Signal-to-noise Ratio Of Signal Research Articles

AI Denoising Significantly Enhances Image Quality and Diagnostic Confidence in Interventional Cone-Beam Computed Tomography.

(1) To investigate whether interventional cone-beam computed tomography (cbCT) could benefit from AI denoising, particularly with respect to patient body mass index (BMI); (2) From 1 January 2016 to 1 January 2022, 100 patients with liver-directed interventions and peri-procedural cbCT were included. The unenhanced mask run and the contrast-enhanced fill run of the cbCT were reconstructed using weighted filtered back projection. Additionally, each dataset was post-processed using a novel denoising software solution. Place-consistent regions of interest measured signal-to-noise ratio (SNR) per dataset. Corrected mixed-effects analysis with BMI subgroup analyses compared objective image quality. Multiple linear regression measured the contribution of “Radiation Dose”, “Body-Mass-Index”, and “Mode” to SNR. Two radiologists independently rated diagnostic confidence. Inter-rater agreement was measured using Spearman correlation (r); (3) SNR was significantly higher in the denoised datasets than in the regular datasets (p < 0.001). Furthermore, BMI subgroup analysis showed significant SNR deteriorations in the regular datasets for higher patient BMI (p < 0.001), but stable results for denoising (p > 0.999). In regression, only denoising contributed positively towards SNR (0.6191; 95%CI 0.6096 to 0.6286; p < 0.001). The denoised datasets received overall significantly higher diagnostic confidence grades (p = 0.010), with good inter-rater agreement (r ≥ 0.795, p < 0.001). In a subgroup analysis, diagnostic confidence deteriorated significantly for higher patient BMI (p < 0.001) in the regular datasets but was stable in the denoised datasets (p ≥ 0.103).; (4) AI denoising can significantly enhance image quality in interventional cone-beam CT and effectively mitigate diagnostic confidence deterioration for rising patient BMI.

SNR Enhancement of up to 9.5 Db Utilizing Four-Wave Mixing for Angle-Modulated Analog Optical Links

Angle modulation, such as phase and frequency modulation (FM/PM), has recently attracted attention for improving the fidelity of analog optical links. This is because angle modulation can enhance the signal-to-noise ratio (SNR) of the original analog message simply by increasing the modulation index or the bandwidth. However, direct generation of a broadband optical angle-modulated signal is not necessarily easy. On the other hand, there is an indirect generation method, where a narrowband angle-modulated signal is first generated and then converted to a broadband signal with the help of nonlinear devices. This method is often used in FM radio systems, but few reports are available on the optical implementation of this indirect method. In this paper, therefore, we propose the optical implementation by using four-wave mixing (FWM) in an optical fiber. Since an optical fiber naturally has a third-order nonlinearity (i.e., the Kerr effect), it should be possible to use fiber nonlinearity for the optical implementation of the indirect method to increase the bandwidth of an optical angle-modulated signal. We propose two types of FWM-based methods. In the first type, an angle-modulated signal is injected into a dispersion-shifted fiber (DSF) with an unmodulated pump wave. Consequently, one of the idlers generated via the FWM has a double modulation index, and the SNR of the original signal can be enhanced by 6 dB. In the second type, the input becomes a conjugate pair of an angle-modulated signal. Consequently, the idler has a threefold modulation index, and therefore, this type enhances the SNR by 9.5 dB. We experimentally confirmed the concept. The results showed that the modulation index can actually be increased almost threefold, and the idler can enhance the SNR of the original signal by approximately 8.8 dB.

Signal fusion research in passive radar based on polarization diversity technology

AbstractPolarization diversity technology is an effective method to improve the detection performance of passive radar systems, but the related papers mainly conduct research on the polarization diversity in interference suppression, and there are few studies focusing on the polarization characteristics of the target itself. The work in this paper is divided into two parts. The first part is going to investigate the potential benefits of signal fusion in target polarization-scattering characteristic. The analysis results show that the cross-polarized component of the target scattered echo is not always weaker than the co-polarized component, and the polarization amplitude ratio value is mostly concentrated near 1. This indicates that polarization signal fusion will likely improve target detection performance. The other part introduces a polarization signal fusion method based on the non-coherent integration (P-NCI), but this method is susceptible to the influence of the difference in the signal-to-noise ratio (SNR) of the signal of each polarization channel, resulting in a reduction in the SNR of the fusion signal. The polarization signal fusion method based on adaptive weighting (AW-PSF) can effectively improve the robustness of signal fusion. The experimental results show that AW-PSF has better detection performance compared with single-polarization channel and P-NCI.

Training strategies for deep learning gravitational-wave searches

Compact binary systems emit gravitational radiation which is potentially detectable by current Earth bound detectors. Extracting these signals from the instruments' background noise is a complex problem and the computational cost of most current searches depends on the complexity of the source model. Deep learning may be capable of finding signals where current algorithms hit computational limits. Here we restrict our analysis to signals from non-spinning binary black holes and systematically test different strategies by which training data is presented to the networks. To assess the impact of the training strategies, we re-analyze the first published networks and directly compare them to an equivalent matched-filter search. We find that the deep learning algorithms can generalize low signal-to-noise ratio (SNR) signals to high SNR ones but not vice versa. As such, it is not beneficial to provide high SNR signals during training, and fastest convergence is achieved when low SNR samples are provided early on. During testing we found that the networks are sometimes unable to recover any signals when a false alarm probability $<10^{-3}$ is required. We resolve this restriction by applying a modification we call unbounded Softmax replacement (USR) after training. With this alteration we find that the machine learning search retains $\geq 91.5\%$ of the sensitivity of the matched-filter search down to a false-alarm rate of 1 per month.

Sensing-Assisted Secure Uplink Communications With Full-Duplex Base Station

This letter proposes a sensing-assisted uplink communications framework between a single-antenna user and a full-duplex (FD) base station (BS) against an aerial eavesdropper (AE). To protect the information from being overheard, the BS transmits radar signals to localize and jam AE while receiving uplink signals. The radar signal transmission is divided into detection phase and tracking phase. In detection phase, the BS synthesizes a wide beam to localize the AE under the secrecy rate constraint; while in tracking phase, the BS maximizes the signal-to-interference-plus-noise ratio (SINR) of its received signals under the AE’s SINR constraint while guaranteeing a predefined radar echo signal signal-to-noise ratio (SNR) level. To deal with the self interference, we jointly optimize the radar waveform and receive beamforming vector. An alternating optimization algorithm and a successive convex approximation (SCA) based algorithm are proposed to solve the two formulated problems, respectively. Simulation results verify the effectiveness of the proposed algorithms. They also show that the secrecy rate can be significantly improved with the assistance of BS sensing.

Enhancement of spin noise spectroscopy of rubidium atomic ensemble by using the polarization squeezed light.

We measured the spin noise spectroscopy (SNS) of rubidium atomic ensemble with two different kinds of atomic vapor cells (filled with buffer gas or coated with paraffin film on the inner wall) and demonstrated the enhancement of the signal-to-noise ratio (SNR) by using polarization squeezed state (PSS) of 795-nm light field with Stokes operator S Λ 2 squeezed. The PSS is prepared by locking the relative phase between the squeezed vacuum state of light obtained with a sub-threshold optical parametric oscillator and the orthogonally polarized local oscillator beam by means of the quantum noise lock. Under the same conditions, the PSS can be employed not only to improve the SNR, but also to keep the full width at half maximum (FWHM) of SNS, compared with the case of using the polarization coherent state (PCS), enhancement of SNR is positively correlated with the squeezing level of the PSS. With increasing probe laser power and atomic number density, the SNR and FWHM of SNS will increase correspondingly. With the help of the PSS of the Stokes operator S Λ 2, quantum improvements of both the SNR and FWHM of SNS signal has been demonstrated by controlling optical power of polarization squeezed light beam or atomic number density in our experiments.

An Improved UKF Algorithm for Extracting Weak Signals Based on RBF Neural Network

It is difficult to extract effective weak signals from complex noise environment in many engineering applications. To address the problem, an improved unscented Kalman filter (UKF) algorithm based on a radial basis function (RBF)-neural network (NN) is proposed in this paper. This algorithm achieves effective extraction of weak signals under the condition of low signal-to-noise ratio (SNR). It uses RBF-NN to establish a distributed system of state and observation models to replace the traditional UKF modeling method. Then, the RBF-NN is applied to build a constant coefficient model to modify the UKF filtering deviation value, and thus, the accuracy of the algorithm is improved. Finally, simulation experiments are conducted at different SNRs of surface nuclear magnetic resonance signals. Results show that effective weak signals can still be obtained when SNR = -10 dB, verifying the effectiveness of the algorithm.

Signal Preprocessing Technique With Noise-Tolerant for RF-Based UAV Signal Classification

Since the beginning of the COVID-19 pandemic, the demand for unmanned aerial vehicles (UAVs) has surged owing to an increasing requirement of remote, noncontact, and technologically advanced interactions. However, with the increased demand for drones across a wide range of fields, their malicious use has also increased. Therefore, an anti-UAV system is required to detect unauthorized drone use. In this study, we propose a radio frequency (RF) based solution that uses 15 drone controller signals. The proposed method can solve the problems associated with the RF based detection method, which has poor classification accuracy when the distance between the controller and antenna increases or the signal-to-noise ratio (SNR) decreases owing to the presence of a large amount of noise. For the experiment, we changed the SNR of the controller signal by adding white Gaussian noise to SNRs of -15 to 15 dB at 5 dB intervals. A power-based spectrogram image with an applied threshold value was used for convolution neural network training. The proposed model achieved 98% accuracy at an SNR of -15 dB and 99.17% accuracy in the classification of 105 classes with 15 drone controllers within 7 SNR regions. From these results, it was confirmed that the proposed method is both noise-tolerant and scalable.

Assessment of higher-order singular value decomposition denoising methods on dynamic hyperpolarized [1-13C]pyruvate MRI data from patients with glioma

BackgroundReal-time metabolic conversion of intravenously-injected hyperpolarized [1-13C]pyruvate to [1-13C]lactate and [13C]bicarbonate in the brain can be measured using dynamic hyperpolarized carbon-13 (HP-13C) MRI. However, voxel-wise evaluation of metabolism in patients with glioma is challenged by the limited signal-to-noise ratio (SNR) of downstream 13C metabolites, especially within lesions. The purpose of this study was to evaluate the ability of higher-order singular value decomposition (HOSVD) denoising methods to enhance dynamic HP [1-13C]pyruvate MRI data acquired from patients with glioma. MethodsDynamic HP-13C MRI were acquired from 14 patients with glioma. The effects of two HOSVD denoising techniques, tensor rank truncation-image enhancement (TRI) and global–local HOSVD (GL-HOSVD), on the SNR and kinetic modeling were analyzed in [1-13C]lactate data with simulated noise that matched the levels of [13C]bicarbonate signals. Both methods were then evaluated in patient data based on their ability to improve [1-13C]pyruvate, [1-13C]lactate and [13C]bicarbonate SNR. The effects of denoising on voxel-wise kinetic modeling of kPL and kPB was also evaluated. The number of voxels with reliable kinetic modeling of pyruvate-to-lactate (kPL) and pyruvate-to-bicarbonate (kPB) conversion rates within regions of interest (ROIs) before and after denoising was then compared. ResultsBoth denoising methods improved metabolite SNR and regional signal coverage. In patient data, the average increase in peak dynamic metabolite SNR was 2-fold using TRI and 4–5 folds using GL-HOSVD denoising compared to acquired data. Denoising reduced kPL modeling errors from a native average of 23% to 16% (TRI) and 15% (GL-HOSVD); and kPB error from 42% to 34% (TRI) and 37% (GL-HOSVD) (values were averaged voxelwise over all datasets). In contrast-enhancing lesions, the average number of voxels demonstrating within-tolerance kPL modeling error relative to the total voxels increased from 48% in the original data to 84% (TRI) and 90% (GL-HOSVD), while the number of voxels showing within-tolerance kPB modeling error increased from 0% to 15% (TRI) and 8% (GL-HOSVD). ConclusionPost-processing denoising methods significantly improved the SNR of dynamic HP-13C imaging data, resulting in a greater number of voxels satisfying minimum SNR criteria and maximum kinetic modeling errors in tumor lesions. This enhancement can aid in the voxel-wise analysis of HP-13C data and thereby improve monitoring of metabolic changes in patients with glioma following treatment.

A generation and distribution system of clock signal source for signal acquisition system

AbstractThe clock signal is the heartbeat of modern electronic system, and demands of increasing high‐quality signal has been raised, with the development of science and technology in the field of electronic information. The clock signal with low jitter is helpful to improve the signal‐to‐noise ratio (SNR) of sampled data and obtain high‐precision result. In this article, a programmable clock system of signal generation and distribution is designed for high‐speed acquisition system. The system can provide the clock of low jitter which frequency is as high as 3.2 GHz. The output channels for clock are 14 and each channel for clock can also be configured for synchronizing, delay adjusting or calibrating. Based on the system, a new type of configuration program is developed by using Verilog language. It can directly control the chip which has better compatibility in the application scenario of Field Programmable Gate Array (FPGA) as the master chip. It also provides a reference and guidance for the design and implementation of the dual‐Phase Locked Loop clock chips. When it provides the clocks for the high‐speed acquisition system, the SNR of the acquisition signal has been significantly improved compared with similar acquisition systems. It indicates that the clock system we designed provides an effective way for the high‐speed acquisition system to obtain the data with higher SNR.

Denoising of ECG signals using weighted stationary wavelet total variation

ECG signals capture the electrical activity of the heart and can be used to determine its health. The P-QRS-T morphological features of the ECG may be used to identify a variety of cardiac problems. The existence of various disturbances in ECG signals, on the other hand, has a significant impact on correct feature extraction. During acquisition, various noises affect ECG readings, including power line interference, baseline wandering, motion artifacts, and electromyogram noise. In this paper, we proposed an algorithm called STationary Wavelet Total Variation (STWaTV) that introduces a total variation (TV) method with a bivariate shrinkage rule in the stationary wavelet transform (SWT) domain. STWaTV is tested with three existing threshold schemes: firm threshold, hard threshold, and bivariate shrinkage function with SWT for thresholding coefficients and TV for minimizing an objective function. To evaluate the effectiveness of STWaTV, we consider the standard databases and perform the comparative study along with existing state-of-the-art methods. In terms of qualitative analysis of STWaTV, the most significant signal to noise ratio (SNR) is in the range 13.23 to 53.61, root mean square error (RMSE) in the range 0.0006 to 1.1000, and the percentage root mean square difference (PRD) in the range 0.095 to 1.200. In sum, STWaTV outperform its counterparts and denoise ECG data without altering the amplitude of the original signals.

Boron and nitrogen co-doped vertical graphene electrodes for scalp electroencephalogram recording

Graphene has shown immense potential for applications in monitoring scalp electroencephalogram (EEG) signals. However, the radial head shape, high-resistance scalp cuticle, and hair prevent graphene from contacting skin, resulting in high contact impedances and low signal-to-noise ratios (SNRs) in EEG signals. Therefore, B and N co-doped vertical graphene (BNVG) electrodes are synthesized to improve the skin affinity and sweat adsorption capacity. X-ray photoelectron spectroscopy results show that the series of BNVG electrodes comprise B-doping contents of 1.25–9.85 at% and N-doping contents of 1.12–6.48 at%. A systematic analysis of the effects of B and N atom contents on the real-time scalp-contact resistances, EEG correlation coefficients between BNVG and Ag/AgCl electrodes, and SNRs of the EEG signals are conducted. The BNVG electrode comprising 4.47 at% B and 3.18 at% N is determined as the optimum electrode for scalp EEG signal acquisition. Furthermore, an EEG cap assembled with 19 optimized BNVG electrodes records spontaneous and evoked EEG signals. The BNVG-based EEG cap used with sweat or aqueous NaCl solution is confirmed to be advantageous and can be applied to clinical and brain-computer interface systems.

Wire Rope Weak Defect Signal Processing Methods Based on Improved SVD and Phase Space Reconstruction

Due to the complexity of the interference operation environment of wire rope, the detection signals are usually weak and coupled in time-frequency domain, which makes the defect difficult to recognize, while the signal characterizations in phase space are also needed to be studied. Combining the nonlinear dynamic feature identification theories, phase space characteristics and chaotic features of wire rope defect detection signals are mainly investigated in this paper. First, principles of phase space reconstruction method for wire rope detection signals are presented by the chaotic dynamic indexes calculation of embedded dimension and delay time. Second, the change trends of the correlation dimension, approximate entropy and Lyapunov index of different phase space reconstructed wire rope defect detection signals are studied through the nonlinear simulation and analysis. Finally, a phase space reconstruction algorithm based on improved SVD is proposed, and the new algorithm is also compared with traditional signal processing methods. These results obtained by 6 groups of experiments were also evaluated and compared by the parameters of signal-to-noise ratio (SNR) and phase space trajectory chart, which manifests that the improved algorithm not only can increase the weak detection signal SNR to about 2.3dB of wire rope effectively, but also demonstrate the feasibility of the proposed methods in application.

Clinical Evaluation of an Instrument-Integrated OCT-Based Distance Sensor for Robotic Vitreoretinal Surgery

PurposeTo assess the efficacy of an instrument-integrated OCT (iiOCT)-based distance sensor during robotic vitreoretinal surgery using the Preceyes Surgical System (PSS; Preceyes B.V.).DesignSingle-center interventional study.ParticipantsPatients requiring vitreoretinal surgery.MethodsFive patients were enrolled. Standard preoperative OCT images were obtained. After vitrectomy, a predefined set of actions was performed using the iiOCT-based sensor. Images then were processed to assess the signal-to-noise ratio (SNR) at various angles to the retina and at different distances between the instrument tip and the retinal surface. Preoperative and intraoperative OCT images were compared qualitatively and quantitatively.Main Outcomes MeasuresThe feasibility in performing surgical tasks using the iiOCT-based sensor during vitreoretinal surgery, the SNR when imaging the retina, differences among intraoperative and preoperative OCT images, and characteristics of intraoperative retinal movements detected with the iiOCT-based probe.ResultsSurgeons were able to perform all the tasks but one. The PSS was able to maintain a fixed distance. The SNR of the iiOCT-based sensor signal was adequate to determine the distance to the retina and to control the PSS. Analysis of iiOCT-based sensor A-scans identified 3 clearly distinguishable retinal layers, including the inner retinal boundary and the interface at the retinal pigment epithelium–Bruch’s membrane. Thickness values differed by less than 5% from that measured by preoperative OCT, indicating its accuracy. The Fourier analysis of iiOCT-based sensor recordings identified anteroposterior retinal movements attributed to heartbeat and respiration.ConclusionsThis iiOCT-based sensor was tested successfully and promises reliable use during robot-assisted surgery. An iiOCT-based sensor is a promising step toward OCT-guided robotic retinal surgery.

JOM-4S Overhauser Magnetometer and Sensitivity Estimation.

The Overhauser magnetometer is a scalar quantum magnetometer based on the dynamic nuclear polarization (DNP) effect in the Earth’s magnetic field. Sensitivity is a key technical specification reflecting the ability of instruments to sense small variations of the Earth’s magnetic field and is closely related to the signal-to-noise ratio (SNR) of the free induction decay (FID) signal. In this study, deuterated 15N TEMPONE radical is used in our sensor to obtain high DNP enhancement. The measured SNR of the FID signal is approximately 63/1, and the transverse relaxation time T2 is 2.68 s. The direct measurement method with a single instrument and the synchronous measurement method with two instruments are discussed for sensitivity estimation in time and frequency domains under different electromagnetic interference (EMI) environments and different time periods. For the first time, the correlation coefficient of the magnetic field measured by the two instruments is used to judge the degree of the influence of the environmental noise on the sensitivity estimation. The sensitivity evaluation in the field environment is successfully realized without electrical and magnetic shields. The direct measurement method is susceptible to EMI and cannot work in general electromagnetic environments, except it is sufficiently quiet. The synchronous measurement method has an excellent ability to remove most natural and artificial EMIs and can be used under noisy environments. Direct and synchronous experimental results show that the estimated sensitivity of the JOM-4S magnetometer is approximately 0.01 nT in time domain and approximately 0.01 nT/ in frequency domain at a 3 s cycling time. This study provides a low-cost, simple, and effective sensitivity estimation method, which is especially suitable for developers and users to estimate the performance of the instrument.

Coherent detection-based photonic radar for autonomous vehicles under diverse weather conditions.

Autonomous vehicles are regarded as future transport mechanisms that drive the vehicles without the need of drivers. The photonic-based radar technology is a promising candidate for delivering attractive applications to autonomous vehicles such as self-parking assistance, navigation, recognition of traffic environment, etc. Alternatively, microwave radars are not able to meet the demand of next-generation autonomous vehicles due to its limited bandwidth availability. Moreover, the performance of microwave radars is limited by atmospheric fluctuation which causes severe attenuation at higher frequencies. In this work, we have developed coherent-based frequency-modulated photonic radar to detect target locations with longer distance. Furthermore, the performance of the proposed photonic radar is investigated under the impact of various atmospheric weather conditions, particularly fog and rain. The reported results show the achievement of significant signal to noise ratio (SNR) and received power of reflected echoes from the target for the proposed photonic radar under the influence of bad weather conditions. Moreover, a conventional radar is designed to establish the effectiveness of the proposed photonic radar by considering similar parameters such as frequency and sweep time.

Enhanced pulsed thermoacoustic imaging by noncoherent pulse compression

Microwave-induced thermoacoustic imaging (TAI) is a hybrid imaging technique that combines electromagnetic radiation and ultrasonic waves to achieve high imaging contrast and submillimeter spatial resolution. These characteristics make TAI a good candidate to detect material anomalies that change the material electric properties without a noticeable variation in material density. Conventional pulsed TAI systems work by sending a single short pulse to the imaged target and then detecting the generated pressure signal; therefore, a very high peak power microwave pulse or data averaging is needed to produce images with a high signal-to-noise ratio (SNR). In this paper, we propose to enhance the SNR of pulsed TAI systems by using non-coherent pulse compression. In this approach, a predefined pulse coded signal is used to illuminate the imaged sample and the received pressure signal is cross correlated with a template that is related to the power profile of the excitation signal. The proposed approach can be easily deployed to pulsed TAI systems without the need for major system modifications to the RF source because it only requires a timing circuit to control the triggering time of the RF pulses. In this paper, we demonstrate experimentally that the proposed approach highly improves the SNR of TAI signals and images and can be used to reduce the acquisition time by lowering the number of data averaging or reduce the required peak power from RF sources.

Using ASE sources in remote beamforming system with Space-Division-Multiplex fiber

Weakly-coupled Space-Division-Multiplex (SDM) fiber is a promising candidate transmission medium for the remote true-time-delay (TTD) beamforming system. However, the signal-to-crosstalk beating interference (SCBI) can result in severe phase difference (or time delay) variation between TTD channels, which deteriorates the beam direction stability. Here, we develop a model to illustrate the phase variation problem when employing the coherent light source. To obtain a stable beam direction, we propose using an ASE source instead of a coherent source as the optical carrier. When utilizing the ASE source, the signal-to-noise ratio (SNR) of the remote RF signals is also thoroughly derived. It indicates that the RF signal with high SNR can be achieved via increasing the ASE source bandwidth and pre-compensating the fiber link dispersion. We carry out a proof-of-concept experiment of the remote beamforming system with the ASE source at the end. The variation of the phase difference between different channels is less than 3°. The SNR of the RF signal is over 110 dB with a 1 Hz bandwidth. These results pave the way for exploring the SDM fiber in the remote beamforming system.

A detective quantum efficiency for spectroscopic X-ray imaging detectors.

Spectroscopic X-ray detectors (SXDs) are under development for X-ray imaging applications. Recent efforts to extend the detective quantum efficiency (DQE) to SXDs impose a barrier to experimentation and/or do not provide a task-independent measure of detector performance. The purpose of this article is to define a task-independent DQE for SXDs that can be measured using a modest extension of established DQE-metrologymethods. We defined a task-independent spectroscopic DQE and performed a simulation study to determine the relationship between the zero-frequency DQE and the ideal-observer signal-to-noise ratio (SNR) of low-frequency soft-tissue, bone, iodine, and gadolinium signals. In our simulations, we used calibrated models of the spatioenergetic response of cadmium telluride (CdTe) and cadmium-zinc-telluride (CdZnTe) SXDs. We also measured the zero-frequency DQE of a CdTe detector with two energy bins and of a CdZnTe detector with up to six energy bins for an RQA9 spectrum and compared with modelpredictions. The spectroscopic DQE accounts for spectral distortions, energy-bin-dependent spatial resolution, interbin spatial noise correlations, and intrabin spatial noise correlations; it is mathematically equivalent to the squared SNR per unit fluence of the generalized least-squares estimate of the height of an X-ray impulse in a uniform noisy background. The zero-frequency DQE has a strong linear relationship with the ideal-observer SNR of low-frequency soft-tissue, bone, iodine, and gadolinium signals, and can be expressed in terms of the product of the quantum efficiency and a Swank noise factor that accounts for DQE degradation due to, for example, charge sharing (CS) and electronic noise. The spectroscopic Swank noise factor of the CdTe detector was measured to be 0.81 0.04 and 0.83 0.04 with and without anticoincidence logic for CS suppression, respectively. The spectroscopic Swank noise factor of the CdZnTe detector operated with four energy bins was measured to be 0.82 0.02 which is within 5% of the theoretical value. The spectroscopic DQE defined here is (1) task-independent, (2) can be measured using a modest extension of existing DQE-metrology methods, and (3) is predictive of the ideal-observer SNR of soft-tissue, bone, iodine, and gadolinium signals. For CT applications, the combination of CS and electronic noise in CdZnTe spectroscopic detectors will degrade the zero-frequency DQE by 10%-20% depending on the electronic noise level and pixel size.

Long-term stability of the chronic epidural wireless recorder WIMAGINE in tetraplegic patients

Objective. Theevaluation of the long-term stability of ElectroCorticoGram (ECoG) signals isan important scientific question as new implantable recording devices can beused for medical purposes such as Brain-Computer Interface (BCI) or brainmonitoring. Approach. The long-term clinical validation of wireless implantable multi-channel acquisition system for generic interface with neurons (WIMAGINE), a wireless 64-channel epidural ECoG recorder was investigated. The WIMAGINE device was implanted in two quadriplegic patients within the context of a BCI protocol. This study focused on the ECoG signal stability in two patients bilaterally implanted in June 2017 (P1) and in November 2019 (P2). Methods. The ECoG signal was recorded at rest prior to each BCI session resulting in a 32 month and in a 14 month follow-up for P1 and P2 respectively. State-of-the-art signal evaluation metrics such as root mean square (RMS), the band power (BP), the signal to noise ratio (SNR), the effective bandwidth (EBW) and the spectral edge frequency (SEF) were used to evaluate stability of signal over the implantation time course. The time-frequency maps obtained from task-related motor activations were also studied to investigate the long-term selectivity of the electrodes. Main results. Based on temporal linear regressions, we report a limited decrease of the signal average level (RMS), spectral distribution (BP) and SNR, and a remarkable steadiness of the EBW and SEF. Time-frequency maps obtained during motor imagery, showed a high level of discrimination 1 month after surgery and also after 2 years. Conclusions. The WIMAGINE epidural device showed high stability over time. The signal evaluation metrics of two quadriplegic patients during 32 months and 14 months respectively provide strong evidence that this wireless implant is well-suited for long-term ECoG recording. Significance. These findings are relevant for the future of implantable BCIs, and could benefit other patients with spinal cord injury, amyotrophic lateral sclerosis, neuromuscular diseases or drug-resistant epilepsy.