SNR Improvement Research Articles

Denoising Raman spectra using a single layer convolutional model trained on simulated data

AbstractRaman spectroscopy is a powerful means of revealing chemical and structural information about a sample and acquiring chemically specific images. Such images often suffer from low signal to noise ratios (SNR). In this report, a novel way to improve the SNR using machine learning tools based on simulated data. The proposed approach offers an alternative to time consuming acquisition and labeling of large data sets and can be readily applied to unknown systems. Here, the efficacy of a single layer denoising network trained only on simulated data was evaluated, and it was found that the proposed model was able to provide a substantial improvement in SNR.

Dynamic distributed optical fiber sensing based on simultaneous measurement of Brillouin gain and loss spectra with frequency-agile technique.

To simplify the experimental equipment and improve the signal-to-noise ratio (SNR) of the traditional Brillouin optical time-domain analysis (BOTDA) system, we propose a scheme using the frequency-agile technique to measure Brillouin gain and loss spectra simultaneously. The pump wave is modulated into the double-sideband frequency-agile pump pulse train (DSFA-PPT), and the continuous probe wave is up-shifted by a fixed frequency value. With the frequency-scanning of DSFA-PPT, pump pulses at the -1st-order sideband and the +1st-order sideband interact with the continuous probe wave via stimulated Brillouin scattering, respectively. Therefore, the Brillouin loss and gain spectra are generated simultaneously in one frequency-agile cycle. Their difference relates to a synthetic Brillouin spectrum with a 3.65-dB SNR improvement for a 20-ns pump pulse. This work simplifies the experimental device, and no optical filter is needed. Static and dynamic measurements are performed in the experiment.

Performance evaluation of DFT based speckle reduction framework for synthetic aperture radar (SAR) images at different frequencies and image regions

Polarimetric Synthetic Aperture Radar (PolSAR) images are widely used for remote sensing and geoscience applications. However, the coherent processing of radar signals in PolSAR imaging leads to the presence of speckle noise, which can significantly degrade image quality and limit the accuracy of subsequent analyses. To address this issue, speckle reduction frameworks are often applied to PolSAR images to reduce the noise level and enhance image quality. In this paper, the performance of Discrete Fourier Transform (DFT) based speckle reduction framework is evaluated on different bands (L, C, and P band) against various evaluation metrics like CV, SD, SNR, ENL, SSI and SMPI. The proposed framework is evaluated by comparing filtered and unfiltered images across different parameters, such as mean, standard deviation, coefficient of variation, equivalent number of looks (ENL), variance, and signal-to-noise ratio (SNR) on all three bands for the diagonal elements (T11, T22, and T33) of T3 matrix. These metrics provide a comprehensive evaluation of the proposed framework’s ability to (i) smoothen homogeneous regions, (ii) preserve contours, and (iii) retain polarimetric information. The framework’s ability to reduce speckle noise and improve image quality is demonstrated through a significant reduction in standard deviation, coefficient of variation, and improvement in SNR and ENL values. The proposed framework successfully preserved polarimetric information while effectively suppressing speckle noise. These results suggest that the proposed framework could be a valuable tool for improving PolSAR image quality and enhancing subsequent processing of PolSAR data.

Comparator‐noise‐based residue measurement and correction technique in 16 bit 1 MS/s SAR ADC

AbstractThis letter presents an accuracy enhancement technique utilized in 16 bit fully differential successive‐approximation‐register analog‐to‐digital converters (SAR ADC). For noise performance improvement to obtain a higher ENOB, the residue measurement technique which statistically estimates the input residual error of comparator with Gaussian noise fitting is presented. The ADC digital output is compensated by the residue measurement results. It is verified in the 0.18‐μm 1P5M 5 V CMOS process. Finally, a 93.1 dB SNR and a 110.5 dB SFDR are measured operating at 1 MS/s with a 10‐kHz input tone and larger than 1‐dB SNR improvement is obtained in different chips.

Automatic RF Leakage Cancellation for Improved Remote Vital Sign Detection Using a Low-IF Dual-PLL Radar System

Remote vital sign detection using Doppler radar has gained increasing interest over the years. However, challenges remain in improving radar system sensitivity to extend the detection range. In this article, we analyze noise contributions in a dual-PLL low-IF Doppler radar system and identify RF leakage/coupling as a major component of the system noise floor. This effect results in negligible SNR improvement when the transmit output power is increased beyond a certain threshold. By applying RF leakage/coupling cancellation through phase and magnitude adjustments, we demonstrate significant improvements to radar system performance. Furthermore, we propose and validate an automatic RF leakage cancellation scheme. The proposed technique is shown to achieve a vital-sign-sensing range of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4$</tex-math> </inline-formula> m under a very low transmit power of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$-$</tex-math> </inline-formula> 31 dBm and with a high confidence level of three beats per minute against the ground truth measured by an oximeter sensor.

Phase correction based SNR enhancement for distributed acoustic sensing with strong environmental background interference

Fiber-optic distributed acoustic sensing (DAS) systems based on phase-sensitive optical time domain reflectometry (Φ-OTDR) measure acoustic waves by demodulating the phase variations of the Rayleigh backscattering (RBS) signals in a sensing optical fiber. However, in harsh environments, strong environmental background interference, coupled with the interference fading of the RBS, would introduce severe distortions in DAS signal that cannot be corrected or even can be worsened by phase unwrapping, thus resulting in low signal to noise ratio (SNR) and even undetectable signal. In this work, a novel method based on trend prediction is proposed to correct the distortions induced by phase unwrapping error around the fading points. The method is further validated in processing the data acquired from a field test performed in ocean environments using a home-built DAS system. By locating and erasing the distortion points, and then detrending, the acoustic signal buried in strong environmental background interference is retrieved with an SNR improvement greater than 10 dB. The results show that the proposed phase correction method can effectively enhance DAS's SNR for those challenging applications with strong background interference.

Signal-to-noise analysis of point target detection using image pixel binning for space-based infrared electro-optical systems

It is well known that pixel binning is an effective method for improving the signal-to-noise ratio (SNR) of silicon detectors. It has found extensive use in the study of spectroscopy, astronomy, and in the development of numerous image processing algorithms. However, the positive impact of binning technique on SNR does not happen at any moment. In this paper, we model the scenario of the detection of the point target. Our study analyzes the effects of variables such as encircled energy (EE), target motion velocity, and integration time on the imaging process. For different detection scenarios, we quantitatively discussed the degree of improvement in SNR of image pixel binning technique for detecting point targets. Based on actual 3483 point target imaging data collected, the model was validated with an average error of 0.016. A wide range of detection scenarios is covered by the proposed model with high accuracy, providing a useful reference for the design of electro-optical systems.

Association Between Risk of Stroke and Delirium After Cardiac Surgery and a New Electroencephalogram Index of Interhemispheric Similarity

Neurologic complications after surgery (stroke, delirium) remain a major concern despite advancements in surgical and anesthetic techniques. The authors aimed to evaluate whether a novel index of interhemispheric similarity, the lateral interconnection ratio (LIR), between 2 prefrontal electroencephalogram (EEG) channels could be associated with stroke and delirium following cardiac surgery. Retrospective observational study. Single university hospital. A total of 803 adult patients without documentation of a previous stroke, who underwent cardiac surgery with cardiopulmonary bypass (CPB) between July 2016 and January 2018. The LIR index was calculated retrospectively from the patients' EEG database. LIR was analyzed intraoperatively every 10 seconds and compared among patients with postoperative stroke, patients with delirium, and patients without documented neurologic complications, during 5 key periods, each lasting10 minutes: (1) surgery start, (2) before CPB, (3) on CPB, (4) after CPB, and (5) surgery end. After cardiac surgery, 31 patients suffered from stroke; 48 patients were diagnosed with delirium; and 724 had no documented neurologic complications. Patients with stroke demonstrated a decrease in LIR index between the start of surgery and the postbypass period of 0.08 (0.01, 0.36 [21]; median and [interquartile range {IQR}]; valid EEG samples); whereas there was no similar decrease in the no-dysfunction group (-0.04 [-0.13, 0.04; {551}], p < 0.0001). Patients with delirium showed a decrease in LIR index between the start of surgery and the end of the surgery by 0.15 (0.02, 0.30 [12]), compared with no such decrease in the no-dysfunction group (-0.02 [-0.12, 0.08 {376}], p ≈ 0.001). After improvement of SNR, it might be of value to further study the index decrease as an indication for risk for brain injury after surgery. The timing of decrease (after CPB or end of surgery) may provide hints regarding the injury pathophysiology and its onset.

A non-linear triangular split-ring based metaresonator for targeted scanning at 1.5T MRI

In magnetic resonance imaging (MRI), RF signals are initially transmitted to stimulate the body protons which eventually release the electromagnetic energy while returning back to their original states. The image resolution and scanning efficiency of MRI can be improved by enhancing the magnetic fields received from the patient’s body using metamaterials. The major limitation of linear metamaterials is that they amplify RF magnetic fields both during transmission and reception phases. This requires modification of the RF excitation pulses during the transmission phase. Further, local increase of transmitted power poses a potential threat of tissue-heating and high specific absorption rate (SAR) values in addition to perturbing the transmit field homogeneity. In order to circumvent these problems, we propose a self-adaptive metaresonator which has the capability of self-detuning itself during transmission of RF pulses during MRI scans. A triangular split-ring based metaresonator is designed for maximum thirty-fold SNR improvement in 1.5T MRI. Switching diodes have been employed for switching on and off the magnetic field enhancement by the metaresonator. During transmission phase when the switching diodes get turned on, the metaresonator is detuned. During reception phase when the switching diodes get turned off, the metaresonator is tuned to 63.8 MHz which is the Larmor frequency of 1.5T MRI. The proposed metaresonator is thin and compact which enables its easy placement in the multi-element arrays of clinical MRI.

5T magnetic resonance imaging: radio frequency hardware and initial brain imaging.

We aimed to demonstrate the feasibility of generating high-resolution human brain magnetic resonance imaging (MRI) at 5 Tesla (T) using a quadrature birdcage transmit/48-channel receiver coil assembly. A quadrature birdcage transmit/48-channel receiver coil assembly was designed for human brain imaging at 5T. The radio frequency (RF) coil assembly was validated by electromagnetic (EM) simulations and phantom imaging experimental studies. The simulated B1+ field inside a human head phantom and inside a human head model generated by the birdcage coils driven in circularly polarized (CP) mode at 3T, 5T and 7T was compared. Signal-to-noise ratio (SNR) maps, the inverse g-factor maps for evaluation of parallel imaging performance, anatomic images, angiography images, vessel wall images and susceptibility weighted images (SWI) were acquired using the RF coil assembly at 5T and compared to those acquired using a 32-channel head coil on a 3T MRI scanner. For the EM simulations, 5T MRI provided less RF inhomogeneity compared to that of 7T. In the phantom imaging study, the distributions of the measured B1+ field were consistent with the distributions of the simulated B1+ field. In the human brain imaging study, the average SNR value of the brain in the transversal plane at 5T was 1.6 times of that at 3T. The 48-channel head coil at 5T had higher parallel acceleration capability than the 32-channel head coil at 3T. The anatomic images at 5T also showed higher SNR than those at 3T. Improved delineation of the hippocampus, lenticulostriate arteries, and basilar arteries was observed at 5T compared to 3T. SWI with a higher resolution of 0.3 mm ×0.3 mm ×1.2 mm could be acquired at 5T, which enabled better visualization of small blood vessels compared to that at 3T. 5T MRI can provide significant SNR improvement compared to that of 3T with less RF inhomogeneity than that of 7T. The ability to obtain high quality in vivo human brain images at 5T using the quadrature birdcage transmit/48-channel receiver coil assembly has significant in clinical and scientific research applications.

Microwave photonic radar system with improved SNR performance utilizing optical resonant amplification and random Fourier coefficient waveforms.

A microwave photonic (MWP) radar system with improved signal-to-noise ratio (SNR) performance is proposed and experimentally demonstrated. By improving the SNR of echoes through properly designed radar waveforms and resonant amplification in the optical domain, the proposed radar system can detect and image weak targets that were previously hidden in noise. Echoes with a common low-level SNR obtain high optical gain and the in-band noise is suppressed during resonant amplification. The designed radar waveforms, based on random Fourier coefficients, reduce the effect of optical nonlinearity while providing reconfigurable waveform performance parameters for different scenarios. A series of experiments are developed to verify the feasibility of the SNR improvement of the proposed system. Experimental results show a maximum SNR improvement of 3.6 dB with an optical gain of 28.6 dB for the proposed waveforms over a wide input SNR range. From a comparison with linear frequency modulated signals in microwave imaging of rotating targets, significant quality enhancement is observed. The results confirm the ability of the proposed system to improve SNR performance of MWP radars and its great application potential in SNR-sensitive scenarios.

Robust Signal Denoising Techniques for Highly Accurate and Spatial Resolution Preserved Brillouin Optical Time Domain Analysis Sensors

The diminution of measurement uncertainty to an acceptable level and the preservation of experimental spatial resolution are highly desirable in many real-world applications of Brillouin optical time domain analysis (BOTDA) sensors. In practical applications of such sensors, the measurement uncertainty essentially relies upon the signal-to-noise ratio (SNR) of the experimental Brillouin gain spectra (BGSs) obtained throughout the sensing fiber. The improvement of such SNR using improper signal denoising techniques alters the experimental spatial resolution of the BOTDA sensors. In this paper, the use of non-local means filter (NLMF) and anisotropic diffusion filter (ADF) is experimentally demonstrated to enhance the uncertainty in the measurement of temperature using BOTDA sensors. For this purpose, the BGSs along a 41 km fiber are collected by averaging several numbers of BOTDA-traces from BOTDA hardware setup. Such BGSs are first denoised by employing NLMF and ADF to improve the measurement SNR. The Brillouin frequency shifts (BFSs) of denoised BGSs are then extracted via curve fitting technique (CFT). The BFS distribution is finally mapped to temperature distribution depending on the known BFS-temperature relationship of the sensing fiber. The robustness of the used filters is analyzed rigorously in terms of SNRs of BGSs, uncertainty in temperature extraction, spatial resolution and runtime in signal processing. The results indicate that the utilization of NLMF and ADF can enhance the SNRs of BGSs up to the maximum of 15.64 dB and 13.53 dB, respectively. Consequently, the uncertainties in the temperature extraction can be reduced up to the maximum of 52.63% and 57.31% for using NLMF and ADF, respectively. Moreover, both NLMF and ADF can preserve the experimental spatial resolution of the BOTDA sensors and include insignificant runtime to CFT. Thus, NLMF and ADF can be considered as robust signal denoising techniques for highly accurate and spatial resolution preserved BOTDA sensors. DUJASE Vol. 7 (2) 31-38, 2022 (July)

Optimization of MP2RAGE T1 mapping with radial view-ordering for deep brain stimulation targeting at 7 T MRI

PurposeDeep brain stimulation (DBS) is an effective treatment of various neurological disorders. Due to higher intrinsic signal, 7 T MRI can potentially improve delineation of DBS targets. However, the severe RF transmit field (B1+) inhomogeneity at 7 T can compromise the image contrast of traditional single-contrast sequences for DBS targeting, leading to sub-optimal target visualization. The Magnetization Prepared 2 Rapid Acquisition Gradient Echo (MP2RAGE)-based T1 mapping provides an alternative to the traditional single-contrast techniques by allowing retrospective synthesis of images at arbitrary inversion times to aid in visualization of various DBS targets. With this approach, optimization of sequence parameters to create T1 maps with low noise and low quantification bias is critical, as these characteristics directly affect the noise and uniformity of the synthetic images. In this work, we perform sequence optimization for MP2RAGE-based T1 mapping using a radial view-ordering technique to improve image quality, and demonstrate the clinical utility of T1 mapping approach for DBS targeting. MethodsWe first introduce a systematic sequence optimization framework for 7 T MP2RAGE T1 mapping by formulating it into a constrained, multi-dimensional optimization process considering the effect of B1+ inhomogeneity on image noise, T1 quantification bias, and image blurring. With this framework, we investigate the use of radial view-order approach for T1 mapping, in lieu of the conventional linear view-ordering. Bloch's equation-based simulations were performed to compare the T1 maps generated using different approaches. Images of healthy volunteer and patients were acquired on a clinical 7 T MRI scanner for validation and to demonstrate the utility of T1 mapping for DBS targeting. ResultsNumerical experiments demonstrated that the proposed framework allowed optimization of image SNR in T1 maps while controlling the quantification bias and image blurring, therefore facilitating the selection of optimal sequence parameters for visualizing DBS targets. The optimized sequence using radial view-ordering offered 40–60% noise reduction compared to the linear view-ordering. The improvement of SNR was confirmed in the in vivo examples. Clinical images showed that the synthetic images generated from the optimized T1 maps allowed clear visualization of DBS targets. ConclusionWe demonstrated the optimization of MP2RAGE T1 mapping with radial view-ordering technique for DBS targeting at 7 T and showed that the optimized sequence allows retrospective generation of synthetic inversion time images commonly utilized in DBS targeting, such as fast gray matter acquisition T1 inversion recovery (FGATIR) and edge-enhancing gradient echo (EDGE) sequences.

Small training dataset convolutional neural networks for application-specific super-resolution microscopy.

Machine learning (ML) models based on deep convolutional neural networks have been used to significantly increase microscopy resolution, speed [signal-to-noise ratio (SNR)], and data interpretation. The bottleneck in developing effective ML systems is often the need to acquire large datasets to train the neural network. We demonstrate how adding a "dense encoder-decoder" (DenseED) block can be used to effectively train a neural network that produces super-resolution (SR) images from conventional microscopy diffraction-limited (DL) images trained using a small dataset [15 fields of view (FOVs)]. The ML helps to retrieve SR information from a DL image when trained with a massive training dataset. The aim of this work is to demonstrate a neural network that estimates SR images from DL images using modifications that enable training with a small dataset. We employ "DenseED" blocks in existing SR ML network architectures. DenseED blocks use a dense layer that concatenates features from the previous convolutional layer to the next convolutional layer. DenseED blocks in fully convolutional networks (FCNs) estimate the SR images when trained with a small training dataset (15 FOVs) of human cells from the Widefield2SIM dataset and in fluorescent-labeled fixed bovine pulmonary artery endothelial cells samples. Conventional ML models without DenseED blocks trained on small datasets fail to accurately estimate SR images while models including the DenseED blocks can. The average peak SNR (PSNR) and resolution improvements achieved by networks containing DenseED blocks are and , respectively. We evaluated various configurations of target image generation methods (e.g., experimentally captured a target and computationally generated target) that are used to train FCNs with and without DenseED blocks and showed that including DenseED blocks in simple FCNs outperforms compared to simple FCNs without DenseED blocks. DenseED blocks in neural networks show accurate extraction of SR images even if the ML model is trained with a small training dataset of 15 FOVs. This approach shows that microscopy applications can use DenseED blocks to train on smaller datasets that are application-specific imaging platforms and there is promise for applying this to other imaging modalities, such as MRI/x-ray, etc.

Non-integral depth measurement of high-aspect-ratio multi-layer microstructures using numerical-aperture shaped beams

This work presents a new optical metrology technique for accurate depth measurement of individual high-aspect-ratio (HAR) multi-layer microstructures with high spatial resolution and signal-to-noise ratio (SNR). To resolve the limitations of existing optical metrology techniques, light shaping with high light efficiency is optimized using numerical-aperture controlled laser beams. Experimental tests evidenced a 28-fold improvement in SNR for measuring large-depth structures when compared with measurements obtained using traditional broadband incoherent illumination. More importantly, instead of integral measurement characteristics currently limited by conventional spectral reflectometry or scatterometry, non-integral depth measurement of an individual microstructure from densely spaced microstructures such as through-silicon vias (TSV) and redistribution layers (RDL) can be realized using the developed optical measuring system with desired numerical aperture and field of view achieved simultaneously. As demonstrated by the measurement of a single submicron structure with linewidth as small as 0.6 µm and an aspect ratio of 5, the precision of depth measurement can be kept within a few nanometers.

Graceful Performance Degradation and Improved Error Tolerance via Mixed-Mode Distributed Coherent Radar

Distributed coherent radar (DCR) is a distributed multiple-input–multiple-output (MIMO) radar concept, which holds the potential to significantly improve signal-to-noise ratio (SNR) over single aperture and statistical MIMO systems. DCR achieves coherence in one of two modes: Cohere on transmit (COT), which yields an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${N}^{{3}}$ </tex-math></inline-formula> improvement in SNR ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${N}$ </tex-math></inline-formula> is the number of radars) but requires ideal coherence parameter (CP) information before transmission, and cohere on receive (COR), which yields an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${N}^{{2}}$ </tex-math></inline-formula> improvement in SNR but can generally estimate CPs adaptively on reception. System concepts generally make a binary choice between COR and COT operation based on the error level. This has led to multiple studies of CP error and its impact on performance of each mode. In this work, a CP error analysis has been extended to include all three principle CPs (delay, phase, and frequency), as well as simultaneous consideration for the number of radars in the constituent system. This novel analysis reveals significant relationships between the number of elements in a system and the resulting error tolerance. A novel DCR operating mode, mixed-mode DCR (MM-DCR), is introduced, which exploits this relationship and achieves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${N}^{{2} + \log _{N}({M})}$ </tex-math></inline-formula> SNR gain, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${M}$ </tex-math></inline-formula> is the number of elements used for each subaperture. MM-DCR is shown to yield better performance than COR and COT under a broad set of error conditions, yielding overall system error tolerance improvements. Moreover, as error varies during system operation, MM-DCR facilitates graceful performance degradation by varying the size of the subapertures.

Recent developments regarding the 15 dB improvement in SNR in noise available from four talkers in noise with wireless signals provided by simple clip-on-collar Companion Mics Remote Microphones

The Companion Mics® (CMICS™) by Etymotic Research, and more recently by the author’ company MCK Audio, have frequently demonstrated a 10–20 dB improvement in SNR in noisy groups and restaurants for up to four talkers, each of whom wears a CMIC wireless microphone unit on the collar or held near the chin. Until recently, however, the listener would need to wear an earphone. (Experimentally, two ears listening to the same signal provide only a 2 dB advantage.) The disadvantage was that the hearing aid audiologist would have to say: “If you have trouble in noise, take off your hearing aids and put on these earphones.” While waiting for the incorporation of Bluetooth circuits, two other successful alternates have been introduced. The first is an open-ear HearHook© sound tube which hooks over the ear and delivers sound near the earcanal, which is also near the microphone of a hearing aid. The second is the use of a neckloop connected to the CMIC, with the hearing aid switched to “tcoil” mode. An amplified tcoil HHearbud with built-in hum filter allows that use even when electrical hum interference is present.

Spatially resolved multimode excitation for smooth supercontinuum generation in a SiN waveguide.

We propose a method of supercontinuum light generation enhanced by multimode excitation in a precisely dispersion-engineered deuterated SiN (SiN:D) waveguide. Although a regularly designed SiN-based nonlinear optical waveguide exhibits anomalous dispersion with the fundamental and first-order multimode operation, the center-symmetric light pumping at the input edge has so far inhibited the full potential of the nonlinearity of SiN-based materials. On the basis of numerical analysis and simulation for the SiN:D waveguide, we intentionally applied spatial position offsets to excite the fundamental and higher-order modes to realize bandwidth broadening with flatness. Using this method, we achieved an SNR improvement of up to 18 dB at a wavelength of 0.6 µm with an offset of about 1 µm in the Y-axis direction and found that the contribution was related to the presence of dispersive waves due to the excitation of TE10, and TE01 modes.

Quantitative Evaluation of Scatter Correction in 128-slice Fan-Beam Computed Tomography Scan using Geant4 Application for Tomographic Emission Monte Carlo Simulation.

Simulation of tomographic imaging systems with fan-beam geometry, estimation of scattered beam profile using Monte Carlo techniques, and scatter correction using estimated data have always been new challenges in the field of medical imaging. The most important aspect is to ensure the results of the simulation and the accuracy of the scatter correction. This study aims to simulate 128-slice computed tomography (CT) scan using the Geant4 Application for Tomographic Emission (GATE) program, to assess the validity of this simulation and estimate the scatter profile. Finally, a quantitative comparison of the results is made from scatter correction. In this study, 128-slice CT scan devices with fan-beam geometry along with two phantoms were simulated by GATE program. Two validation methods were performed to validate the simulation results. The data obtained from scatter estimation of the simulation was used in a projection-based scatter correction technique, and the post-correction results were analyzed using four quantities, such as: pixel intensity, CT number inaccuracy, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR). Both validation methods have confirmed the appropriate accuracy of the simulation. In the quantitative analysis of the results before and after the scatter correction, it should be said that the pixel intensity patterns were close to each other, and the accuracy of the CT scan number reached <10%. Moreover, CNR and SNR have increased by more than 30%-65% respectively in all studied areas. The comparison of the results before and after scatter correction shows an improvement in CNR and SNR while a reduction in cupping artifact according to pixel intensity pattern and enhanced CT number accuracy.

Deep Learning Methods for Improving Electromagnetic Telemetry Signal-to-Noise Ratio

Summary Electromagnetic (EM) telemetry is a cost-competitive method for communication between downhole tools and oilfield surface equipment. However, EM telemetry use is limited to wellsite deployments that result in favorable signal-to-noise ratio (SNR) levels. Increasingly, the EM attenuation brought by deeper wells, conductive service fluids, and conductive formation layers has made it more important to increase EM telemetry SNR. This study explored the ability of various deep learning (DL) methods to improve EM telemetry SNR with data sets composed of mixed synthetic downhole signals and real field noise. The DL models explored were previously proven to achieve state-of-the-art performance with seismic random noise attenuation and speech separation use cases. Furthermore, architectural adjustments that improved model performance with EM telemetry data were targeted. Then, the SNR improvement performance of different design changes was evaluated. Finally, the study targeted improvements that reduced the requirements for deployment hardware, including number of stakes, without sacrificing SNR improvement performance. Optimized models were found to successfully improve SNR in EM telemetry data, outperforming traditional bandpass filter methods and enabling the demodulation of mixtures that were otherwise too noisy to be demodulated.