SNRs of 0, ±3 dB (re: TNTAve) to estimate the weighted noise-tolerance domain ratings (WNTDRs) for each domain criterion. Listeners also completed the Objective and Subjective Intelligibility Difference (OSID) Test to establish the individual intelligibility performance-intensity (P-I) functions of the TNT materials. All test measures were conducted at 75 and 82 dB SPL speech input levels. NH and HI listeners were tested in the unaided mode. The HI listeners were also tested using a study hearing aid. The WNTDRs were plotted against subjective speech intelligibilities extrapolated from individual P-I of the OSID at the SNRs corresponding to NTDT test conditions. Listeners were grouped according to their most heavily weighed domain and a regression analysis was performed against listener demographics as well as TNT and OSID performances to determine which variable(s) affected listener grouping. Three linear mixed effects (LMEs) models were used to examine whether WNTDRs changed with subjective speech intelligibility. All three LMEs found significant fixed effects of domain criteria, subjective intelligibility, and speech input level on WNTDRs. In general, heavier weights were assigned to speech interference and loudness domains at poorer intelligibility levels (<50%) with reversals to distraction and annoyance at higher intelligibility levels (>80%). The comparison between NH and HI-unaided showed that NH listeners assigned greater weights to loudness than the HI-unaided listeners. The comparison between NH and HI-aided groups showed similar weights between groups. The comparison between HI-unaided and HI-aided found that HI listeners assigned lower weights to speech interference and greater weights to loudness when tested in aided compared with unaided modes. In all comparisons, loudness was weighed heavier at the 82 dB SPL input level than at the 75 dB SPL input level with greater weights to annoyance in the NH versus HI-unaided comparison and lower weights to distraction in the HI-aided versus HI-unaided comparison. A generalized linear model determined that listener grouping was best accounted for by subjective speech intelligibility estimated at TNTAve. The domain criteria used by listeners were driven by their subjective speech intelligibility regardless of their hearing status (i.e., NH versus HI). In general, when subjective intelligibility was poor, the domains of speech interference and loudness were weighed the heaviest. As subjective intelligibility improved, the weightings on annoyance and distraction increased. Furthermore, a listener's criterion for >90% subjective speech understanding at the TNTAve may allow one to profile the listener.

A comparison of denoising approaches for spoken word production related artefacts in continuous multiband fMRI data

Abstract It is well-established from fMRI experiments employing gradient echo echo-planar imaging (EPI) sequences that overt speech production introduces signal artefacts compromising accurate detection of task-related responses. Both design and post-processing ("denoising") techniques have been proposed and implemented over the years to mitigate the various noise sources. Recently, fMRI studies of speech production have begun to adopt multiband EPI sequences that offer better signal-to-noise ratio (SNR) and temporal resolution allowing adequate sampling of physiological noise sources (e.g., respiration, cardiovascular effects) and reduced scanner acoustic noise. However, these new sequences may also introduce additional noise sources. In this study, we demonstrate the impact of applying several noise-estimation and removal approaches to continuous multiband fMRI data acquired during a naming-to-definition task, including rigid body motion regression and outlier censoring, principal component analysis for removal of CSF/edge-related noise components, and global fMRI signal regression (using two different approaches) compared to a baseline of realignment and unwarping alone. Our results show the strongest and most spatially extensive sources of physiological noise are the global signal fluctuations arising from respiration and muscle action and CSF/edge-related noise components, with residual rigid body motion contributing relatively little variance. Interestingly, denoising approaches tended to reduce and enhance task-related BOLD signal increases and decreases, respectively. Global signal regression using a voxel-wise linear model of the global signal estimated from unmasked data resulted in dramatic improvements in temporal SNR. Overall, these findings show the benefits of combining continuous multiband EPI sequences and denoising approaches to investigate the neurobiology of speech production.

Spatial Spectrum Estimation of Weak Scattering Wave Signal in Range-Doppler Domain

How to enhance the desired signal with low signal-to-noise ratio (SNR) is a difficult problem in the estimation process of the direction-of-arrival (DOA) of the target scattering wave signal. In this paper, the feasibility of spatial spectrum estimation in the Range-Doppler (RD) domain is analyzed in principle, and the SNR gain expression of weak scattering wave signal is derived when constructing multi-snapshots virtual array data. On this basis, the mutual eigenvector singular value decomposition (MESVD) method based on RD domain mode excitation is proposed, which can robustly and effectively estimate the direction of the coherent weak signals. Simulation experiments verify that the RD domain spectral estimation method has the ability to simultaneously obtain the direction of multiple weak target scattering waves, and the direction-finding accuracy can reach the Cramer–Rao bound (CRB) of conventional spectral estimation method. The results of Monte Carlo experiments show that the root-mean-square-error (RMSE) of azimuth estimation of RD domain spatial spectrum estimation method is 5.76° lower than that of a conventional multiple signal classification (MUSIC) method. In addition, the practicability of the proposed method is demonstrated by comparing the DOA estimation results of a set of real data with Automatic Dependent Surveillance-Broadcast (ADS-B) data.

Real-Time Attentive Dilated U-Net for Extremely Dark Image Enhancement

Images taken under low-light conditions suffer from poor visibility, color distortion, and graininess, all of which degrade the image quality and hamper the performance of downstream vision tasks, such as object detection and instance segmentation in the field of autonomous driving, making low-light enhancement an indispensable basic component of high-level visual tasks. Low-light enhancement aims to mitigate these issues, and has garnered extensive attention and research over several decades. The primary challenge in low-light image enhancement arises from the low signal-to-noise ratio caused by insufficient lighting. This challenge becomes even more pronounced in near-zero lux conditions, where noise overwhelms the available image information. Both traditional image signal processing pipeline and conventional low-light image enhancement methods struggle in such scenarios. Recently, deep neural networks have been used to address this challenge. These networks take unmodified RAW images as input and produce the enhanced sRGB images, forming a deep learning based image signal processing pipeline. However, most of these networks are computationally expensive and thus far from practical use. In this article, we propose a lightweight model called attentive dilated U-Net (ADU-Net) to tackle this issue. Our model incorporates several innovative designs, including an asymmetric U-shape architecture, dilated residual modules for feature extraction, and attentive fusion modules for feature fusion. The dilated residual modules provide strong representative capability, whereas the attentive fusion modules effectively leverage low-level texture information and high-level semantic information within the network. Both modules employ a lightweight design but offer significant performance gains. Extensive experiments demonstrate that our method is highly effective, achieving an excellent balance between image quality and computational complexity—that is, taking less than 4ms for a high-definition 4K image on a single GTX 1080Ti GPU and yet maintaining competitive visual quality. Furthermore, our method exhibits pleasing scalability and generalizability, highlighting its potential for widespread applicability.

The Effects of Light Level and Signal-to-Noise Ratio on the Task-Evoked Pupil Response in a Speech-in-Noise Task.

There is increasing interest in the measurement of cognitive effort during listening tasks, for both research and clinical purposes. Quantification of task-evoked pupil responses (TEPRs) is a psychophysiological method that can be used to study cognitive effort. However, light level during cognitively demanding listening tasks may affect TEPRs, complicating interpretation of listening-related changes. The objective of this study was to examine the effects of light level on TEPRs during effortful listening across a range of signal-to-noise ratios (SNRs). Thirty-six adults without hearing loss were asked to repeat target sentences presented in background babble noise while their pupil diameter was recorded. Light level and SNRs were manipulated in a 4 × 4 repeated-measures design. Repeated-measures analyses of variance were used to measure the effects. Peak and mean dilation were typically larger in more adverse SNR conditions (except for SNR -6 dB) and smaller in higher light levels. Differences in mean and peak dilation between SNR conditions were larger in dim light than in brighter light. Brighter light conditions make TEPRs less sensitive to variations in listening effort across levels of SNR. Therefore, light level must be considered and reported in detail to ensure sensitivity of TEPRs and for comparisons of findings across different studies. It is recommended that TEPR testing be conducted in relatively low light conditions, considering both background illumination and screen luminance. https://doi.org/10.23641/asha.25676538.

Repeatability of image quality in very low-field MRI.

Very low-field MR has emerged as a promising complementary device to high-field MRI scanners, offering several advantages. One of the key benefits is that very low-field scanners are generally more portable and affordable to purchase and maintain, making them an attractive option for medical facilities looking to reduce costs. Very low-field MRI systems also have lower RF power deposition, making them safer and less likely to cause tissue heating or other safety concerns. They are also simpler to maintain, as they do not require cooling agents such as liquid helium. However, these portable MR scanners are impacted by temperature, lower magnetic field strength, and inhomogeneity, resulting in images with lower signal-to-noise ratio (SNR) and higher geometric distortions. It is essential to investigate and tabulate the variations in these parameters to establish bounds so that subsequent in vivo studies and deployment of these portable systems can be well informed. The aim of this work is to investigate the repeatability of image quality metrics such as SNR and geometrical distortion at 0.05 T over 10 days and three sessions per day. We acquired repeatability data over 10 days with three sessions per day. The measurements included temperature, humidity, transmit frequency, off-resonance maps, and 3D turbo spin echo (TSE) images of an in vitro phantom. This resulted in a protocol with 11 sequences. We also acquired a 3 T data set for reference. The image quality metrics included computing SNR and eccentricity (to assess geometrical distortion) to investigate the repeatability of 0.05 T image quality. The image reconstruction included drift correction, k-space filtering, and off-resonance correction. We computed the experimental parameters' coefficient of variation (CV) and the resulting image quality metrics to assess repeatability. We have explored the impact of electromagnetic interference (EMI) on image quality in very low-field MRI. The investigation involved varying both the distance and amplitude of the EMI-producing coil from the signal generator to analyze their effects on image quality. The range of temperature measured during the study was within 1.5 °C. The off-resonance maps acquired before and after the 3D TSE showed similar hotspots and were changed mainly by a global constant. The SNR measurements were highly repeatable across sessions and over the 10 days, quantified by a CV of 6.7%. The magnetic field inhomogeneity effects quantified by eccentricity showed a CV of 13.7%, but less than 5.1% in two of the three sessions over 10 days. The use of conjugate phase reconstruction mitigated geometrical distortion artifacts. Temperature and humidity did not significantly affect SNR or mean frequency drift within the ranges of these environmental factors investigated. The EMI experiment showed that as the amplitude increased the SNR decreased, and concurrently the root mean square of the background increased with a rise in EMI amplitude or a reduction in distance. We found that humidity and temperature in the range investigated did not impact SNR or frequency. Based on the CV values computed session-wise and for the overall study, our findings indicate high repeatability for SNR and magnetic field homogeneity.

Time-Resolved Dynamic Optical Coherence Tomography for Retinal Blood Flow Analysis.

Optical coherence tomography (OCT) representations in clinical practice are static and do not allow for a dynamic visualization and quantification of blood flow. This study aims to present a method to analyze retinal blood flow dynamics using time-resolved structural OCT. We developed novel imaging protocols to acquire video-rate time-resolved OCT B-scans (1024 × 496 pixels, 10 degrees field of view) at four different sensor integration times (integration time of 44.8 µs at a nominal A-scan rate of 20 kHz, 22.4 µs at 40 kHz, 11.2 µs at 85 kHz, and 7.24 µs at 125 kHz). The vessel centers were manually annotated for each B-scan and surrounding subvolumes were extracted. We used a velocity model based on signal-to-noise ratio (SNR) drops due to fringe washout to calculate blood flow velocity profiles in vessels within five optic disc diameters of the optic disc rim. Time-resolved dynamic structural OCT revealed pulsatile SNR changes in the analyzed vessels and allowed the calculation of potential blood flow velocities at all integration times. Fringe washout was stronger in acquisitions with longer integration times; however, the ratio of the average SNR to the peak SNR inside the vessel was similar across all integration times. We demonstrated the feasibility of estimating blood flow profiles based on fringe washout analysis, showing pulsatile dynamics in vessels close to the optic nerve head using structural OCT. Time-resolved dynamic OCT has the potential to uncover valuable blood flow information in clinical settings.

Low-iodine-dose computed tomography coupled with an artificial intelligence-based contrast-boosting technique in children: a retrospective study on comparison with conventional-iodine-dose computed tomography.

Low-iodine-dose computed tomography (CT) protocols have emerged to mitigate the risks associated with contrast injection, often resulting in decreased image quality. To evaluate the image quality of low-iodine-dose CT combined with an artificial intelligence (AI)-based contrast-boosting technique in abdominal CT, compared to a standard-iodine-dose protocol in children. This single-center retrospective study included 35 pediatric patients (mean age 9.2years, range 1-17years) who underwent sequential abdominal CT scans-one with a standard-iodine-dose protocol (standard-dose group, Iobitridol 350 mgI/mL) and another with a low-iodine-dose protocol (low-dose group, Iohexol 240 mgI/mL)-within a 4-month interval from January 2022 to July 2022. The low-iodine CT protocol was reconstructed using an AI-based contrast-boosting technique (contrast-boosted group). Quantitative and qualitative parameters were measured in the three groups. For qualitative parameters, interobserver agreement was assessed using the intraclass correlation coefficient, and mean values were employed for subsequent analyses. For quantitative analysis of the three groups, repeated measures one-way analysis of variance with post hoc pairwise analysis was used. For qualitative analysis, theFriedman test followed by post hoc pairwise analysis was used. Paired t-tests were employed to compare radiation dose and iodine uptake between the standard- and low-dose groups. The standard-dose group exhibited higher attenuation, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR) of organs and vessels compared to the low-dose group (all P-values < 0.05 except for liver SNR, P = 0.12). However, noise levels did not differ between the standard- and low-dose groups (P = 0.86). The contrast-boosted group had increased attenuation, CNR, and SNR of organs and vessels, and reduced noise compared with the low-dose group (all P < 0.05). The contrast-boosted group showed no differences in attenuation, CNR, and SNR of organs and vessels (all P > 0.05), and lower noise (P = 0.002), than the standard-dose group. In qualitative analysis, the contrast-boosted group did not differ regarding vessel enhancement and lesion conspicuity (P > 0.05) but had lower noise (P < 0.05) and higher organ enhancement and artifacts (all P < 0.05) than the standard-dose group. While iodine uptake was significantly reduced in low-iodine-dose CT (P < 0.001), there was no difference in radiation dose between standard- and low-iodine-dose CT (all P > 0.05). Low-iodine-dose abdominal CT, combined with an AI-based contrast-boosting technique exhibited comparable organ and vessel enhancement, as well as lesion conspicuity compared to standard-iodine-dose CT in children. Moreover, image noise decreased in the contrast-boosted group, albeit with an increase in artifacts.

Improving intracranial aneurysms image quality and diagnostic confidence with deep learning reconstruction in craniocervical CT angiography.

The diagnostic impact of deep learning computed tomography (CT) reconstruction on intracranial aneurysm (IA) remains unclear. To quantify the image quality and diagnostic confidence on IA in craniocervical CT angiography (CTA) reconstructed with DEep Learning Trained Algorithm (DELTA) compared to the routine hybrid iterative reconstruction (HIR). A total of 60 patients who underwent craniocervical CTA and were diagnosed with IA were retrospectively enrolled. Images were reconstructed with DELTA and HIR, where the image quality was first compared in noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). Next, two radiologists independently graded the noise appearance, arterial sharpness, small vessel visibility, conspicuity of calcifications that may present in arteries, and overall image quality, each with a 5-point Likert scale. The diagnostic confidence on IAs of various sizes was also graded. Significantly lower noise and higher SNR and CNR were found on DELTA than on HIR images (all P < 0.05). All five subjective metrics were scored higher by both readers on the DELTA images (all P < 0.05), with good to excellent inter-observer agreement (κ = 0.77-0.93). DELTA images were rated with higher diagnostic confidence on IAs compared to HIR (P < 0.001), particularly for those with size ≤3 mm, which were scored 4.5 ± 0.6 versus 3.4 ± 0.8 and 4.4 ± 0.7 versus 3.5 ± 0.8 by two readers, respectively. The DELTA shows potential for improving the image quality and the associated confidence in diagnosing IA that may be worth consideration for routine craniocervical CTA applications.

Signal to noise ratio of masticatory muscle activity of functional and non-functional oral tasks.

Electromyographic activity (EMG) of masticatory muscles during wakefulness is understudied. It is unclear if single channel ambulatory EMG devices are sensitive enough to detect masticatory muscle activity (MMA) during wakefulness. To compare the MMA of various oral tasks recorded with a single channel EMG device ((Grindcare4-datalogger Prototype device) (GC4-β)) and a conventional EMG (cEMG) device. EMG activity of 30 standardised oral tasks was recorded unilaterally from the masseter and anterior temporalis muscle in 24 healthy volunteers using GC4-β and a cEMG device. To compare the EMG data, signal-to-noise ratios (SNR) were calculated as a way to normalise EMG activity across tasks. Analysis of variance was used to compare the SNR between the devices, muscles and oral tasks. SNR measured from GC4-β was overall significantly higher than the cEMG device (p =.001). The SNR for maximum voluntary contraction (MVC) was significantly higher than all other tasks (p <.001). SNR for temporalis with GC4-β was significantly higher for MVC, hard food, soft food, gum chewing (dominant side), rhythmic clenching and upper lip biting compared to the cEMG device (p <.021). The SNR for masseter with GC4-β was significantly higher for hard food and gum chewing (dominant side), rhythmic clenching, rhythmic biting of an object and yawning compared to the cEMG device (p <.022). This study provides novel insight into the EMG patterns of numerous oral tasks enhancing knowledge of physiological differences between the masticatory muscles. Further, single channel EMG devices can effectively measure the EMG activity of various oral tasks during wakefulness.

Feasibility of acousto-electric tomography

In acousto-electric tomography (AET) the goal is to reconstruct the electric conductivity in a domain from electrostatic boundary measurements of corresponding currents and voltages, while the domain is perturbed by a time-dependent acoustic wave, thus taking advantage of the acousto-electric effect. We approach the AET reconstruction in two steps: First, the interior power density is obtained from boundary measurements by solving a linear inverse and ill-posed problem; second, the interior conductivity is reconstructed from the power density by solving a non-linear and well-posed problem. Mathematically these inverse problems are fairly well understood, and reconstruction methods work well on synthetic data. This is in contrast to experimental findings. An effect can indeed be observed and data can be collected. However, the acousto-electric coupling is very weak, and consequently, the change in the measured voltage due to the acoustic perturbation might be too small compared to the background noise for viable reconstructions. In this paper, we take one step towards understanding the feasibility of AET. We provide an in-silico model of the coupled physics scenario based on standard models for the individual phenomena. Moreover, we formulate and implement numerically a full reconstruction method for the inverse problem via the two steps. We perform computational experiments with realistically chosen parameters from the context of medical imaging. The focus is on understanding the role of the acousto-electric coupling parameter and the signal-to-noise ratio (SNR). The critical signal strength is analyzed and the omnipresent Johnson–Nyquist noise is estimated. We obtain both positive and negative findings; we can reconstruct features even under severe noise conditions, but we also find that the SNR one is likely to face in practice is too low to obtain useful reconstructions.

Computed chest radiography for total body irradiation: image quality and clinical feasibility

Objective. In myeloablative total body irradiation (TBI), lung shielding blocks are used to reduce the dose to the lungs and hence decrease the risk of radiation pneumonitis. Some centers are still using mega-Volt (MV) imaging with dedicated silver halide-based films during simulation and treatment for lung delineation and position verification. However, the availability of these films has recently become an issue. This study examines the clinical performance of a computed radiography (CR) solution in comparison to radiographic films and potential improvement of image quality by filtering and post-processing. Approach. We compared BaFBrI-based CR plates to radiographic films. First, images of an aluminum block were analyzed to assess filter impact on scatter reduction. Secondly, a dedicated image quality phantom was used to assess signal linearity, signal-to-noise ratio (SNR), contrast and spatial resolution. Ultimately, a clinical performance study involving two impartial observers was conducted on an anthropomorphic chest phantom, employing visual grading analysis (VGA). Various filter materials and positions as well as post-processing were examined, and the workflow between CR and film was compared. Main results. CR images exhibited high SNR and linearity but demonstrated lower spatial and contrast resolution when compared to film. However, filtering improved contrast resolution and SNR, while positioning filters inside the cassette additionally enhanced sharpness. Image processing improved VGA scores, while additional filtering also resulted in higher spine visibility scores. CR shortened TBI simulation by over 10 minutes for one patient, alongside a dose reduction by order of 0.1 Gy. Significance. This study highlights potential advantages of shifting from conventional radiographic film to CR for TBI. Overall, CR with the incorporation of processing and filtering proves to be suitable for TBI chest imaging. When compared to radiographic film, CR offers advantages such as reduced simulation time and dose delivery, re-usability of image plates and digital workflow integration.

Feasibility and optimization of 19F MRI on a clinical 3T with a large field-of-view torso coil

Objective. The objective of this work is to: (1) demonstrate fluorine-19 (19F) MRI on a 3T clinical system with a large field of view (FOV) multi-channel torso coil (2) demonstrate an example parameter selection optimization for a 19F agent to maximize the signal-to-noise ratio (SNR)-efficiency for spoiled gradient echo (SPGR), balanced steady-state free precession (bSSFP), and phase-cycled bSSFP (bSSFP-C), and (3) validate detection feasibility in ex vivo tissues. Approach. Measurements were conducted on a 3.0T Discovery MR750w MRI (GE Healthcare, USA) with an 8-channel 1H/19F torso coil (MRI Tools, Germany). Numerical simulations were conducted for perfluoropolyether to determine the theoretical parameters to maximize SNR-efficiency for the sequences. Theoretical parameters were experimentally verified, and the sensitivity of the sequences was compared with a 10 min acquisition time with a 3.125 × 3.125 × 3 mm3 in-plane resolution. Feasibility of a bSSFP-C was also demonstrated in phantom and ex vivo tissues. Main Results. Flip angles (FAs) of 12 and 64° maximized the signal for SPGR and bSSFP, and validation of optimal FA and receiver bandwidth showed close agreement with numerical simulations. Sensitivities of 2.47, 5.81, and 4.44 ms−0.5mM−1 and empirical detection limits of 20.3, 1.5, and 6.2 mM were achieved for SPGR, bSSFP, and bSSFP-C, respectively. bSSFP and bSSFP-C achieved 1.8-fold greater sensitivity over SPGR (p < 0.01). Significance. bSSFP-C was able to improve sensitivity relative to simple SPGR and reduce both bSSFP banding effects and imaging time. The sequence was used to demonstrate the feasibility of 19F MRI at clinical FOVs and field strengths within ex-vivo tissues.

Ergodic Rate Analysis for Full-Duplex and Half-Duplex Networks with Energy Harvesting

Considering energy harvesting, the ergodic data rates for both in band full-duplex (FD) and half-duplex (HD) wireless communications were studied. The analytic expressions of downlink and uplink ergodic rates for the proposed system were first derived with independent and identically distributed (i.i.d.) Rayleigh fading link. It was revealed that the uplink data rate can be improved by decreasing the downlink data rate. Furthermore, the uplink/downlink data rates are also shown to be influenced by some significance parameters, for example, the power split parameter and signal-to-noise ratio (SNR) (i.e., PS/σ2) of each link. Additionally, unlike the HD, the proposed FD node is capable of harvesting energy during the communication process; however, this is at the cost of performance loss induced by the residual self-interference (RSI), which is caused by the essence of simultaneous uplink and downlink transmissions in a single frequency band.

Probing a neural unreliability account of auditory sensory processing atypicalities in Rett Syndrome

BackgroundIn the search for objective tools to quantify neural function in Rett Syndrome (RTT), which are crucial in the evaluation of therapeutic efficacy in clinical trials, recordings of sensory-perceptual functioning using event-related potential (ERP) approaches have emerged as potentially powerful tools. Considerable work points to highly anomalous auditory evoked potentials (AEPs) in RTT. However, an assumption of the typical signal-averaging method used to derive these measures is “stationarity” of the underlying responses – i.e. neural responses to each input are highly stereotyped. An alternate possibility is that responses to repeated stimuli are highly variable in RTT. If so, this will significantly impact the validity of assumptions about underlying neural dysfunction, and likely lead to overestimation of underlying neuropathology. To assess this possibility, analyses at the single-trial level assessing signal-to-noise ratios (SNR), inter-trial variability (ITV) and inter-trial phase coherence (ITPC) are necessary.MethodsAEPs were recorded to simple 100 Hz tones from 18 RTT and 27 age-matched controls (Ages: 6–22 years). We applied standard AEP averaging, as well as measures of neuronal reliability at the single-trial level (i.e. SNR, ITV, ITPC). To separate signal-carrying components from non-neural noise sources, we also applied a denoising source separation (DSS) algorithm and then repeated the reliability measures.ResultsSubstantially increased ITV, lower SNRs, and reduced ITPC were observed in auditory responses of RTT participants, supporting a “neural unreliability” account. Application of the DSS technique made it clear that non-neural noise sources contribute to overestimation of the extent of processing deficits in RTT. Post-DSS, ITV measures were substantially reduced, so much so that pre-DSS ITV differences between RTT and TD populations were no longer detected. In the case of SNR and ITPC, DSS substantially improved these estimates in the RTT population, but robust differences between RTT and TD were still fully evident.ConclusionsTo accurately represent the degree of neural dysfunction in RTT using the ERP technique, a consideration of response reliability at the single-trial level is highly advised. Non-neural sources of noise lead to overestimation of the degree of pathological processing in RTT, and denoising source separation techniques during signal processing substantially ameliorate this issue.