Ergodic speckle contrast optical coherence tomography velocimetry of rapid blood flow.

Visualizing a 3D blood flow velocity field through noninvasive imaging is crucial for analyzing hemodynamic mechanisms in areas prone to disorders. However, traditional correlation-based optical coherence tomography (OCT) velocimetry techniques have a maximum measurable flow velocity depending on the A-line rate. We presented the ergodic speckle contrast OCT (ESCOCT) to break the bottleneck in measuring the rapid blood flow velocity. It achieved a measurement of blood flow velocity ranging from 9.5 to 280 mm/s using a 100 kHz swept-source (SS) OCT based on 100 A-repeats scanning mode. Addressing the non-ergodic problem of temporal OCT signals by integrating more consecutive A-scans, ESCOCT can enable the estimation for lower velocity flows by increasing A-repeats. ESCOCT provided a wide dynamic range with no upper limit on measuring blood flow velocity with an adequate signal-to-noise ratio and improved the sensitivity and accuracy of the hemodynamic assessment.

The effect of elimination of gibbs ringing, noise and systematic errors on the DTI metrics and tractography in a rat brain.

Diffusion tensor imaging (DTI) metrics and tractography can be biased due to low signal-to-noise ratio (SNR) and systematic errors resulting from image artifacts and imperfections in magnetic field gradients. The imperfections include non-uniformity and nonlinearity, effects caused by eddy currents, and the influence of background and imaging gradients. We investigated the impact of systematic errors on DTI metrics of an isotropic phantom and DTI metrics and tractography of a rat brain measured at high resolution. We tested denoising and Gibbs ringing removal methods combined with the B matrix spatial distribution (BSD) method for magnetic field gradient calibration. The results showed that the performance of the BSD method depends on whether Gibbs ringing is removed and the effectiveness of stochastic error removal. Region of interest (ROI)-based analysis of the DTI metrics showed that, depending on the size of the ROI and its location in space, correction methods can remove systematic bias to varying degrees. The preprocessing pipeline proposed and dedicated to this type of data together with the BSD method resulted in an even - 90% decrease in fractional anisotropy (FA) (globally and locally) in the isotropic phantom and - 45% in the rat brain. The largest global changes in the rat brain tractogram compared to the standard method without preprocessing (sDTI) were noticed after denoising. The direction of the first eigenvector obtained from DTI after denoising, Gibbs ringing removal and BSD differed by an average of 56 and 10 degrees in the ROI from sDTI and from sDTI after denoising and Gibbs ringing removal, respectively. The latter can be identified with the amount of improvement in tractography due to the elimination of systematic errors related to imperfect magnetic field gradients. Based on the results, the systematic bias for high resolution data mainly depended on SNR, but the influence of non-uniform gradients could also be seen. After denoising, the BSD method was able to further correct both the metrics and tractography of the diffusion tensor in the rat brain by taking into account the actual distribution of magnetic field gradients independent of the examined object and uniquely dependent on the scanner and sequence. This means that in vivo studies are also subject to this type of errors, which should be taken into account when processing such data.

Comparison of image quality between Deep learning image reconstruction and Iterative reconstruction technique for CT Brain- a pilot study.

Background: Non-contrast Computed Tomography (NCCT) plays a pivotal role in assessing central nervous system disorders and is a crucial diagnostic method. Iterative reconstruction (IR) methods have enhanced image quality (IQ) but may result in a blotchy appearance and decreased resolution for subtle contrasts. The deep-learning image reconstruction (DLIR) algorithm, which integrates a convolutional neural network (CNN) into the reconstruction process, generates high-quality images with minimal noise. Hence, the objective of this study was to assess the IQ of the Precise Image (DLIR) and the IR technique (iDose 4) for the NCCT brain. Methods: This is a prospective study. Thirty patients who underwent NCCT brain were included. The images were reconstructed using DLIR-standard and iDose 4. Qualitative IQ analysis parameters, such as overall image quality (OQ), subjective image noise (SIN), and artifacts, were measured. Quantitative IQ analysis parameters such as Computed Tomography (CT) attenuation (HU), image noise (IN), posterior fossa index (PFI), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) in the basal ganglia (BG) and centrum-semiovale (CSO) were measured. Paired t-tests were performed for qualitative and quantitative IQ analyses between the iDose 4 and DLIR-standard. Kappa statistics were used to assess inter-observer agreement for qualitative analysis. Results: Quantitative IQ analysis showed significant differences (p<0.05) in IN, SNR, and CNR between the iDose 4 and DLIR-standard at the BG and CSO levels. IN was reduced (41.8-47.6%), SNR (65-82%), and CNR (68-78.8%) were increased with DLIR-standard. PFI was reduced (27.08%) the DLIR-standard. Qualitative IQ analysis showed significant differences (p<0.05) in OQ, SIN, and artifacts between the DLIR standard and iDose 4. The DLIR standard showed higher qualitative IQ scores than the iDose 4. Conclusion: DLIR standard yielded superior quantitative and qualitative IQ compared to the IR technique (iDose4). The DLIR-standard significantly reduced the IN and artifacts compared to iDose 4 in the NCCT brain.

Assessment of traumatic mandibular nerve using MR neurography sequence: a preliminary study.

BACKGROUND: Iatrogenic mandibular nerve damage resulting from oral surgeries and dental procedures is painful and a formidable challenge for patients and oral surgeons alike, mainly because the absence of objective and quantitative methods for diagnosing nerve damage renders treatment and compensation ambiguous while often leading to medico-legal disputes. The aim of this study was to examine discriminating factors of traumatic mandibular nerve within a specific magnetic resonance imaging (MRI) protocol and to suggest tangible diagnostic criteria for peripheral trigeminal nerve injury. METHODS: Twenty-six patients with ipsilateral mandibular nerve trauma underwent T2 Flex water, 3D short tau inversion recovery (STIR), and diffusion-weighted imaging (DWI) acquired by periodically rotating overlapping parallel lines with enhanced reconstruction (PROPELLER) pulse sequences; 26 injured nerves were thus compared with contra-lateral healthy nerves at anatomically corresponding sites. T2 Flex apparent signal to noise ratio (FSNR), T2 Flex apparent nerve-muscle contrast to noise ratio (FNMCNR) 3D STIR apparent signal to noise ratio (SSNR), 3D STIR apparent nerve-muscle contrast to noise ratio (SNMCNR), apparent diffusion coefficient (ADC) and area of cross-sectional nerve (Area) were evaluated. RESULTS: Mixed model analysis revealed FSNR and FNMCNR to be the dual discriminators for traumatized mandibular nerve (p < 0.05). Diagnostic performance of both parameters was also determined with area under the receiver operating characteristic curve (AUC for FSNR = 0.712; 95% confidence interval [CI]: 0.5660, 0.8571 / AUC for FNMCNR = 0.7056; 95% confidence interval [CI]: 1.011, 1.112). CONCLUSIONS: An increase in FSNR and FNMCNR within our MRI sequence seems to be accurate indicators of the presence of traumatic nerve. This prospective study may serve as a foundation for sophisticated model diagnosing trigeminal nerve trauma within large patient cohorts.

Application Value of a Novel Micro-Coil in High-Resolution Imaging of Experimental Mice Based on 3.0 T Clinical MR.

The clinical magnetic resonance scanner (field strength ≤ 3.0 T) has limited efficacy in the high-resolution imaging of experimental mice. This study introduces a novel magnetic resonance micro-coil designed to enhance the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), thereby improving high-resolution imaging in experimental mice using clinical magnetic resonance scanners. Initially, a phantom was utilized to determine the maximum spatial resolution achievable by the novel micro-coil. Subsequently, 12 C57BL/6JGpt mice were included in this study, and the novel micro-coil was employed for their scanning. A clinical flexible coil was selected for comparative analysis. The scanning methodologies for both coils were consistent. The imaging clarity, noise, and artifacts produced by the two coils on mouse tissues and organs were subjectively evaluated, while the SNR and CNR of the brain, spinal cord, and liver were objectively measured. Differences in the images produced by the two coils were compared. The results indicated that the maximum spatial resolution of the novel micro-coil was 0.2 mm. Furthermore, the subjective evaluation of the images obtained using the novel micro-coil was superior to that of the flexible coil (p < 0.05). The SNR and CNR measurements for the brain, spinal cord, and liver using the novel micro-coil were significantly higher than those obtained with the flexible coil (p < 0.001). Our study suggests that the novel micro-coil is highly effective in enhancing the image quality of clinical magnetic resonance scanners in experimental mice.

Computed Tomography Effective Dose and Image Quality in Deep Learning Image Reconstruction in Intensive Care Patients Compared to Iterative Algorithms.

Deep learning image reconstruction (DLIR) algorithms employ convolutional neural networks (CNNs) for CT image reconstruction to produce CT images with a very low noise level, even at a low radiation dose. The aim of this study was to assess whether the DLIR algorithm reduces the CT effective dose (ED) and improves CT image quality in comparison with filtered back projection (FBP) and iterative reconstruction (IR) algorithms in intensive care unit (ICU) patients. We identified all consecutive patients referred to the ICU of a single hospital who underwent at least two consecutive chest and/or abdominal contrast-enhanced CT scans within a time period of 30 days using DLIR and subsequently the FBP or IR algorithm (Advanced Modeled Iterative Reconstruction [ADMIRE] model-based algorithm or Adaptive Iterative Dose Reduction 3D [AIDR 3D] hybrid algorithm) for CT image reconstruction. The radiation ED, noise level, and signal-to-noise ratio (SNR) were compared between the different CT scanners. The non-parametric Wilcoxon test was used for statistical comparison. Statistical significance was set at p < 0.05. A total of 83 patients (mean age, 59 ± 15 years [standard deviation]; 56 men) were included. DLIR vs. FBP reduced the ED (18.45 ± 13.16 mSv vs. 22.06 ± 9.55 mSv, p < 0.05), while DLIR vs. FBP and vs. ADMIRE and AIDR 3D IR algorithms reduced image noise (8.45 ± 3.24 vs. 14.85 ± 2.73 vs. 14.77 ± 32.77 and 11.17 ± 32.77, p < 0.05) and increased the SNR (11.53 ± 9.28 vs. 3.99 ± 1.23 vs. 5.84 ± 2.74 and 3.58 ± 2.74, p < 0.05). CT scanners employing DLIR improved the SNR compared to CT scanners using FBP or IR algorithms in ICU patients despite maintaining a reduced ED.

A comprehensive assessment of a prototype high ratio antiscatter grid in interventional cardiology using experimental measurements and Monte Carlo simulations.

Objective. To assess the performance of a new antiscatter grid design in interventional cardiology for image quality improvement and dose reduction using experimental measurements and Monte Carlo (MC) simulation.Approach.Experimental measurements were performed on an angiography system, using a multi-layered tissue simulating composite phantom made from of poly(methyl methacrylate), aluminium and expanded polystyrene (2/0.2/0.7 cm). The total phantom thickness ranged from 20.3 cm to 40.6 cm. Four conditions were compared; (A) 105 cm source-image receptor distance (SID) without grid, (Bi) 105 cm SID with grid ratio (r) and strip density (N) (r15N80), (Bii) 120 cm SID without grid, and (Biii) 120 cm SID with high ratio grid (r29N80). The system efficiency (η), defined by the signal-to-noise ratio, was compared from theBconditions against caseA. These conditions were also simulated with MC techniques, allowing additional phantom compositions to be explored. Weighted image quality improvement factor (ηw(u)) was studied experimentally at a specific spatial frequency due to the SID change. Images were simulated with an anthropomorphic chest phantom for the different conditions, and the system efficiency was compared for the different anatomical regions.Main results.Good agreement was found between theηandηw(u) methods using both measured and simulated data, with average relative differences between 2%-11%. CaseBiiiprovided higherηvalues compared toA, andBifor thicknesses larger than 20.3 cm. In addition, caseBiiialso provided higherηvalues for high attenuating areas in the anthropomorphic phantom, such as behind the spine.Significance.The new antiscatter grid design provided higher system efficiency compared to the standard grid for the parameters explored in this work.

Peak detection in intracranial pressure signal waveforms: a comparative study.

BACKGROUND: The monitoring and analysis of quasi-periodic biological signals such as electrocardiography (ECG), intracranial pressure (ICP), and cerebral blood flow velocity (CBFV) waveforms plays an important role in the early detection of adverse patient events and contributes to improved care management in the intensive care unit (ICU). This work quantitatively evaluates existing computational frameworks for automatically extracting peaks within ICP waveforms. METHODS: Peak detection techniques based on state-of-the-art machine learning models were evaluated in terms of robustness to varying noise levels. The evaluation was performed on a dataset of ICP signals assembled from 700 h of monitoring from 64 neurosurgical patients. The groundtruth of the peak locations was established manually on a subset of 13, 611 pulses. Additional evaluation was performed using a simulated dataset of ICP with controlled temporal dynamics and noise. RESULTS: The quantitative analysis of peak detection algorithms applied to individual waveforms indicates that most techniques provide acceptable accuracy with a mean absolute error (MAE) ≤ 10 ms without noise. In the presence of a higher noise level, however, only kernel spectral regression and random forest remain below that error threshold while the performance of other techniques deteriorates. Our experiments also demonstrated that tracking methods such as Bayesian inference and long short-term memory (LSTM) can be applied continuously and provide additional robustness in situations where single pulse analysis methods fail, such as missing data. CONCLUSION: While machine learning-based peak detection methods require manually labeled data for training, these models outperform conventional signal processing ones based on handcrafted rules and should be considered for peak detection in modern frameworks. In particular, peak tracking methods that incorporate temporal information between successive periods of the signals have demonstrated in our experiments to provide more robustness to noise and temporary artifacts that commonly arise as part of the monitoring setup in the clinical setting.

Electrical rejuvenation of chronically implanted macroelectrodes in nonhuman primates.

Objective.Electrodes chronically implanted in the brain undergo complex changes over time that can lower the signal to noise ratio (SNR) of recorded signals and reduce the amount of energy delivered to the tissue during therapeutic stimulation, both of which are relevant for the development of robust, closed-loop control systems. Several factors have been identified that link changes in the electrode-tissue interface (ETI) to increased impedance and degraded performance in micro- and macro-electrodes. Previous studies have demonstrated that brief pulses applied every few days can restore SNR to near baseline levels during microelectrode recordings in rodents, a process referred to as electrical rejuvenation. However, electrical rejuvenation has not been tested in clinically relevant macroelectrode designs in large animal models, which could serve as preliminary data for translation of this technique. Here, several variations of this approach were tested to characterize parameters for optimization.Approach. Alternating-current (AC) and direct-current (DC) electrical rejuvenation methods were explored in three electrode types, chronically implanted in two adult male nonhuman primates (NHP) (Macaca mulatta), which included epidural electrocorticography (ECoG) electrodes and penetrating deep-brain stimulation (DBS) electrodes. Electrochemical impedance spectroscopy (EIS) was performed before and after each rejuvenation paradigm as a gold standard measure of impedance, as well as at subsequent intervals to longitudinally track the evolution of the ETI. Stochastic error modeling was performed to assess the standard deviation of the impedance data, and consistency with the Kramers-Kronig relations was assessed to evaluate the stationarity of EIS measurement.Main results. AC and DC rejuvenation were found to quickly reduce impedance and minimize the tissue component of the ETI on all three electrode types, with DC and low-frequency AC producing the largest impedance drops and reduction of the tissue component in Nyquist plots. The effects of a single rejuvenation session were found to last from several days to over 1 week, and all rejuvenation pulses induced no observable changes to the animals' behavior.Significance. These results demonstrate the effectiveness of electrical rejuvenation for diminishing the impact of chronic ETI changes in NHP with clinically relevant macroelectrode designs.

Large-scale deep tissue voltage imaging with targeted-illumination confocal microscopy.

Voltage imaging with cellular specificity has been made possible by advances in genetically encoded voltage indicators. However, the kilohertz rates required for voltage imaging lead to weak signals. Moreover, out-of-focus fluorescence and tissue scattering produce background that both undermines the signal-to-noise ratio and induces crosstalk between cells, making reliable in vivo imaging in densely labeled tissue highly challenging. We describe a microscope that combines the distinct advantages of targeted illumination and confocal gating while also maximizing signal detection efficiency. The resulting benefits in signal-to-noise ratio and crosstalk reduction are quantified experimentally and theoretically. Our microscope provides a versatile solution for enabling high-fidelity in vivo voltage imaging at large scales and penetration depths, which we demonstrate across a wide range of imaging conditions and different genetically encoded voltage indicator classes.

Influence of anatomical structure on antiscatter grid performance in 2D: application to x-ray angiography and a prototype 29:1 ratio grid.

Objective.The use of uniform phantoms to assess the influence of x-ray scatter and antiscatter grids on x-ray angiography and fluoroscopy image quality disregards the influence of spatially variable x-ray attenuation of patients. The purpose of this work was to measure scatter to primary ratio (SPR) and antiscatter grid SNR improvement factor (KSNR) using experimental conditions which better mimic patient imaging conditions.Approach.Three adult-sized anthropomorphic phantoms were used. AP and lateral projection images of the thorax and abdomen were acquired with and without an antiscatter grid. Grids with ratio 15:1 and 29:1 (r15, r29) and x-ray fields of view 20, 25 (thorax) and 32, 42 cm (abdomen) were tested. Combined with a-priori measurements of grid scatter and primary transmission fractions, these images were used to calculate 2D SPR andKSNRmaps.Main results.Results demonstrated that measurements by uniform phantom do not describe the complex 2D SPR andKSNRdistributions associated with anthropomorphic phantoms. The regions of the images with the lowest primary x-ray intensity (greatest attenuation) had the highest SPR and the highestKSNRattributable to the grids. Considering all conditions, the 95th percentile of the SPR maps was in the range 42%-185% greater than the median values and that of theKSNRmaps was 4%-20% higher than the median values. The combined influences of SID 120 vs. 107 cm and r29 vs. r15 grid resulted inKSNRin the range 1.05-1.49.Significance.Performance of anti-scatter grids using anatomically complex phantoms highlights the substantial variation of SPR andKSNRwithin 2D images. Also, this work demonstrates the benefit of the prototype r29 grid for thoracic and abdominal angiography imaging conditions is substantial, especially for large patients and radiodense image regions.

Multivariate signal-to-noise ratio as a metric for characterizing spectral computed tomography.

Objective.With the introduction of spectral CT techniques into the clinic, the imaging capacities of CT were expanded to multiple energy levels. Due to a variety of factors, the acquired signal in spectral CT datasets is shared between these images. Conventional image quality metrics assume independence between images which is not preserved within spectral CT datasets, limiting their utility for characterizing energy selective images. The purpose of this work was to develop a metrology to characterize energy selective images by incorporating the shared information between images within a spectral CT dataset.Approach.The signal-to-noise ratio (SNR) was extended into a multivariate space where each image within a spectral CT dataset was treated as a separate information channel. The general definition was applied to the specific case of contrast to define a multivariate contrast-to-noise ratio (CNR). The matrix contained two types of terms: a conventional CNR term which characterized image quality within each image in the spectral CT dataset and covariance weighted CNR (Covar-CNR) which characterized the contrast in each image relative to the covariance between images. Experimental data from an investigational photon-counting CT scanner was used to demonstrate the insight of this metrology. A cylindrical water phantom containing vials of iodine and gadolinium (2, 4, and 8 mg ml-1) was imaged under conditions of variable tube current, tube voltage, and energy threshold. Two image series (threshold and bin images) containing two images each were defined based upon the contribution of photons to reconstructed images. Analysis of variance (ANOVA) was calculated between CNR terms and image acquisition variables. A multivariate regression was then fitted to experimental data.Main Results.Image type had a major difference on how Covar-CNR values were distributed. Bin images had a slightly higher mean and wider standard deviation (Covar-CNRlo: 3.38 ±17.25, Covar-CNRhi: 5.77 ± 30.64) compared to threshold images (Covar-CNRlo: 2.08 ±1.89, Covar-CNRhi: 3.45 ± 2.49) across all conditions. ANOVA found that each acquisition variable had a significant relationship with both Covar-CNR terms. The multivariate regression model suggested that material concentration had the largest impact on all CNR terms.Signficance.In this work, we described a theoretical framework to extend the SNR to a multivariate form that is able to characterize images independently and also provide insight regarding the relationship between images. Experimental data was used to demonstrate the insight that this metrology provides about image formation factors in spectral CT.

Channel-Specific and Spatial Residual Attention Network for Medical Image Denoising.

Medical image quality is crucial for physicians to ensure accurate diagnosis and therapeutic strategies. However, due to the interference of noise, there are often various types of noise and artifacts in medical images. This not only damages the visual clarity of images, but also reduces the accuracy of information extraction. Considering that the edges of medical images are rich in high-frequency information, to enhance the quality of medical images, a dual attention mechanism, the channel-specific and spatial residual attention network (CSRAN) in the U-Net framework is proposed. The CSRAN seamlessly integrates the U-Net architecture with channel-wise and spatial feature attention (CSAR) modules, as well as low-frequency channel attention modules. Combined with the two modules, the ability of medical image processing to extract high-frequency features is improved, thereby significantly improving the edge effects and clarity of reconstructed images. This model can present better performance in capturing high-frequency information and spatial structures in medical image denoising and super-resolution reconstruction tasks. It cannot only enhance the ability to extract high-frequency features and strengthen its nonlinear representation capability, but also endow strong edge detection capabilities of the model. The experimental results further prove the superiority of CSRAN in medical image denoising and super-resolution reconstruction tasks.

Combining Dipole and Loop Coil Elements for 7 T Magnetic Resonance Studies of the Human Calf Muscle.

Combining proton and phosphorus magnetic resonance spectroscopy offers a unique opportunity to study the oxidative and glycolytic components of metabolism in working muscle. This paper presents a 7 T proton calf coil design that combines dipole and loop elements to achieve the high performance necessary for detecting metabolites with low abundance and restricted visibility, specifically lactate, while including the option of adding a phosphorus array. We investigated the transmit, receive, and parallel imaging performance of three transceiver dipoles with six pair-wise overlap-decoupled standard or twisted pair receive-only coils. With a higher SNR and more efficient transmission decoupling, standard loops outperformed twisted pair coils. The dipoles with standard loops provided a four-fold-higher image SNR than a multinuclear reference coil comprising two proton channels and 32% more than a commercially available 28-channel proton knee coil. The setup enabled up to three-fold acceleration in the right-left direction, with acceptable g-factors and no visible aliasing artefacts. Spectroscopic phantom measurements revealed a higher spectral SNR for lactate with the developed setup than with either reference coil and fewer restrictions in voxel placement due to improved transmit homogeneity. This paper presents a new use case for dipoles and highlights their advantages for the integration in multinuclear calf coils.

Comparative Effectiveness of Personal Sound Amplification Products Versus Hearing Aids for Unilateral Hearing Loss: A Prospective Randomized Crossover Trial.

BACKGROUND: This study compared hearing outcomes with use of personal sound amplification products (PSAPs) and hearing aids (HAs) in patients with moderate to moderately severe unilateral hearing loss. METHODS: Thirty-nine participants were prospectively enrolled, and randomly assigned to use either one HA (basic or premium type) or one PSAP (basic or high-end type) for the first 8 weeks and then the other device for the following 8 weeks. Participants underwent a battery of examinations at three visits, including sound-field audiometry, word recognition score (WRS), speech perception in quiet and in noise, real-ear measurement, and self-report questionnaires. RESULTS: Functional gain was significantly higher with HAs across all frequencies (P < 0.001). While both PSAPs and HAs improved WRS from the unaided condition, HAs were superior to PSAPs. The speech recognition threshold in quiet conditions and signal-to-noise ratio in noisy conditions were significantly lower in the HA-aided condition than in the PSAP-aided condition, and in the PSAP-aided condition than in the unaided condition. Subjective satisfaction also favored HAs than PSAPs in questionnaires, Abbreviated Profile of Hearing Aid Benefit, International Outcome Inventory for Hearing Aids, and Host Institutional Questionnaire. CONCLUSION: While PSAPs provide some benefit for moderate to moderately severe unilateral hearing loss, HAs are more effective. This underscores the potential role of PSAPs as an accessible, affordable first-line intervention in hearing rehabilitation, particularly for individuals facing challenges in accessing conventional HAs.

Retinal prosthesis edge detection (RPED) algorithm: Low-power and improved visual acuity strategy for artificial retinal implants.

This paper proposes a retinal prosthesis edge detection (RPED) algorithm that can achieve high visual acuity and low power. Retinal prostheses have been used to stimulate retinal tissue by injecting charge via an electrode array, thereby artificially restoring the vision of visually impaired patients. The retinal prosthetic chip, which generates biphasic current pulses, should be located in the foveal area measuring 5 mm × 5 mm. When a high-density stimulation pixel array is realized in a limited area, the distance between the stimulation pixels narrows, resulting in current dispersion and high-power dissipation related to heat generation. Various edge detection methods have been proposed over the past decade to reduce these deleterious effects and achieve high-resolution pixels. However, conventional methods have the disadvantages of high-power consumption and long data processing times because many pixels are activated to detect edges. In this study, we propose a novel RPED algorithm that has a higher visual acuity and less power consumption despite using fewer active pixels than existing techniques. To verify the performance of the devised RPED algorithm, the peak signal-to-noise ratio and structural similarity index map, which evaluates the quantitative numerical value of the image are employed and compared with the Sobel, Canny, and past edge detection algorithms in MATLAB. Finally, we demonstrate the effectiveness of the proposed RPED algorithm using a 1600-pixel retinal stimulation chip fabricated using a 0.35 µm complementary metal-oxide-semiconductor process.

Effect of Digital Noise Reduction in Hearing Aids on Speech Intelligibility in Both Quiet and Noisy Environments.

AIMS: Digital noise reduction (DNR) minimizes the effect of noise on speech signals by continuously monitoring frequency bands in the presence of noise. In the present study, we explored the effect of DNR technology on speech intelligibility in individuals using hearing aids (HAs) and investigated implications for daily use. METHODS AND MATERIAL: Eighteen participants with bilateral moderate sensorineural hearing loss (aged 16-45 years) were included. Bilateral receiver-in-the-ear HAs were fitted in the participants. The adaptive and nonadaptive (with a signal-to-noise ratio (SNR) of +5 and -5 dB, respectively) Turkish matrix sentence test (TURMatrix) in noise and free-field hearing assessments, including hearing thresholds with hearing aids, speech recognition thresholds (SRT), and speech discrimination scores, were conducted in two different conditions: HA in the DNR-on and DNR-off conditions. RESULTS: No significant difference was observed between free-field hearing assessments with the HA in the DNR-off and DNR-on conditions (P > 0.05). Furthermore, the adaptive and nonadaptive TURMatrix revealed significant differences between the scores under the DNR-on and DNR-off conditions (P < 0.05). Nevertheless, under the DNR-on condition, there was no correlation between free-field hearing assessments with HA and TURMatrix results (P > 0.05). However, a significant correlation was observed between SRT scores with HA and TURMatrix scores (adaptive and nonadaptive, +5 and -5 dB SNR, respectively) under the DNR-off condition (P < 0.05). CONCLUSION: Our study findings suggest that DNR can improve speech intelligibility in noisy environments. Therefore, DNR can enhance an individual's auditory comfort by improving their capacity to grasp speech in background noise.

Signal automatic modulation based on AMC neural network fusion.

With the rapid development of modern communication technology, it has become a core problem in the field of communication to find new ways to effectively modulate signals and to classify and recognize the results of automatic modulation. To further improve the communication quality and system processing efficiency, this study combines two different neural network algorithms to optimize the traditional signal automatic modulation classification method. In this paper, the basic technology involved in the communication process, including automatic signal modulation technology and signal classification technology, is discussed. Then, combining parallel convolution and simple cyclic unit network, three different connection paths of automatic signal modulation classification model are constructed. The performance test results show that the classification model can achieve a stable training and verification state when the two networks are connected. After 20 and 29 iterations, the loss values are 0.13 and 0.18, respectively. In addition, when the signal-to-noise ratio (SNR) is 25dB, the classification accuracy of parallel convolutional neural network and simple cyclic unit network model is as high as 0.99. Finally, the classification models of parallel convolutional neural networks and simple cyclic unit networks have stable correct classification probabilities when Doppler shift conditions are introduced as interference in practical application environment. In summary, the neural network fusion classification model designed can significantly improve the shortcomings of traditional automatic modulation classification methods, and further improve the classification accuracy of modulated signals.

The word-with-noise test: development, validation and reference values. / Teste de palavras no ruído: desenvolvimento, validação e valores de referência.

PURPOSE: To propose an instrument for assessing speech recognition in the presence of competing noise. To define its application strategy for use in clinical practice. To obtain evidence of criterion validity and present reference values. METHODS: The study was conducted in three stages: Organization of the material comprising the Word-with-Noise Test (Stage 1); Definition of the instrument's application strategy (Stage 2); Investigation of criterion validity and definition of reference values for the test (Stage 3) through the evaluation of 50 normal-hearing adult subjects and 12 subjects with hearing loss. RESULTS: The Word-with-Noise Test consists of lists of monosyllabic and disyllabic words and speech spectrum noise (Stage 1). The application strategy for the test was defined as the determination of the Speech Recognition Threshold with a fixed noise level at 55 dBHL (Stage 2). Regarding criterion validity, the instrument demonstrated adequate ability to distinguish between normal-hearing subjects and subjects with hearing loss (Stage 3). Reference values for the test were established as cut-off points expressed in terms of signal-to-noise ratio: 1.47 dB for the monosyllabic stimulus and -2.02 dB for the disyllabic stimulus. Conclusion: The Word-with-Noise Test proved to be quick to administer and interpret, making it a useful tool in audiological clinical practice. Furthermore, it showed satisfactory evidence of criterion validity, with established reference values.

OBJETIVO: Propor um instrumento para a avaliação do reconhecimento de fala na presença de ruído competitivo. Definir sua estratégia de aplicação, para ser aplicado na rotina clínica. Obter evidências de validade de critério e apresentar seus valores de referência. MÉTODO: Estudo realizado em três etapas: Organização do material que compôs o Teste de Palavras no Ruído (Etapa 1); Definição da estratégia de aplicação do instrumento (Etapa 2); Investigação da validade de critério e definição dos valores de referência para o teste (Etapa 3), por meio da avaliação de 50 sujeitos adultos normo-ouvintes e 12 sujeitos com perda auditiva. RESULTADOS: O Teste de Palavras no Ruído é composto por listas de vocábulos mono e dissilábicos e um ruído com espectro de fala (Etapa 1). Foi definida como estratégia de aplicação do teste, a realização do Limiar de Reconhecimento de Fala com ruído fixo em 55 dBNA (Etapa 2). Quanto à validade de critério, o instrumento apresentou adequada capacidade de distinção entre os sujeitos normo-ouvintes e os sujeitos com perda auditiva (Etapa 3). Foram definidos como valores de referência para o teste, os pontos de corte expressos em relação sinal/ruído de 1,47 dB para o estímulo monossilábico e de -2,02 dB para o dissilábico. CONCLUSÃO: O Teste de Palavras no Ruído demonstrou ser rápido e de fácil aplicação e interpretação dos resultados, podendo ser uma ferramenta útil a ser utilizada na rotina clínica audiológica. Além disso, apresentou evidências satisfatórias de validade de critério, com valores de referência estabelecidos.

Pillar data-acquisition strategies for cryo-electron tomography of beam-sensitive biological samples.

For cryo-electron tomography (cryo-ET) of beam-sensitive biological specimens, a planar sample geometry is typically used. As the sample is tilted, the effective thickness of the sample along the direction of the electron beam increases and the signal-to-noise ratio concomitantly decreases, limiting the transfer of information at high tilt angles. In addition, the tilt range where data can be collected is limited by a combination of various sample-environment constraints, including the limited space in the objective lens pole piece and the possible use of fixed conductive braids to cool the specimen. Consequently, most tilt series are limited to a maximum of ±70°, leading to the presence of a missing wedge in Fourier space. The acquisition of cryo-ET data without a missing wedge, for example using a cylindrical sample geometry, is hence attractive for volumetric analysis of low-symmetry structures such as organelles or vesicles, lysis events, pore formation or filaments for which the missing information cannot be compensated by averaging techniques. Irrespective of the geometry, electron-beam damage to the specimen is an issue and the first images acquired will transfer more high-resolution information than those acquired last. There is also an inherent trade-off between higher sampling in Fourier space and avoiding beam damage to the sample. Finally, the necessity of using a sufficient electron fluence to align the tilt images means that this fluence needs to be fractionated across a small number of images; therefore, the order of data acquisition is also a factor to consider. Here, an n-helix tilt scheme is described and simulated which uses overlapping and interleaved tilt series to maximize the use of a pillar geometry, allowing the entire pillar volume to be reconstructed as a single unit. Three related tilt schemes are also evaluated that extend the continuous and classic dose-symmetric tilt schemes for cryo-ET to pillar samples to enable the collection of isotropic information across all spatial frequencies. A fourfold dose-symmetric scheme is proposed which provides a practical compromise between uniform information transfer and complexity of data acquisition.