Signal-to-noise Research Articles

Denoising methods for removal of Baseline Wander, AWGN and power line interface noises in ECG signal: a comparative analysis

ABSTRACT The electrocardiogram (ECG) is one of the most significant signals in the field of biomedicine for the diagnosis of cardiac arrhythmia (CA). Due to non-stationary behaviour of the ECG signal, it is often interrupted with various noises, which cause difficulty in diagnosis. To solve the issues, the image denoising process helps the physicians make the perfect decision. In this paper, Denoising Techniques using Empirical Mode Decomposition (EMD) and Wavelet Transform for the removal of various noises like Additive White Gaussian Noise (AWGN), Baseline Wander (BW) noise, Power Line Interface (PLI) noise at various concentrations. The proposed method has quality and good efficiency in Peak Signal-to-Noise Ratio (PSNR), Root Mean Square Error (RSME), Signal-to-Noise Ratio (SNR), Cross-Correlation (CC) and Percent Root Square Difference (PRD).

Dynamic MRI interpolation in temporal direction using an unsupervised generative model

PurposeCardiac cine magnetic resonance imaging (MRI) is an important tool in assessing dynamic heart function. However, this technique requires long acquisition time and long breath holds, which presents difficulties. The aim of this study is to propose an unsupervised neural network framework that can perform cardiac cine interpolation in time, so that we can increase the temporal resolution of cardiac cine without increasing acquisition time. MethodsIn this study, a subject-specific unsupervised generative neural network is designed to perform temporal interpolation for cardiac cine MRI. The network takes in a 2D latent vector in which each element corresponds to one cardiac phase in the cardiac cycle and then the network outputs the cardiac cine images which are acquired on the scanner. After the training of the generative network, we can interpolate the 2D latent vector and input the interpolated latent vector into the network and the network will output the frame-interpolated cine images. The results of the proposed cine interpolation neural network (CINN) framework are compared quantitatively and qualitatively with other state-of-the-art methods, the ground truth training cine frames, and the ground truth frames removed from the original acquisition. Signal-to-noise ratio (SNR), structural similarity index measures (SSIM), peak signal-to-noise ratio (PSNR), strain analysis, as well as the sharpness calculated using the Tenengrad algorithm were used for image quality assessment. ResultsAs shown quantitatively and qualitatively, the proposed framework learns the generative task well and hence performs the temporal interpolation task well. Furthermore, both quantitative and qualitative comparison studies show the effectiveness of the proposed framework in cardiac cine interpolation in time. ConclusionThe proposed generative model can effectively learn the generative task and perform high quality cardiac cine interpolation in time.

Benefits of speech recognition in noise using remote microphones for people with typical hearing

IntroductionRemote microphone (RM) systems are designed to enhance speech recognition in noisy environments by improving the signal-to-noise ratio (SNR) for individuals with typical hearing (TH) and hearing impairment (HI). The aim of this investigation was to evaluate the advantages of speech recognition in noise for individuals with TH in a simulated group setting using two different remote microphones. MethodsA quasi-experimental, repeated-measures design was employed, involving ten participants with TH, ages 20 to 63 years. Each were fit with Roger Focus receivers bilaterally to listen to three RM conditions: Roger Select, Roger Pen, and no technology. Participants were instructed to transcribe sentences that were presented randomly at varying signal-to-noise ratios (SNRs: 0, -5, and -10 dB) from five speakers positioned equidistant around a circular table to simulate a group dining scenario. ResultsSignificant main effects of the technology condition and noise level (p < .05) were found. Participants exhibited superior performance with Roger Select compared to Roger Pen. As expected, recognition rates decreased with lower SNRs across all three technology conditions. ConclusionsTo enhance speech recognition in group settings for individuals with TH, the utilization of the Roger Select microphone in conjunction with bilateral Roger Focus receivers is recommended over the Roger Pen

Non-invasive ultrasonic sensing of internal conditions on a partial full-scale spent nuclear fuel canister mock-up

The safe storage of spent nuclear fuel (SNF) in dry cask storage systems (DCSSs) is critical to the nuclear fuel cycle and the future of nuclear energy. A critical component of DCSSs is the SNF canister. The canister is a sealed stainless-steel structure, which is first vacuum dried and then backfilled with helium. The structural deterioration within a canister can be monitored through its internal gas properties. This monitoring serves as the driving force behind the non-invasive ultrasonic sensing approach in this paper. A major challenge in collecting gas-borne signals using ultrasonic sensing is the impedance mismatch between the stainless-steel canister and the helium gas inside. Only a small fraction of the ultrasonic signal makes its way from the transmitter to the receiver through the gas medium. In this paper, experimental studies on a partial full-scale canister mock-up were carried out to capture the gas-borne signals. Damping materials were applied on the outside, and blocking and unblocking tests were conducted to identify the gas-borne signal. The research results showed that the excitation frequency played an important role in maximizing the gas-borne signals. The gas-borne signal was successfully detected at around the theoretical time-of-flight (TOF) at 225 kHz. A high signal-to-noise ratio (SNR) was achieved in the measurements. Next, acoustic impedance matching (AIM) layers were added, and it was found that the gas signal energy was improved by 160.4% compared with that of no AIM layers. Subsequently, the relative humidity (RH) level and temperature of the gas were varied to simulate abnormal internal conditions of the canister. The non-invasive testing system demonstrated reliability and sensitivity in detecting gas temperature and RH variations. Theoretical calculations demonstrated the potential for detecting low-level xenon and air within an actual SNF canister filled with helium. Last, an active noise cancellation (ANC) method, previously developed by the authors, was verified on the canister mock-up for the first time. The results showed that the SNR of the gas signal was improved by 213.6% compared with that of no ANC.

Glaucoma Rehabilitation Using ElectricAI Transcranial Stimulation (GREAT)-Optimizing Stimulation Protocol for Vision Enhancement Using an RCT.

We compared the effect of three different transcranial electrical stimulation (tES) protocols delivered to the occipital lobe on peripheral vision in patients with glaucoma. A double-masked, placebo-controlled study was conducted with 35 patients with glaucoma. We compared three different tES protocols: anodal transcranial direct current stimulation (a-tDCS), transcranial alternating current stimulation (tACS), and transcranial random noise stimulation (tRNS) against sham stimulation. Each patient attended four stimulation sessions (a-tDCS, tACS, tRNS, and sham) in a random order with at least 48hours between visits. Stimulation involved placing an anodal electrode over the occipital lobe (Oz) and cathodal electrode on the cheek for 20minutes. High-resolution perimetry (HRP) and multifocal visual evoked potential (mfVEP) measurements were made before and immediately after stimulation. Changes in HRP detection accuracy/reaction time and mfVEP signal-to-noise ratio (SNR)/latency were analyzed using linear mixed models. Compared to sham, HRP detection accuracy was significantly improved after a-tDCS in both the central 20-degree (b = 0.032, P = 0.018) and peripheral analysis (b = 0.051, P = 0.002). Additionally,mfVEP SNR was significantly increased (b = 0.016, P = 0.017) and the latency was shortened (b = -1.405, P = 0.04) by the a-tDCS in the central 20-degree analysis. In the peripheral analysis, there was a trend toward an enhancement of SNR after a-tDCS stimulation (b = 0.014, P = 0.052), but it did not reach statistical significance; latency was increased after tACS (b = 1.623, P = 0.041). No significant effects were found in comparison to other active tES protocols. A single session of a-tDCS enhances perceptual and electrophysiologic measures of vision in patients with glaucoma. However, the small magnitude of changes observed in HRP (3.2% for accuracy in central and 5.1% in peripheral) did not exceed previous test variability and may not be clinically meaningful. a-tDCS holds promise as a potential treatment for enhancing visual function. However, future studies are needed to evaluate the long-term effects and clinical relevance of this intervention using validated measures of perimetric changes in the visual field.

Recognition of car horns based on principal component analysis of MEL frequency Cepstral coefficients and support vector machine

With the rapid increase in the number of automobiles, the noise pollution caused by car horns is attracting more attention. In order to accurately identify car horn sounds in traffic noise and strengthen traffic law enforcement, research on car horn sound recognition is becoming increasingly important. In this paper, using Principal Component Analysis (PCA) to optimize and reduce the dimensionality of extracted Mel Frequency Cepstral Coefficients (MFCC), feature vectors are input into four models: Support Vector Machine (SVM), Back Propagation (BP) Neural Network, Extreme Learning Machine (ELM), and Random Forest (RF). These models are utilized to classify and identify car horn sounds from traffic noise. The impact of feature dimensions and Signal-to-noise ratio(SNR) on recognition performance is analyzed. The results show that under this method, the recognition rates of the four models can be increased by increasing the number of Mel filters and feature dimensions in MFCC, which can improve recognition performance, especially when the SNR is lower. The SVM model exhibits the most significant improvement in recognition rate. They achieve the best recognition rates for measured car horn sounds of 98.66 %, 93.98 %, 89.44 %, and 95.12 %, respectively. Experimental results demonstrate that the proposed method has good recognition performance and can be effectively applied to the supervision of car horn usage on actual roads.

Less is more: surface-lattice-resonance-enhanced aluminum metasurface with giant saturable absorption for a wavelength-tunable Q-switched Yb-doped fiber laser

Resonant metasurfaces provide a promising solution to overcome the limitations of nonlinear materials in nature by enhancing the interaction between light and matter and amplifying optical nonlinearity. In this paper, we design an aluminum (Al) metasurface that supports surface lattice resonance (SLR) with less nanoparticle filling density but more prominent saturable absorption effects, in comparison to a counterpart that supports localized surface plasmon resonance (LSPR). In detail, the SLR metasurface exhibits a narrower resonance linewidth and a greater near-field enhancement, leading to a more significant modulation depth (9.6%) at a low incident fluence of 25 μJ/cm2. As an application example, we have further achieved wavelength-tunable Q-switched pulse generation from 1020 to 1048 nm by incorporating the SLR-based Al metasurface as a passive saturable absorber (SA) in a polarization-maintaining ytterbium-doped fiber laser. Typically, the Q-switched pulse with a repetition rate of 33.7 kHz, pulse width of 2.1 μs, pulse energy of 141.7 nJ, and signal-to-noise ratio (SNR) of greater than 40 dB at the fundamental frequency can be obtained. In addition, we have investigated the effects of pump power and central wavelength of the filter on the repetition rate and pulse width of output pulses, respectively. In spite of demonstration of only using the Al metasurface to achieve a passive Q-switched fiber laser, our work offers an alternative scheme to build planar, lightweight, and broadband SA devices that could find emerging applications from ultrafast optics to neuromorphic photonics, considering the fast dynamics, CMOS-compatible fabrication, and decent nonlinear optical response of Al-material-based nanoplasmonics.

A Proposed Algorithm Based on Variance to Effectively Estimate Crack Source Localization in Solids.

Acoustic emissions (AEs) are produced by elastic waves generated by damage in solid materials. AE sensors have been widely used in several fields as a promising tool to analyze damage mechanisms such as cracking, dislocation movement, etc. However, accurately determining the location of damage in solids in a non-destructive manner is still challenging. In this paper, we propose a crack wave arrival time determination algorithm that can identify crack waves with low SNRs (signal-to-noise ratios) generated in rocks. The basic idea is that the variances in the crack wave and noise have different characteristics, depending on the size of the moving window. The results can be used to accurately determine the crack source location. The source location is determined by observing where the variance in the crack wave velocities of the true and imaginary crack location reach a minimum. By performing a pencil lead break test using rock samples, it was confirmed that the proposed method could successfully find wave arrival time and crack localization. The proposed algorithm for source localization can be used for evaluating and monitoring damage in tunnels or other underground facilities in real time.

Quantum ghost imaging microscopy depth-of-field study

Quantum ghost imaging approaches have been proposed to enhance biological microscopy, for example, using 2D visible detectors to provide IR images or providing additional dimensions of spatial or spectral information. Toward the goal of making such imaging schemes practical, we compare image quality and depth-of-field between traditional images and ghost images at the same excitation levels. We measure how image quality and depth-of-field depend on the parameters of the entangled light produced using type-I spontaneous parametric down-conversion (SPDC). We use a pair of time-synchronized, photon-timing single-photon avalanche diode (SPAD) array detectors to capture two distinct microscope imaging paths simultaneously on a photon-pair-by-photon-pair basis: one in a traditional imaging pathway and the other a quantum ghost imaging pathway. We calculate the depth-of-field, resolution, contrast, and signal-to-noise ratio (SNR) through the parameter space of a β-Barium Borate (BBO) type-I bulk non-linear crystal length and angle. Our results provide a basis for choosing parameters for quantum ghost imaging with type-I SPDC sources.

Radar shielding jamming method based on random phase modulation of digital coding metasurface

In this paper, a novel radar jamming method based on random phase modulation of digital coding metasurface was proposed. For typical radar system, the modulation model of coding metasurface and the echo model are deduced firstly. Then, the ordered statistics greatest of the constant false alarm rate (OSGO-CFAR) detection model was chosen to analyze the radar detection performance. Through serials of simulation, the influence of modulation modes, modulation switching interval, the number of coding elements and the signal-to-noise ratio (SNR) on radar detection performance is analyzed. Finally, the Monte Carlo simulation is performed. The result shows that as the modulation switching interval of metasurface decreases, the spectrum energy distribution of the target becomes more dispersed and the detection probability of the target decreases obviously. Traditional radar detectors will become ineffective, and the shielding of the target will be achieved. This implies a new radar jamming method which can realize target shielding instead of traditional stealth by reducing the radar cross section.

Improvement in Image Quality of Low-Dose CT of Canines with Generative Adversarial Network of Anti-Aliasing Generator and Multi-Scale Discriminator.

Computed tomography (CT) imaging is vital for diagnosing and monitoring diseases in both humans and animals, yet radiation exposure remains a significant concern, especially in animal imaging. Low-dose CT (LDCT) minimizes radiation exposure but often compromises image quality due to a reduced signal-to-noise ratio (SNR). Recent advancements in deep learning, particularly with CycleGAN, offer promising solutions for denoising LDCT images, though challenges in preserving anatomical detail and image sharpness persist. This study introduces a novel framework tailored for animal LDCT imaging, integrating deep learning techniques within the CycleGAN architecture. Key components include BlurPool for mitigating high-resolution image distortion, PixelShuffle for enhancing expressiveness, hierarchical feature synthesis (HFS) networks for feature retention, and spatial channel squeeze excitation (scSE) blocks for contrast reproduction. Additionally, a multi-scale discriminator enhances detail assessment, supporting effective adversarial learning. Rigorous experimentation on veterinary CT images demonstrates our framework's superiority over traditional denoising methods, achieving significant improvements in noise reduction, contrast enhancement, and anatomical structure preservation. Extensive evaluations show that our method achieves a precision of 0.93 and a recall of 0.94. This validates our approach's efficacy, highlighting its potential to enhance diagnostic accuracy in veterinary imaging. We confirm the scSE method's critical role in optimizing performance, and robustness to input variations underscores its practical utility.

Error performance analysis for OOK modulated optical camera communication systems

Integrating image sensors’ imaging capabilities into the receiver for visible light communication is a prominent characteristic of optical camera communication (OCC). However, the exposure effect during imaging distorts received waveforms and introduces inter-symbol interference (ISI), leading to decreased OCC reliability. This paper aims to provide an in-depth analysis of the impact of image sensor exposure effects on the error performance of OCC systems. Analytical expressions of the pixel signal-to-interference and noise ratio (PSINR) are derived using the pulse response function (PRF) for an on-off keying (OOK) modulated OCC system with varying exposure times. Furthermore, the bit error rate (BER) performance is evaluated analytically using PSINR, and a straightforward BER measurement scheme is proposed for experimental validation. Results from analyses and experiments conducted under different exposure times indicate that longer exposures lead to increased ISI and decreased PSINR, thereby increasing the error probability of data demodulation. Additionally, a combined impact of noise and exposure on OCC system reliability is observed, highlighting noise-limited and interference-limited characteristics under low and high signal-to-noise ratio (SNR) conditions, respectively. By utilizing PSINR as a bridge, this paper precisely analyzes OCC system reliability under exposure effects, laying a theoretical foundation for system design and optimization.

Effects of Stimulus Intensity and Sweep Count on the Click- and Chirp-Evoked Auditory Brainstem Response

Purpose: The auditory brainstem response (ABR) can be elicited by broadband stimuli such as the click and chirp. Differences in the click- and chirp-evoked ABR have been extensively described using subjective analyses. The aim of the current research is to determine if subjectively observed differences between the click- and chirp-evoked ABR are also represented in objective signal-to-noise ratio (SNR) measurements obtained from these responses at different stimulus intensities and sweep counts. Method: ABRs were collected using click and chirp stimuli on nine adults with normal hearing sensitivity. Three SNR methods (split-sweep average–based SNR [SSA-SNR], F ratio–based single-point [Fsp], and multipoint [Fmp]) were used for the objective analysis of the waveforms at four stimulus intensities (20, 40, 60, and 80 dB nHL) and five sweep counts (100, 200, 300, 400, and 500 sweeps). Results: SSA-SNR measurements were statistically stronger for chirp- as compared to click-evoked ABRs. Additionally, chirp/click SSA-SNR, Fsp, and Fmp ratio measurements were consistently greater than 1. The advantage associated with the chirp was most noticeable at moderate stimulus intensities for all three measurements. Discussion: The advantages for the chirp observed here could have implications for threshold estimation and screening ABR tests. Furthermore, when stimulus intensity and sweep count were varied, general response trends suggest that the three SNR-based methods performed similarly to each other, as well as to traditionally used subjective measures, suggesting that these techniques have the potential to be used effectively in clinic. Supplemental Material: https://doi.org/10.23641/asha.27011461

Low-dose high-resolution chest CT in adults with cystic fibrosis: intraindividual comparison between photon-counting and energy-integrating detector CT

BackgroundRegular disease monitoring with low-dose high-resolution (LD-HR) computed tomography (CT) scans is necessary for the clinical management of people with cystic fibrosis (pwCF). The aim of this study was to compare the image quality and radiation dose of LD-HR protocols between photon-counting CT (PCCT) and energy-integrating detector system CT (EID-CT) in pwCF.MethodsThis retrospective study included 23 pwCF undergoing LD-HR chest CT with PCCT who had previously undergone LD-HR chest CT with EID-CT. An intraindividual comparison of radiation dose and image quality was conducted. The study measured the dose-length product, volumetric CT dose index, effective dose and signal-to-noise ratio (SNR). Three blinded radiologists assessed the overall image quality, image sharpness, and image noise using a 5-point Likert scale ranging from 1 (deficient) to 5 (very good) for image quality and image sharpness and from 1 (very high) to 5 (very low) for image noise.ResultsPCCT used approximately 42% less radiation dose than EID-CT (median effective dose 0.54 versus 0.93 mSv, p < 0.001). PCCT was consistently rated higher than EID-CT for overall image quality and image sharpness. Additionally, image noise was lower with PCCT compared to EID-CT. The average SNR of the lung parenchyma was lower with PCCT compared to EID-CT (p < 0.001).ConclusionIn pwCF, LD-HR chest CT protocols using PCCT scans provided significantly better image quality and reduced radiation exposure compared to EID-CT.Relevance statementIn pwCF, regular follow-up could be performed through photon-counting CT instead of EID-CT, with substantial advantages in terms of both lower radiation exposure and increased image quality.Key PointsPhoton-counting CT (PCCT) and energy-integrating detector system CT (EID-CT) were compared in 23 people with cystic fibrosis (pwCF).Image quality was rated higher for PCCT than for EID-CT.PCCT used approximately 42% less radiation dose and offered superior image quality than EID-CT.Graphical

An MTCA-based LLRF prototype system of LINAC in Hefei Advanced light facility

The linear accelerator (LINAC) system currently under construction at the Hefei Advanced Light Facility (HALF) employs acceleration tubes to accelerate the electron beam to 2.2 GeV. The RF field within the acceleration tube is controlled by an MTCA.4-based low-level radio frequency (LLRF) prototype system to meet stringent phase stability requirements. This paper introduces the structure of the LLRF system, encompassing both its hardware and software components. The LLRF system uses the non-IQ-sampling method to suppress odd harmonics through digital filters, achieving a signal-to-noise ratio (SNR) of approximately 73 dB. Given that the power sources of most acceleration tubes operate in a saturated state, the radio-frequency (RF) control algorithm within the software is based on a separate amplitude open-loop and phase close-loop to maintain stable phase control during amplitude disturbances. The design of the RF control logic, including digital filters, the REF phase tracking method, the close-loop feedback controller, and the output pulse mode control algorithm, is detailed. Offline pulse-to-pulse test results demonstrate that it achieves a phase stability of 0.0176° rms with a solid-state amplifier, meeting the facility's requirements.

Power-aided probabilistic shaping parameter optimization for long-haul optical fiber transmission

A power-aided probabilistic shaping (PS) parameter optimization scheme is proposed to localize the optimal transmitting setup and maximize the transmission capacity in long-haul optical fiber transmission. Using a neural network and genetic algorithm (NNGA), the proposed scheme can select the optimal transmission parameter set, including the launching optical power (LOP) and three PS characteristics. We built a testbed using MATLAB and VPI to validate the proposed scheme. The proposed scheme was demonstrated in a dual-polarization coherent transmission system. The results show that the proposed scheme improves the generalized mutual information (GMI) by 0.3035 bits/symbol/pol and normalized GMI by 0.1022 in a 1000 km G.654E fiber transmission at 420-Gbit/s, compared to a traditional MB-based PS technique. The signal-to-noise ratio (SNR) achieves a gain of 1.003 dB, which validates the outperformance of the proposed scheme.

Multi-combination fault data augmentation method of aero-engine gas path based on Extraction TimeGAN

Aero-engine is the core power component of aircraft, and the accurate processing of engine monitoring data is the key to ensure its reliable operation and prevent faults. In the use and maintenance of aero-engines, the multi-combination fault data acquisition of gas path system is expensive and unavailable, thus intelligent multi-combination fault diagnosis under small samples scenarios poses a significant challenge. Aero-engine gas path combination fault data exhibit strong coupling, making it difficult for data augmentation methods to produce high-quality data. To solve the above problem, a novel aero-engine gas path combination fault data augmentation method based on Extraction TimeGAN is proposed. First, original data of multi-combination faults were obtained from Gasturb. To increase the complexity of generating data, Gaussian white noise of Signal-to-Noise Ratio (SNR) 20 was injected into the original data. Then, new samples with similar characteristics were generated from the original data according to different fault categories by TimeGAN, and the original data and generated data were both mapped to the latent space by Principal Component Analysis (PCA). Finally, the optimized extraction mechanism of generated data was established, and the high-density original data was identified and divided in the latent space through Kernel Density Estimation (KDE), the high-density regional hypersphere was constructed by Support Vector Data Description (SVDD), and the generated data located inside the hypersphere is extracted as the optimized generated data. In the multi-combination fault diagnosis experiment of aero-engine gas path, Area Under the Curve (AUC) of Support Vector Machine (SVM), Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) reaches 99.74%, 99.82% and 99.85%. Under high noise conditions, the AUC of Extraction TimeGAN only decreases 0.94%. Furthermore, Under the condition of small sample, Extraction TimeGAN maintains the AUC of over 99%.

Improving the Performance of Bidirectional Communication System Using Second-Order Raman Amplifiers

In order to achieve low-cost scalability, the same-wavelength bidirectional (SWB) fiber communication system is a better solution. We present a detailed investigation of the performance of the different orders Raman amplifiers in same-wavelength bidirectional fiber communication systems. We discuss how to suppress the main factor affecting system performance which is Rayleigh scattering noise (RSN). By using different Raman amplifiers to construct different quasi-lossless transmission, the performance changes in the same-wavelength bidirectional fiber optic communication system were studied. On this basis, multi-channel and same-wavelength single fiber bidirectional system experiments were conducted to compare the performance of second-order Raman systems and first-order Raman systems. The results indicate that the Rayleigh scattering suppression effect of second-order Raman systems is better, and compared to first-order Raman systems, the average signal-to-noise ratio (SNR) can be increased by 2.88 dB.

Accelerated 3D whole-heart non-contrast-enhanced mDIXON coronary MR angiography using deep learning-constrained compressed sensing reconstruction

ObjectivesTo investigate the feasibility of a deep learning-constrained compressed sensing (DL-CS) method in non-contrast-enhanced modified DIXON (mDIXON) coronary magnetic resonance angiography (MRA) and compare its diagnostic accuracy using coronary CT angiography (CCTA) as a reference standard.MethodsNinety-nine participants were prospectively recruited for this study. Thirty healthy subjects (age range: 20–65 years; 50% female) underwent three non-contrast mDIXON-based coronary MRA sequences including DL-CS, CS, and conventional sequences. The three groups were compared based on the scan time, subjective image quality score, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). The remaining 69 patients suspected of coronary artery disease (CAD) (age range: 39–83 years; 51% female) underwent the DL-CS coronary MRA and its diagnostic performance was compared with that of CCTA.ResultsThe scan time for the DL-CS and CS sequences was notably shorter than that of the conventional sequence (9.6 ± 3.1 min vs 10.0 ± 3.4 min vs 13.0 ± 4.9 min; p < 0.001). The DL-CS sequence obtained the highest image quality score, mean SNR, and CNR compared to CS and conventional methods (all p < 0.001). Compared to CCTA, the accuracy, sensitivity, and specificity of DL-CS mDIXON coronary MRA per patient were 84.1%, 92.0%, and 79.5%; those per vessel were 90.3%, 82.6%, and 92.5%; and those per segment were 98.0%, 85.1%, and 98.0%, respectively.ConclusionThe DL-CS mDIXON coronary MRA provided superior image quality and short scan time for visualizing coronary arteries in healthy individuals and demonstrated high diagnostic value compared to CCTA in CAD patients.Critical relevance statementDL-CS resulted in improved image quality with an acceptable scan time, and demonstrated excellent diagnostic performance compared to CCTA, which could be an alternative to enhance the workflow of coronary MRA.Key PointsCurrent coronary MRA techniques are limited by scan time and the need for noise reduction.DL-CS reduced the scan time in coronary MR angiography.Deep learning achieved the highest image quality among the three methods.Deep learning-based coronary MR angiography demonstrated high performance compared to CT angiography.Graphical

Joint optimization of spatial correlation for space-constrained MIMO underwater optical wireless communication system in weak turbulence

For the space-constrained underwater installations, spatial correlation is an essential factor affecting the reliability of the underwater optical wireless communication (UOWC) system with multiple-input multiple-output (MIMO) technology. In this paper, a joint optimization method for the layout and transmit power allocation of the light-emitting diode (LED)-based UOWC system is proposed and demonstrated under the weak turbulent channel. To analysis the transmission performance of the MIMO UOWC system, a composite channel model incorporating absorption, scattering and weak turbulence effects is established, and the channel spatial correlation characteristics with various system parameters is analyzed based on this model. Among these parameters, we take a 9 × 9 MIMO UOWC system as an example to simulate the communication performance of the optimization method under different turbulence intensities. The simulation results show the optimized system exhibits a channel capacity improvement of 13bps/Hz compared to the original system at the signal-to-noise ratio (SNR) of 20 dB and the same turbulence intensity. Moreover, the SNR of jointly optimized system can achieve a gain of 5 dB at the same bit error rate (BER). By extending the joint optimization algorithm to a 16 × 16 MIMO UOWC system, the optimized SNR can also obtain a gain of 7.5 dB which effectively demonstrates the universality of the proposed joint optimization algorithm.