High SNR Research Articles

Enhancement of pH imaging of light addressable potentiometric sensor by colloidal spherical lens array

Visualizing the distribution of pH in a solution is an important basis for monitoring reaction processes and metabolic changes in micro-biochemical species. Light addressable potentiometric sensor (LAPS), as a simple structure, flexible imaging, low-cost and label-free electrochemical sensor, has been widely used to pH imaging. However, its spatiotemporal resolution is greatly affected by the frequency of modulation light, thus limiting its imaging performance. In this study, LAPS device modified by well-ordered and low-cost polystyrene (PS) colloidal monolayer as spherical lens array is proposed to obtain higher available modulation frequency of the maximum photoresponse. The morphology, optical properties and the electric field distribution of PS colloidal spherical lens array were tested and analyzed. pH sensing, imaging of uniform pH solution, the spatiotemporal pH imaging and dynamic imaging of enzymatic reactions of LAPS modified by PS colloidal spherical lens array were tested and evaluated. The experimental results show that LAPS modified by PS colloidal spherical lens array not only has acceptable pH sensing, but also widens the modulation frequency of the maximum photoresponse to 40 kHz. LAPS modified by colloidal spherical lens array modulated at the frequency of 40 kHz shows acceptable sensitivity and linearity, good reproducibility and stability, high SNR and high-spatiotemporal pH imaging capability. This work can be suitable for high-spatiotemporal monitoring of the changes in electrolyte environmental pH caused by the reaction and metabolism of the biochemical species.

Statistical spatial information based power optimization algorithm for multi-user massive MIMO systems

Power optimization has a significant role in multi-user massive multiple-input and multiple-output (mMIMO) wireless communication systems, whereas the acquisition of instantaneous channel state information (I-CSI) is a serious challenge in practical scenarios. The statistical spatial information (SPI), which contains angle-of-departures (AoDs) and corresponding large-scale fading factors, is slowly varying. The power optimization based on SPI to improve system performance needs to be explored for multi-user mMIMO systems. In this paper, two novel algorithms for SPI-based power optimization are proposed to maximize the ergodic sum-rate (ESR) under total power constraint for the system. Specifically, the minorization-maximization-based power optimization (MM-PO) algorithm and the closed-form power optimization (CF-PO) algorithm are proposed utilizing the minorization-maximization (MM) method and the fractional programming (FP) method, respectively. Moreover, the ESR is expressed based on the extracted SPI. The approximate expression of the ESR is derived. The complexity of the algorithms depends on the number of users, and the algorithms are scalable in the system. The proposed algorithms can effectively achieve the advantages of SPI while being robust to the uncertainty of I-CSI. Simulation results verify the ESR of our two proposed algorithms is 1.47 and 1.4 times higher than that of the equal power allocation algorithm at a high SNR when the number of antenna and user are equal to 64 and 20, respectively.

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.

Investigation in image quality and immediate patient safety using pre-dual-flow injection for low-contrast dose spectral pulmonary artery CT angiography

PurposeThe patient safety of iodine contrast-enhanced pulmonary artery CT angiography (CTPA) is widely concerned. This study aimed to investigate the image quality and immediate patient safety of spectral CTPA using a lower-contrast dose pre-dual-flow injection method. MethodsThis retrospective study included 120 patients with suspected pulmonary embolisms who received spectral CTPA between February and December 2022. Patients were divided into normal contrast injection (Group A, n=60) and pre-dual-flow group (Group B, n=60). CT values of pulmonary arteries (PAs) at different levels, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), arteriovenous separation performance, and beam hardening artifact (BHA) index of two sets of images were measured or calculated. The subjective image quality and immediate patient safety were also scored using the three-point method. ResultsGroup B had a contrast dose reduction by 42.5 % (60 vs. 34.5 mL). Radiation exposure dose was not statistically different between the two groups (P>0.05). CT values of different-level PAs on group B images were higher than those on group A images (P<0.05). Group B images had higher SNR and CNR, better arteriovenous separation between PA trunk and pulmonary vein, and lower BHA index on soft tissue and PA (all P<0.05). For subjective evaluation of image quality, group B had a better score in beam hardening artifact (P<0.05). For immediate patient safety, the score in comfortability was statistically higher in group B, with P<0.05. ConclusionsComparing with the normal injection method, pre-dual-flow spectral CTPA with a lower contrast dose injected results in better image quality and shows potential in patient-safety promotion.

Ultra-high resolution coronary CT angiography on photon-counting detector CT: bi-centre study on the impact of quantum iterative reconstruction on image quality and accuracy of stenosis measurements

PurposeTo assess the impact of different quantum iterative reconstruction (QIR) levels on objective and subjective image quality of ultra-high resolution (UHR) coronary CT angiography (CCTA) images and to determine the effect of strength levels on stenosis quantification using photon-counting detector (PCD)-CT. MethodA dynamic vessel phantom containing two calcified lesions (25 % and 50 % stenosis) was scanned at heart rates of 60, 80 and 100 beats per minute with a PCD-CT system. In vivo CCTA examinations were performed in 102 patients. All scans were acquired in UHR mode (slice thickness0.2 mm) and reconstructed with four different QIR levels (1–4) using a sharp vascular kernel (Bv64). Image noise, signal-to-noise ratio (SNR), sharpness, and percent diameter stenosis (PDS) were quantified in the phantom, while noise, SNR, contrast-to-noise ratio (CNR), sharpness, and subjective quality metrics (noise, sharpness, overall image quality) were assessed in patient scans. ResultsIncreasing QIR levels resulted in significantly lower objective image noise (in vitro and in vivo: both p < 0.001), higher SNR (both p < 0.001) and CNR (both p < 0.001). Sharpness and PDS values did not differ significantly among QIRs (all pairwise p > 0.008). Subjective noise of in vivo images significantly decreased with increasing QIR levels, resulting in significantly higher image quality scores at increasing QIR levels (all pairwise p < 0.001). Qualitative sharpness, on the other hand, did not differ across different levels of QIR (p = 0.15). ConclusionsThe QIR algorithm may enhance the image quality of CCTA datasets without compromising image sharpness or accurate stenosis measurements, with the most prominent benefits at the highest strength level.

Prompt gamma timing for proton range verification with TlBr and TlCl as pure Cherenkov emitters

Objective. Prompt gamma timing (PGT) uses the detection time of prompt gammas emitted along the range of protons in proton radiotherapy to verify the position of the Bragg peak (BP). Cherenkov detectors offer the possibility of enhanced signal-to-noise ratio (SNR) due to the inherent physics of Cherenkov emission which enhances detection of high energy prompt gamma rays relative to other induced uncorrelated signals. In this work, the PGT technique was applied to 3 semiconductor material slabs that emit only Cherenkov light for use in a full scale system: a 3 × 3 × 20 mm3 TlBr, a 12 × 12 × 12 mm3 TlBr, and a 5 × 5 × 5 mm3 TlCl. Approach. A polymethyl methacrylate (PMMA) target was exposed to a 67.5 MeV, 0.5 nA proton beam and shifted in 3 mm increments at the Crocker nuclear laboratory (CNL) in Davis, CA, USA. A fast plastic scintillator coupled to a photomultiplier tube (PMT) provided the start reference for the proton time of flight. Time of flight (TOF) distributions were generated using this reference and the gamma-ray timestamp in the Cherenkov detector. Main results. The SNR of the proton correlated peaks relative to the background was 20, 29, and 30 for each of the three samples, respectively. The upper limit of the position resolutions with the TlCl sample were 2 mm, 3 mm, and 5 mm for 30k, 10k, and 5k detected events, respectively. The time distribution of events with respect to the reference reproduced with clarity the periodicity of the beam, implying a very high SNR of the Cherenkov crystals to detect prompt gammas. Background presence from the neutron-induced continuum, prompt gammas from deuterium, or positron activation were not observed. Material choice and crystal dimensions did not seem to affect significantly the outcome of the results. Significance. These results show the high SNR of the pure Cherenkov emitters TlBr and TlCl for the detection of prompt gammas in a proton beam with current of clinical significance and their potential for verifying the proton range. The accuracy in determining shifts of the BP was highly dependent on the number of events acquired, therefore, the performance of these detectors are expected to vary with different beam conditions such as current, pulse repetition, and proton bunch width.

Accurate Blood Pressure Measurement Using Smartphone's Built-in Accelerometer

Efficient blood pressure (BP) monitoring in everyday contexts stands as a substantial public health challenge that has garnered considerable attention from both industry and academia. Commercial mobile phones have emerged as a promising tool for BP measurement, benefitting from their widespread popularity, portability, and ease of use. Most mobile phone-based systems leverage a combination of the built-in camera and LED to capture photoplethysmography (PPG) signals, which can be used to infer BP by analyzing the blood flow characteristics. However, due to low Signal-to-Noise (SNR), various factors such as finger motion, improper finger placement, skin tattoos, or fluctuations in environmental lighting can distort the PPG signal. These distortions consequentially affect the performance of BP estimation. In this paper, we introduce a novel sensing system that utilizes the built-in accelerometer of a mobile phone to capture seismocardiography (SCG) signals, enabling accurate BP measurement. Our system surpasses previous mobile phone-based BP measurement systems, offering advantages such as high SNR, ease of use, and power efficiency. We propose a triple-stage noise reduction scheme, integrating improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), recursive least squares (RLS) adaptive filter, and soft-thresholding, to effectively reconstruct high-quality heartbeat waveforms from initially contaminated raw SCG signals. Moreover, we introduce a data augmentation technique encompassing normalization coupled with temporal-sliding, effectively augmenting the diversity of the training sample set. To enable battery efficiency on smartphone, we propose a resource-efficient deep learning model that incorporates resource-efficient convolution, shortcut connections, and Huber loss. We conduct extensive experiments with 70 volunteers, comprising 35 healthy individuals and 35 individuals diagnosed with hypertension, under a user-independent setting. The excellent performance of our system demonstrates its capacity for robust and accurate daily BP measurement.

Hybrid selection combining scheme for correlated cooperative communication over [formula omitted]-[formula omitted] fading channels

Due to poor (limited) scattering, a correlation among channels is inevitable, resulting in performance degradation of wireless communication systems. In this paper, we propose a hybrid selection combining (SC), also known as switched scaled – SC for correlated cooperative communication over κ-μ fading channel. By using paired error analysis, we derive the end-to-end symbol error probability of this system over bivariate κ-μ channel fading with M-ary phase shift keying (MPSK) modulation. In addition, we obtain significant parameters of hybrid SS-SC, such as the optimum scaling factor and optimum threshold by using the second derivative test. For every signal to noise ratio (SNR), we obtain different optimum thresholds and scaling factors. Our analytical results were found to overlap significantly with those of the simulation results. They were also compared with the variants of SC and switched diversity techniques. Our study demonstrates that the proposed hybrid technique performs better than the other SC and switched diversity schemes over correlated and uncorrelated fading channels. Furthermore, we derive an asymptotic symbol error probability for the proposed system, and the asymptotic results are in agreement with the analytical results at higher SNRs. Our study also shows that the diversity order of the system depends on the mutually exclusive events of the proposed technique.

Weak signal detection method based on nonlinear differential equations

With the rapid development of computer network technology, it is often necessary to collect weak signals to collect favorable information. The development of signal detection technology is ongoing; however, various issues arise during the detection process. These issues include low efficiency and a high signal noise threshold. However, many problems will be encountered in the process of detection. In order to solve these problems, the nonlinear chaos theory is introduced to detect signals, and the simulation experiments of weak pulse signals and weak partial discharge signals are carried out respectively. The experimental results showed that the detection effect was remarkable in the quasi periodic state, and it had a good detection effect for weak pulse signals. At a signal-to-noise ratio of -25 dB, double coupling system, two-way ring coupling system, and single ring coupling system displayed detection success rates exceeding 98%. Meanwhile, the detection success rate of the strong coupling system was only 12%. Even at a noise signal ratio as low as -40 dB, the dual coupling system still maintained a detection success rate above 80%. The simulation results of partial discharge signal detection showed that there was a high fluctuation only at 2 ms, and the rest was basically stable at about 0 V, indicating that the system had a strong suppression effect on Gaussian white noise. When comparing the simulation results of the detection of the new chaotic system and the double coupling system, it was found that the new chaotic system has a superior impact in detecting weakly attenuated partial discharge signals. Through analysis of the system’s dynamic behavior, the research confirms its rich dynamic characteristics and sheds light on the reasons for phase state mutation and missed detection. The noise system is utilized for comparing the performance of various systems, with the goal of enhancing the system’s detection capability.

Measuring source properties and quasinormal mode frequencies of heavy massive black-hole binaries with LISA

The Laser Interferometer Space Antenna (LISA) will be launched in the mid-2030s. It promises to observe the coalescence of massive black-hole (BH) binaries with signal-to-noise ratios (SNRs) reaching thousands. Crucially, it will detect some of these binaries with high SNR in both the inspiral and the merger-ringdown stages. Such signals are ideal for tests of general relativity (GR) using information from the whole waveform. Here, we consider astrophysically motivated binary systems at the high-mass end of the population observable by LISA and simulate their signals using the newly developed multipolar effective-one-body model: pSEOBNRv5HM. The merger-ringdown signal in this model depends on the binary properties (masses and spins) and also on parameters that describe fractional deviations from the GR quasinormal mode (complex) frequencies of the remnant BH. Performing full Bayesian analyses, we assess to which accuracy LISA will be able to constrain deviations from GR in the ringdown signal when using information from the whole signal. We find that these deviations can typically be constrained to within 10% and in the best cases to within 1%. We also show that with this model we can measure the binary masses and spins with great accuracy even for very massive BH systems with low SNR in the inspiral. In particular, individual source-frame masses can typically be constrained to within 10% and as precisely as 1%, and individual spins can typically be constrained to within 0.1 and, in the best cases, to within 0.001. We also probe the accuracy of the SEOBNRv5HM waveform family by performing synthetic injections of GR numerical-relativity waveforms. Using a novel method that we develop here to quantify the impact of systematic errors, we show that, already for sources with SNR O(100), we would measure erroneous deviations from GR due to waveform model inaccuracies. One of the main sources of error is the mismodeling of the relative alignment between harmonics. These results confirm the need for improving waveform models to perform tests of GR with binary BHs observed at high SNR by LISA. Published by the American Physical Society 2024

Detection of microplastics based on splicing grating spatial heterodyne Raman spectroscopy

As a new type of persistent pollutant, microplastics pose a serious threat to the earth’s ecological environment and human health. Efficient and reliable microplastic detection technology is of great significance in the management of microplastic pollution. Aiming at the problems of low signal-to-noise ratio (SNR), narrow spectral range and low spectral resolution in traditional microplastic detection technology, a splicing grating spatial heterodyne Raman spectroscopy (SG-SHRS) is proposed in this paper. The splicing grating is composed of four sub-gratings with groove densities of 320, 298, 276 and 254 gr / mm, respectively. Each sub-grating has an independent sub-filter to improve the SNR of the system. The system is simulated, built and calibrated. The actual resolution of the SG-SHRS system is 0.7 cm−1, and the spectral detection range of a single sub-grating is 2947.2 cm−1. Four kinds of microplastics, polyamide (PA), polystyrene (PS), polycarbonate (PC), and polyphenylene sulfide (PPS), were detected by the SG-SHRS system. The complete Raman spectral information of microplastics was obtained, and the peak assignment of Raman characteristic peaks of the four kinds of microplastics was analyzed. By comparing the test results with a commercial dispersion spectrometer, it has been proven that the SG-SHRS system has the advantages of high spectral resolution, wide spectral range, and high SNR, and has good application prospects in the field of microplastic detection.

Design Method of Surface Receive Coil with High SNR for Various Field Intensities in MRI

Design Method of Surface Receive Coil with High SNR for Various Field Intensities in MRI

Long-term assessment and analysis of the radiometric quality of standard data products for Chinese Gaofen-1/2/6/7 optical remote sensing satellites

The first Chinese Gaofen (GF) remote sensing satellite was launched in August 2013 and has been in orbit for more than 10 years, providing a rich variety of image product data for remote sensing applications in various industries, with other remote sensing satellites of the GF series. To ensure the reliability of the information generated via remote sensing applications, all remote sensing images must undergo systematic quality evaluation. The quality of GF satellites is evaluated almost comprehensively during in-orbit testing at the early stage after launch; however, the remaining evaluations of product quality including geometric or radiometric calibrations are performed only annually. Thus, long-term systematic image quality evaluations are lacking, and the quality level and long-term change trends of the image products are unknown. These deficits have introduced uncertainty in the application of these products. This study aimed to establish a radiometric quality evaluation index framework for GF satellites to comprehensively measure the radiometric quality of GF image products. Multiple types of sample data, such as uniform field, knife-edge target, and radiometric calibration field data with different reflectance features, were acquired globally. Four core indexes, namely, the relative radiometric correction accuracy (RRCA), absolute radiometric correction accuracy (ARCA), modulation transfer function (MTF), and signal-to-noise ratio (SNR), were used to analyze and evaluate the long-term radiometric quality of the images from four GF optical remote sensing satellites: GF1, GF2, GF6, and GF7. The four GF satellites had high SNR and RRCA but low dynamic MTF and variable ARCA for different reflectance features. For example, the average SNRs for the four satellites reached 42.66, 43.91, 44.33, and 47.46 dB; the average RRCA (@root-mean-square errors) values reached 1.6%, 1.0%, 1.6%, and 0.1%; the average MTF values (at Nyquist frequency) of the panchromatic bands were only 0.15, 0.12, 0.07 and 0.04; and the difference in ARCA for different reflectance targets was approximately 10%. In the long time series, the SNRs for each sensor of the four GF satellites were high and stable for high-reflectance features, whereas the RCA and SNR values for low-reflectance features and the MTF of the sensors fluctuated in a cyclic manner. Meanwhile, large dynamic fluctuations and episodic image quality problems occurred at certain local time points, which must be considered during practical applications. The findings presented here will lay a foundation for the in-depth application of GF images.

Seismic deconvolution based on SNR-weighted stacking

We propose a seismic deconvolution algorithm based on signal-to-noise ratio (SNR)-weighted stacking. First, we applied the time-frequency analysis to decompose the common midpoint (CMP) or common reflection point (CRP) gather. It is considered that there are differences in noise between different traces in the frequency domain profile. Then, we employ an SNR-weighted stacking strategy in each frequency-domain profile. Finally, we construct the resolution-enhancement amplitude spectrum, apply a linear inversion approach to calculate the overall weights of each frequency-domain profile, perform weighted stacking between the frequency-domain profiles, and obtain the resolution-enhancement post-stack data. The frequency decomposition of the pre-stack gather is efficiently utilized for the primary data of the proposed method. The SNR-weighted stacking adaptively selects the components with high SNR at each frequency. In addition, the resolution-oriented stacking weights are determined by the linear inversion approach. This algorithm can be adaptively weighted by considering the SNR of each frequency component, which can effectively overcome the problem of low SNR in spectral simulation and obtain high-resolution seismic data. The algorithm does not include wavelet extraction and regularization; thus, the resolution-enhancement data is more reliable. Applications to synthetic and field seismic datasets demonstrate that the proposed resolution-enhancement methodology is of great benefit to the succeeding structural interpretation and thin-bed identification.

Organic-inorganic hybrids of BiI3-polymers with embedded conductive carbon fillers displaying high SNR for direct X-ray imaging detectors

Organic polymers are light weight, low toxic and environmentally stable candidates of interest in X-ray detection application. But low attenuation and high recombination rate of charge carriers cause low sensitivity is major limitation of these detectors. In this work, organic polymers (PS/PMMA) composites were prepared with high-Z inorganic BiI3 by simple dry mixing technique. The organic-inorganic hybrid further embedded with conductive carbon filler (MWCNT, CB, GP) to improve the charge collection of the samples. Resistivity and mobility of the samples were studied with help of Hall measurements and mobility-lifetime was calculated by Hecth equation and found (2.1 ± 0.2) × 10−3 cm2V−1. The elemental analysis of the samples had been done by XPS analysis. The X-ray investigations done by using dental X-ray source and the sensitivity of the samples increases from 3.4 to 200 μGy−1 cm−3 by incorporating CB filler with a low leakage current of order of 33.3 pA/cm2.

Reconfigurable multi-channel microwave photonic receiver for a multi-band signal based on cascaded microring resonator banks

What we believe to be a novel reconfigurable multi-channel microwave photonic (MWP) receiver for multi-band RF signal is demonstrated for the first time, to the best of our knowledge. A reconfigurable MWP signal processing chip based on two cascaded microring filter banks is employed in the proposed receiver, which slices the multi-band RF input into several narrow band signals and selects optical frequency comb lines for frequency converting of each channel. Due to the significant reconfigurability of the signal processing chip, the proposed receiver can flexibly choose the output frequency band of each channel, and thus different frequency components of the multi-band RF input can be down converted to the intermediate frequency (IF) band for receiving or converted to other frequency band for forwarding. A multi-band RF signal composed of a linear frequency modulation (LFM) signal with 2 GHz bandwidth and a quad-phase shift keyed (QPSK) signal with 100 Mbit/s rate is experimentally received and reconstructed by the proposed receiver, where the reconstructed LFM component exhibits a signal to noise ratio (SNR) of 10.2 dB, and the reconstructed QPSK component reaches a high SNR of 26.1 dB and a great error vector magnitude (EVM) of 11.73%. On the other hand, the QPSK component of the multi-band RF signal centered at 13.5 GHz is successfully converted to 3.1 GHz.

Effect of Breath Training on Image Quality of Chest Magnetic Resonance Free-breathing Sequence.

Magnetic Resonance Imaging (MRI) plays a role in demonstrating substantial utility in lung lesion imaging, detection, diagnosis, and evaluation. Previous studies have found that free-breathing star VIBE sequences not only have high image quality but also have a high ability to detect and display nodules. However, in our routine clinical practice, we have encountered suboptimal image quality in the free-breathing sequences of certain patients. This study aims to assess the impact of breath training on the quality of chest magnetic resonance imaging obtained during free-breathing sequences. A total of 68 patients with lung lesions, such as nodules or masses detected via Computed Tomography (CT) examination, were prospectively gathered. They were then randomly divided into two groups: an observation group and a control group. Standard preparation was performed for all patients in both groups before the examination. The observation group underwent 30 minutes of breath training prior to the MRI examination additionally, followed by the acquisition of MRI free-breathing sequence images. The signal intensity (SI) and standard deviation (SD) of the lesion and adjacent normal lung tissue were measured, and the image signal-to-noise ratio (SNR) and contrast signal-to-noise ratio (CNR) of the lesion were calculated for objective image quality evaluation. The subjective image quality of the two groups of images was also evaluated using a 5-point method. MRI examinations were completed in both groups. Significantly better subjective image quality (edge and internal structure clarity, vascular clarity, breathing and cardiac artifacts, and overall image quality) was achieved in the observation group compared to the control group (P<0.05). In addition, higher SNR and CNR values for disease lesions were observed in the observation group compared to the control group (t=4.35, P<0.05; t=5.35, P<0.05). It is concluded that the image quality of free-breathing sequences MRI can be improved through breath training before examination.

ECG signal quality in intermittent long-term dry electrode recordings with controlled motion artifacts.

Wearable long-term monitoring applications are becoming more and more popular in both the consumer and the medical market. In wearable ECG monitoring, the data quality depends on the properties of the electrodes and on how they interface with the skin. Dry electrodes do not require any action from the user. They usually do not irritate the skin, and they provide sufficiently high-quality data for ECG monitoring purposes during low-intensity user activity. We investigated prospective motion artifact-resistant dry electrode materials for wearable ECG monitoring. The tested materials were (1) porous: conductive polymer, conductive silver fabric; and (2) solid: stainless steel, silver, and platinum. ECG was acquired from test subjects in a 10-min continuous settling test and in a 48-h intermittent long-term test. In the settling test, the electrodes were stationary, whereas both stationary and controlled motion artifact tests were included in the long-term test. The signal-to-noise ratio (SNR) was used as the figure of merit to quantify the results. Skin-electrode interface impedance was measured to quantify its effect on the ECG, as well as to leverage the dry electrode ECG amplifier design. The SNR of all electrode types increased during the settling test. In the long-term test, the SNR was generally elevated further. The introduction of electrode movement reduced the SNR markedly. Solid electrodes had a higher SNR and lower skin-electrode impedance than porous electrodes. In the stationary testing, stainless steel showed the highest SNR, followed by platinum, silver, conductive polymer, and conductive fabric. In the movement testing, the order was platinum, stainless steel, silver, conductive polymer, and conductive fabric.

Fabrication of Flexible and Conductive Microneedle Array Electrodes from Silk Fibroin by Mesoscopic Engineering

AbstractConventional electrodes are of significant challenges in acquiring bioelectric signals concerning a high signal‐to‐noise ratio (SNR), long‐term and comfortable wear, low motional noise, etc. To resolve these issues, silk fibroin (SF) materials are adopted to create flexible microneedle array electrodes (FMNA‐electrode) based on meso‐engineering. The research approaches involve constructing flexible conductive electrodes from non‐conductive and bristle‐regenerated SF. By controlling the molecular interchain nucleation in the refolding process of unfolded regenerated SF molecules, this goal can be achieved. In this process, silver nanowires (Ag NWs) are adopted as meso‐dopants to reconstruct the meso‐electronic structure of SF films at the surface, while polyurethane (PU) is selected as molecular templates to facilitate the molecular nucleation in the refolding of SF molecules. This leads to the occurrence of SF‐PU meso‐hybridization, giving rise to more flexible meso‐hybridized SF. The SF‐PU FMNA‐electrode demonstrates biocompatibility, comfort wearing, long‐term monitoring capability, low motional noise, and a high SNR in the application of collecting ECG, EMG, and EOG signals. The outperformance of SF‐PU FMNA‐electrode is superior to both conventional wet and commercially available dry electrodes, paving the way for the next generation of flexible, skin‐friendly, durable, and high‐performance health monitoring devices and brain‐machine interfaces.

Hepatic steatosis modeling and MRI signal simulations for comparison of single- and dual-R2* models and estimation of fat fraction at 1.5T and 3T

Background and objectiveMagnetic resonance imaging (MRI) has emerged as a noninvasive clinical tool for assessment of hepatic steatosis. Multi-spectral fat-water MRI models, incorporating single or dual transverse relaxation decay rate(s) (R2*) have been proposed for accurate fat fraction (FF) estimation. However, it is still unclear whether single- or dual-R2* model accurately mimics in vivo signal decay for precise FF estimation and the impact of signal-to-noise ratio (SNR) on each model performance. Hence, this study aims to construct virtual steatosis models and synthesize MRI signals with different SNRs to systematically evaluate the accuracy of single- and dual-R2* models for FF and R2* estimations at 1.5T and 3.0T. MethodsRealistic hepatic steatosis models encompassing clinical FF range (0–60 %) were created using morphological features of fat droplets (FDs) extracted from human liver biopsy samples. MRI signals were synthesized using Monte Carlo simulations for noise-free (SNRideal) and varying SNR conditions (5–100). Fat-water phantoms were scanned with different SNRs to validate simulation results. Fat water toolbox was used to calculate R2* and FF for both single- and dual-R2* models. The model accuracies in R2* and FF estimates were analyzed using linear regression, bias plot and heatmap analysis. ResultsThe virtual steatosis model closely mimicked in vivo fat morphology and Monte Carlo simulation produced realistic MRI signals. For SNRideal and moderate-high SNRs, water R2* (R2*W) by dual-R2* and common R2* (R2*com) by single-R2* model showed an excellent agreement with slope close to unity (0.95–1.01) and R2 > 0.98 at both 1.5T and 3.0T. In simulations, the R2*com-FF and R2*W-FF relationships exhibited slopes similar to in vivo calibrations, confirming the accuracy of our virtual models. For SNRideal, fat R2* (R2*F) was similar to R2*W and dual-R2* model showed slightly higher accuracy in FF estimation. However, in the presence of noise, dual-R2* produced higher FF bias with decreasing SNR, while leading to only marginal improvement for high SNRs and in regions dominated by fat and water. In contrast, single-R2* model was robust and produced accurate FF estimations in simulations and phantom scans with clinical SNRs. ConclusionOur study demonstrates the feasibility of creating virtual steatosis models and generating MRI signals that mimic in vivo morphology and signal behavior. The single-R2* model consistently produced lower FF bias for clinical SNRs across entire FF range compared to dual-R2* model, hence signifying that single-R2* model is optimal for assessing hepatic steatosis.