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We present a study of the narrow Fe K α line in seven bright, nearby active galactic nuclei (AGN) that have been observed extensively with the Chandra High Energy Transmission Grating (HETG). The HETG data reveal a wider Fe K α line in the first-order spectrum than in the second- and third-order spectra, which we interpret as the result of spatially extended Fe K α emission. We utilize these differences in narrow Fe K α line widths in the multi-order Chandra HETG spectra to determine the spatial extent and intrinsic velocity width of the emitting material in each object. We find that there is modest evidence for spatially extended emission in each object, corresponding to extension of r ∼ 5â€"100 pc. These distances are significantly larger than those inferred from velocity widths assuming gravitational motions, which give r ∼ 0.01â€"1 pc. This implies either that the gas is emitting at a range of radii, with smaller radii dominating the velocity width and larger radii dominating the spatial extent, or that the gas is exhibiting nongravitational motions, which we suggest would be outflows due to slight excess redshift in the line and velocities that exceed the freefall velocity. We also use the spatial extent information to estimate the mass of the emitting gas by counting fluorescing iron atoms, finding masses on the order of M gas ∼ 105â€"108 M ⊙. Future work with observatories like XRISM will be able to extend this study to a larger number of AGN and decrease uncertainties that arise as a result of the low signal-to-noise ratio of the higher-order HETG data. [ABSTRACT FROM AUTHOR]

In this work, samarium vanadate (SmV) and single-walled carbon nanotubes (SWCNTs) were self-assembled by ultrasonic treatment to obtain a novel SmV/SWCNTs nanocomposite. Subsequently, an electrochemical sensor based on SmV/SWCNTs nanocomposite modified glassy carbon electrode (GCE) was constructed. SmV/SWCNTs/GCE exhibited high selectivity, excellent anti-interference, good stability and reproducibility toward paracetamol (PA) detection. The outstanding performances of the developed electrochemical sensor could be mainly attributed to its high electrocatalytic activity for PA, which might be caused by the synergistic effect of SmV and SWCNTs. Under optimized experimental conditions, SmV/SWCNTs/GCE exhibited a good linear relationship between current signal and PA concentration over a wide concentration range 1–100 μM, and limit of detection (LOD) (signal-to-noise ratio of 3, S/N = 3) was calculated to be 0.035 μM. The satisfactory recoveries of 98.06%–104.20% and 98.55%–101.88% could be obtained in the bovine serum and PA tablet samples, respectively. We anticipated this work could provide a reference for the future development and application of samarium-based materials in the field of electroanalysis. [ABSTRACT FROM AUTHOR]

Objective. Cone-Beam CT (CBCT) often results in severe image artifacts and inaccurate HU values, meaning poor quality CBCT images cannot be directly applied to dose calculation in radiotherapy. To overcome this, we propose a cycle-residual connection with a dilated convolution-consistent generative adversarial network (Cycle-RCDC-GAN). Approach. The cycle-consistent generative adversarial network (Cycle-GAN) was modified using a dilated convolution with different expansion rates to extract richer semantic features from input images. Thirty pelvic patients were used to investigate the effect of synthetic CT (sCT) from CBCT, and 55 head and neck patients were used to explore the generalizability of the model. Three generalizability experiments were performed and compared: the pelvis trained model was applied to the head and neck; the head and neck trained model was applied to the pelvis, and the two datasets were trained together. Main results. The mean absolute error (MAE), the root mean square error (RMSE), peak signal to noise ratio (PSNR), the structural similarity index (SSIM), and spatial nonuniformity (SNU) assessed the quality of the sCT generated from CBCT. Compared with CBCT images, the MAE improved from 28.81 to 18.48, RMSE from 85.66 to 69.50, SNU from 0.34 to 0.30, and PSNR from 31.61 to 33.07, while SSIM improved from 0.981 to 0.989. The sCT objective indicators of Cycle-RCDC-GAN were better than Cycle-GAN’s. The objective metrics for generalizability were also better than Cycle-GAN’s. Significance. Cycle-RCDC-GAN enhances CBCT image quality and has better generalizability than Cycle-GAN, which further promotes the application of CBCT in radiotherapy. [ABSTRACT FROM AUTHOR]

The Vera Rubin Observatory Legacy Survey of Space and Time (LSST) is expected to process ∼106 transient detections per night. For precision measurements of cosmological parameters and rates, it is critical to understand the detection efficiency, magnitude limits, artifact contamination levels, and biases in the selection and photometry. Here we rigorously test the LSST Difference Image Analysis (DIA) pipeline using simulated images from the Rubin Observatory LSST Dark Energy Science Collaboration Data Challenge (DC2) simulation for the Wide-Fast-Deep survey area. DC2 is the first large-scale (300 deg2) image simulation of a transient survey that includes realistic cadence, variable observing conditions, and CCD image artifacts. We analyze ∼15 deg2 of DC2 over a 5 yr time span in which artificial point sources from Type Ia supernova (SNIa) light curves have been overlaid onto the images. The magnitude limits per filter are u = 23.66 mag, g = 24.69 mag, r = 24.06 mag, i = 23.45 mag, z = 22.54 mag, and y = 21.62 mag. The artifact contamination levels are ∼90% of all detections, corresponding to ∼1000 artifacts degâ€"2 in g band, and falling to 300 degâ€"2 in y band. The photometry has biases <1% for magnitudes 19.5 < m < 23. Our DIA performance on simulated images is similar to that of the Dark Energy Survey difference-imaging pipeline on real images. We also characterize DC2 image properties to produce catalog-level simulations needed for distance bias corrections. We find good agreement between DC2 data and simulations for distributions of signal-to-noise ratio, redshift, and fitted light-curve properties. Applying a realistic SNIa cosmology analysis for redshifts z < 1, we recover the input cosmology parameters to within statistical uncertainties. [ABSTRACT FROM AUTHOR]

This paper is the second in a series aimed at examining the gaseous environments of z ≤ 0.3 quasars and ultraluminous infrared galaxies (ULIRGs) as a function of AGN/host galaxy properties across the merger sequence. This second paper focuses on the Ly α emission and O vi 1032, 1038 and N v 1238, 1243 absorption features, tracers of highly ionized gas outflows, in ULIRGs observed with HST/COS. Ly α emission is detected in 15 out of 19 ULIRGs, and 12 of the 14 clear Ly α detections show emission with blueshifted velocity centroids and/or wings. The equivalent widths of the Ly α emission increase with increasing AGN luminosities and AGN bolometric fractions. The blueshifts of the Ly α emission correlate positively with those of the [O iii ] λ 5007 emission, where the latter traces the ionized gas outflows. The Ly α escape fractions tend to be slightly larger in objects with stronger AGN and larger outflow velocities, but they do not correlate with nebular line reddening. Among the 12 ULIRGs with good continuum signal-to-noise ratios, O vi and/or N v absorption features are robustly detected in six of them, all of which are blueshifted, indicative of outflows. In the combined ULIRG + quasar sample, the outflows are more frequently detected in the X-ray weak or absorbed sources. The absorption equivalent widths, velocities, and velocity dispersions of the outflows are also higher in the X-ray weak sources. No other strong correlations are visible between the properties of the outflows and those of the AGN or host galaxies. [ABSTRACT FROM AUTHOR]

The CO Mapping Array Project (COMAP) aims to use line-intensity mapping of carbon monoxide (CO) to trace the distribution and global properties of galaxies over cosmic time, back to the Epoch of Reionization (EoR). To validate the technologies and techniques needed for this goal, a Pathfinder instrument has been constructed and fielded. Sensitive to CO(1â€"0) emission from z = 2.4â€"3.4 and a fainter contribution from CO(2â€"1) at z = 6â€"8, the Pathfinder is surveying 12 deg2 in a 5 yr observing campaign to detect the CO signal from z ∼ 3. Using data from the first 13 months of observing, we estimate P CO(k) = âˆ'2.7 ± 1.7 Ă— 104 ÎĽ K2 Mpc3 on scales k = 0.051 âˆ'0.62 Mpcâˆ'1, the first direct three-dimensional constraint on the clustering component of the CO(1â€"0) power spectrum. Based on these observations alone, we obtain a constraint on the amplitude of the clustering component (the squared mean CO line temperature bias product) of Tb 2 < 49 ÎĽ K2, nearly an order-of-magnitude improvement on the previous best measurement. These constraints allow us to rule out two models from the literature. We forecast a detection of the power spectrum after 5 yr with signal-to-noise ratio (S/N) 9â€"17. Cross-correlation with an overlapping galaxy survey will yield a detection of the COâ€"galaxy power spectrum with S/N of 19. We are also conducting a 30 GHz survey of the Galactic plane and present a preliminary map. Looking to the future of COMAP, we examine the prospects for future phases of the experiment to detect and characterize the CO signal from the EoR. [ABSTRACT FROM AUTHOR]

We introduce COMAP- EoR, the next generation of the Carbon Monoxide Mapping Array Project aimed at extending CO intensity mapping to the Epoch of Reionization. COMAP- EoR supplements the existing 30 GHz COMAP Pathfinder with two additional 30 GHz instruments and a new 16 GHz receiver. This combination of frequencies will be able to simultaneously map CO(1â€"0) and CO(2â€"1) at reionization redshifts (z ∼ 5â€"8) in addition to providing a significant boost to the z ∼ 3 sensitivity of the Pathfinder. We examine a set of existing models of the EoR CO signal, and find power spectra spanning several orders of magnitude, highlighting our extreme ignorance about this period of cosmic history and the value of the COMAP- EoR measurement. We carry out the most detailed forecast to date of an intensity mapping cross correlation, and find that five out of the six models we consider yield signal to noise ratios (S/Ns) ≳ 20 for COMAP- EoR, with the brightest reaching a S/N above 400. We show that, for these models, COMAP- EoR can make a detailed measurement of the cosmic molecular gas history from z ∼ 2â€"8, as well as probe the population of faint, star-forming galaxies predicted by these models to be undetectable by traditional surveys. We show that, for the single model that does not predict numerous faint emitters, a COMAP- EoR -type measurement is required to rule out their existence. We briefly explore prospects for a third-generation Expanded Reionization Array (COMAP- ERA) capable of detecting the faintest models and characterizing the brightest signals in extreme detail. [ABSTRACT FROM AUTHOR]

We analyze spatially resolved and co-added SDSS-IV MaNGA spectra with signal-to-noise ratio ∼100 from 2200 passive central galaxies (z ∼ 0.05) to understand how central galaxy assembly depends on stellar mass (M *) and halo mass (M h ). We control for systematic errors in M h by employing a new group catalog from Tinker and the widely used Yang et al. catalog. At fixed M *, the strengths of several stellar absorption features vary systematically with M h . Completely model-free, this is one of the first indications that the stellar populations of centrals with identical M * are affected by the properties of their host halos. To interpret these variations, we applied full spectral fitting with the code alf. At fixed M *, centrals in more massive halos are older, show lower [Fe/H], and have higher [Mg/Fe] with 3.5 σ confidence. We conclude that halos not only dictate how much M * galaxies assemble but also modulate their chemical enrichment histories. Turning to our analysis at fixed M h , high- M * centrals are older, show lower [Fe/H], and have higher [Mg/Fe] for M h > 1012 h âˆ'1 M ⊙ with confidence >4 σ. While massive passive galaxies are thought to form early and rapidly, our results are among the first to distinguish these trends at fixed M h . They suggest that high- M * centrals experienced unique early formation histories, either through enhanced collapse and gas fueling or because their halos were early forming and highly concentrated, a possible signal of galaxy assembly bias. [ABSTRACT FROM AUTHOR]

The Airborne Infrared Spectrometer (AIR-Spec) was commissioned during the 2017 total solar eclipse, when it observed five infrared coronal emission lines from a Gulfstream V research jet owned by the National Science Foundation and operated by the National Center for Atmospheric Research. The second AIR-Spec research flight took place during the 2019 July 2 total solar eclipse across the south Pacific. The 2019 eclipse flight resulted in seven minutes of observations, during which the instrument measured all four of its target emission lines: S xi 1.393 μ m, Si x 1.431 μ m, S xi 1.921 μ m, and Fe ix 2.853 μ m. The 1.393 μ m S xi line was detected for the first time, and probable first detections were made of Si xi 1.934 μ m and Fe x 1.947 μ m. The 2017 AIR-Spec detection of Fe ix was confirmed and the first observations were made of the Fe ix line intensity as a function of solar radius. Telluric absorption features were used to calibrate the wavelength mapping, instrumental broadening, and throughput of the instrument. AIR-Spec underwent significant upgrades in preparation for the 2019 eclipse observation. The thermal background was reduced by a factor of 30, providing a 5.5× improvement in signal-to-noise ratio, and the postprocessed pointing stability was improved by a factor of 5 to <10″ rms. In addition, two imaging artifacts were identified and resolved, improving the spectral resolution and making the 2019 data easier to interpret. [ABSTRACT FROM AUTHOR]

We evaluate the cosmological coalescence and detection rates for massive black hole (MBH) binaries targeted by the gravitational wave observatory Laser Interferometer Space Antenna (LISA). Our calculation starts with a population of gravitationally unbound MBH pairs, drawn from the TNG50-3 cosmological simulation, and follows their orbital evolution from kiloparsec scales all the way to coalescence using a semi-analytic model developed in our previous work. We find that for the majority of MBH pairs that coalesce within a Hubble time dynamical friction is the most important mechanism that determines their coalescence rate. Our model predicts an MBH coalescence rate ≲0.45 yrâˆ'1 and a LISA detection rate ≲0.34 yrâˆ'1. Most LISA detections should originate from 106 to 106.8 M ⊙ MBHs in gas-rich galaxies at redshifts 1.6 ≤ z ≤ 2.4 and have a characteristic signal-to-noise ratio S/N ∼100. We however find a dramatic reduction in the coalescence and detection rates, as well as the average S/N, if the effects of radiative feedback from accreting MBHs are taken into account. In this case, the MBH coalescence rate is reduced by 78% (to ≲0.1 yrâˆ'1), and the LISA detection rate is reduced by 94% (to 0.02 yrâˆ'1), whereas the average S/N is ∼10. We emphasize that our model provides a conservative estimate of the LISA detection rates, due to the limited MBH mass range in TNG50-3, consistent with other works in the literature that draw their MBH pairs from cosmological simulations. [ABSTRACT FROM AUTHOR]

Objective. Photon-counting x-ray detectors (PCDs) can produce dual-energy (DE) x-ray images of lung cancer in a single x-ray exposure. It is important to understand the factors that affect contrast, noise and the contrast-to-noise ratio (CNR). This study quantifies the dependence of CNR on tube voltage, energy threshold and patient thickness in single exposure, DE, bone-suppressed thoracic imaging with PCDs, and elucidates how the fundamental processes inherent in x-ray detection by PCDs contribute to CNR degradation. Approach. We modeled the DE CNR for five theoretical PCDs, ranging from an ideal PCD that detects every primary photon in the correct energy bin while rejecting all scattered radiation to a non-ideal PCD that suffers from charge-sharing and electronic noise, and detects scatter. CNR was computed as a function of tube voltage and high energy threshold for average and larger-than-average patients. Model predictions were compared with experimental data extracted from images acquired using a cadmium telluride (CdTe) PCD with two energy bins and analog charge summing for charge-sharing suppression. The imaging phantom simulated attenuation, scatter and contrast in lung nodule imaging. We quantified CNR improvements achievable with anti-correlated noise reduction (ACNR) and measured the range of exposure rates over which pulse pile-up is negligible. Main Results. The realistic model predicted overall trends observed in the experimental data. CNR improvements with ACNR were approximately five-fold, and modeled CNR-enhancements were on average within 10% of experiment. CNR increased modestly (i.e.<20%) when increasing the tube voltage from 90 kV to 130 kV. Optimal energy thresholds ranged from 50 keV to 70 keV across all tube voltages and patient thicknesses with and without ACNR. Quantum efficiency, electronic noise, charge sharing and scatter degraded CNR by ~50%. Charge sharing and scatter had the largest effect on CNR, degrading it by ~30% and ~15% respectively. Dead-time losses were less than 5% for patient exposure rates within the range of clinical exposure rates. Significance. In this study, we (1) employed analytical and computational models to assess the impact of different factors on CNR in single-exposure DE imaging with PCDs, (2) evaluated the accuracy of these models in predicting experimental trends, (3) quantified improvements in CNR achievable through ACNR and (4) determined the range of patient exposure rates at which pulse pile-up can be considered negligible. To the best of our knowledge, this study represents the first systematic investigation of single-exposure DE imaging of lung nodules with PCDs., (Creative Commons Attribution license.)

Objective. Respiratory motion, cardiac motion and inherently low signal-to-noise ratio (SNR) are major limitations of in vivo cardiac diffusion tensor imaging (DTI). We propose a novel enhancement method that uses unsupervised learning based invertible wavelet scattering (IWS) to improve the quality of in vivo cardiac DTI. Approach. Our method starts by extracting nearly transformation-invariant features from multiple cardiac diffusion-weighted (DW) image acquisitions using multi-scale wavelet scattering (WS). Then, the relationship between the WS coefficients and DW images is learned through a multi-scale encoder and a decoder network. Using the trained encoder, the deep features of WS coefficients of multiple DW image acquisitions are further extracted and then fused using an average rule. Finally, using the fused WS features and trained decoder, the enhanced DW images are derived. Main result. We evaluate the performance of the proposed method by comparing it with several methods on three in vivo cardiac DTI datasets in terms of SNR, contrast to noise ratio (CNR), fractional anisotropy (FA), mean diffusivity (MD) and helix angle (HA). Comparing against the best comparison method, SNR/CNR of diastolic, gastric peristalsis influenced, and end-systolic DW images were improved by 1%/16%, 5%/6%, and 56%/30%, respectively. The approach also yielded consistent FA and MD values and more coherent helical fiber structures than the comparison methods used in this work. Significance. The ablation results verify that using the transformation-invariant and noise-robust wavelet scattering features enables us to effectively explore the useful information from the limited data, providing a potential mean to alleviate the dependence of the fusion results on the number of repeated acquisitions, which is beneficial for dealing with the issues of noise and residual motion simultaneously and therefore improving the quality ofinvivocardiac DTI. Code can be found inhttps://github.com/strawberry1996/WS-MCNN., (© 2024 Institute of Physics and Engineering in Medicine. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

Objective. Multi-energy CT conducted by photon-counting detector has a wide range of applications, especially in multiple contrast agents imaging. However, static multi-energy (SME) CT imaging suffers from higher statistical noise because of increased energy bins with static energy thresholds. Our team has proposed a dynamic dual-energy (DDE) CT detector model and the corresponding iterative reconstruction algorithm to solve this problem. However, rigorous and detailed analysis of the statistical noise characterization in this DDE CT was lacked. Approach. Starting from the properties of the Poisson random variable, this paper analyzes the noise characterization of the DDE CT and compares it with the SME CT. It is proved that the multi-energy CT projections and reconstruction images calculated from the proposed DDE CT algorithm have less statistical noise than that of the SME CT. Main results. Simulations and experiments verify that the expectations of the multi-energy CT projections calculated from DDE CT are the same as those of the SME projections. Still, the variance of the former is smaller. We further analyze the convergence of the iterative DDE CT algorithm through simulations and prove that the derived noise characterization can be realized under different CT imaging configurations. Significance. The low statistical noise characteristics demonstrate the value of DDE CT imaging technology., (© 2024 Institute of Physics and Engineering in Medicine. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

Objective. Optical coherence tomography (OCT) is widely used in clinical practice for its non-invasive, high-resolution imaging capabilities. However, speckle noise inherent to its low coherence principle can degrade image quality and compromise diagnostic accuracy. While deep learning methods have shown promise in reducing speckle noise, obtaining well-registered image pairs remains challenging, leading to the development of unpaired methods. Despite their potential, existing unpaired methods suffer from redundancy in network structures or interaction mechanisms. Therefore, a more streamlined method for unpaired OCT denoising is essential. Approach. In this work, we propose a novel unpaired method for OCT image denoising, referred to as noise-imitation learning (NIL). NIL comprises three primary modules: the noise extraction module, which extracts noise features by denoising noisy images; the noise imitation module, which synthesizes noisy images and generates fake clean images; and the adversarial learning module, which differentiates between real and fake clean images through adversarial training. The complexity of NIL is significantly lower than that of previous unpaired methods, utilizing only one generator and one discriminator for training. Main results. By efficiently fusing unpaired images and employing adversarial training, NIL can extract more speckle noise information to enhance denoising performance. Building on NIL, we propose an OCT image denoising pipeline, NIL-NAFNet. This pipeline achieved PSNR, SSIM, and RMSE values of 31.27 dB, 0.865, and 7.00, respectively, on the PKU37 dataset. Extensive experiments suggest that our method outperforms state-of-the-art unpaired methods both qualitatively and quantitatively. Significance. These findings indicate that the proposed NIL is a simple yet effective method for unpaired OCT speckle noise reduction. The OCT denoising pipeline based on NIL demonstrates exceptional performance and efficiency. By addressing speckle noise without requiring well-registered image pairs, this method can enhance image quality and diagnostic accuracy in clinical practice., (© 2024 Institute of Physics and Engineering in Medicine. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

Objective. Magnetic particle imaging (MPI) is an emerging tracer-based in vivo imaging technology. The use of MPI at low superparamagnetic iron oxide nanoparticle concentrations has the potential to be a promising area of clinical application due to the inherent safety for humans. However, low tracer concentrations reduce the signal-to-noise ratio of the magnetization signal, leading to severe noise artifacts in the reconstructed MPI images. Hardware improvements have high complexity, while traditional methods lack robustness to different noise levels, making it difficult to improve the quality of low concentration MPI images. Approach. Here, we propose a novel deep learning method for MPI image denoising and quality enhancing based on a sparse lightweight transformer model. The proposed residual-local transformer structure reduces model complexity to avoid overfitting, in which an information retention block facilitates feature extraction capabilities for the image details. Besides, we design a noisy concentration dataset to train our model. Then, we evaluate our method with both simulated and real MPI image data. Main results. Simulation experiment results show that our method can achieve the best performance compared with the existing deep learning methods for MPI image denoising. More importantly, our method is effectively performed on the real MPI image of samples with an Fe concentration down to 67 μ g Fe ml -1 . Significance. Our method provides great potential for obtaining high quality MPI images at low concentrations., (© 2024 Institute of Physics and Engineering in Medicine. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

Objective. Deep learning has markedly enhanced the performance of sparse-view computed tomography reconstruction. However, the dependence of these methods on supervised training using high-quality paired datasets, and the necessity for retraining under varied physical acquisition conditions, constrain their generalizability across new imaging contexts and settings. Approach. To overcome these limitations, we propose an unsupervised approach grounded in the deep image prior framework. Our approach advances beyond the conventional single noise level input by incorporating multi-level linear diffusion noise, significantly mitigating the risk of overfitting. Furthermore, we embed non-local self-similarity as a deep implicit prior within a self-attention network structure, improving the model's capability to identify and utilize repetitive patterns throughout the image. Additionally, leveraging imaging physics, gradient backpropagation is performed between the image domain and projection data space to optimize network weights. Main Results. Evaluations with both simulated and clinical cases demonstrate our method's effective zero-shot adaptability across various projection views, highlighting its robustness and flexibility. Additionally, our approach effectively eliminates noise and streak artifacts while significantly restoring intricate image details. Significance . Our method aims to overcome the limitations in current supervised deep learning-based sparse-view CT reconstruction, offering improved generalizability and adaptability without the need for extensive paired training data., (Creative Commons Attribution license.)

Objective . Low-count positron emission tomography (PET) imaging is an efficient way to promote more widespread use of PET because of its short scan time and low injected activity. However, this often leads to low-quality PET images with clinical image reconstruction, due to high noise and blurring effects. Existing PET image restoration (IR) methods hinder their own restoration performance due to the semi-convergence property and the lack of suitable denoiser prior. Approach . To overcome these limitations, we propose a novel deep plug-and-play IR method called Deep denoiser Prior driven Relaxed Iterated Tikhonov method (DP-RI-Tikhonov). Specifically, we train a deep convolutional neural network denoiser to generate a flexible deep denoiser prior to handle high noise. Then, we plug the deep denoiser prior as a modular part into a novel iterative optimization algorithm to handle blurring effects and propose an adaptive parameter selection strategy for the iterative optimization algorithm. Main results . Simulation results show that the deep denoiser prior plays the role of reducing noise intensity, while the novel iterative optimization algorithm and adaptive parameter selection strategy can effectively eliminate the semi-convergence property. They enable DP-RI-Tikhonov to achieve an average quantitative result (normalized root mean square error, structural similarity) of (0.1364, 0.9574) at the stopping iteration, outperforming a conventional PET IR method with an average quantitative result of (0.1533, 0.9523) and a state-of-the-art deep plug-and-play IR method with an average quantitative result of (0.1404, 0.9554). Moreover, the advantage of DP-RI-Tikhonov becomes more obvious at the last iteration. Experiments on six clinical whole-body PET images further indicate that DP-RI-Tikhonov successfully reduces noise intensity and recovers fine details, recovering sharper and more uniform images than the comparison methods. Significance . DP-RI-Tikhonov's ability to reduce noise intensity and effectively eliminate the semi-convergence property overcomes the limitations of existing methods. This advancement may have substantial implications for other medical IR., (© 2024 Institute of Physics and Engineering in Medicine.)

Objective. The study aimed to generate synthetic contrast-enhanced Dual-energy CT (CE-DECT) images from non-contrast single-energy CT (SECT) scans, addressing the limitations posed by the scarcity of DECT scanners and the health risks associated with iodinated contrast agents, particularly for high-risk patients. Approach. A conditional denoising diffusion probabilistic model (C-DDPM) was utilized to create synthetic images. Imaging data were collected from 130 head-and-neck (HN) cancer patients who had undergone both non-contrast SECT and CE-DECT scans. Main Results. The performance of the C-DDPM was evaluated using Mean Absolute Error (MAE), Structural Similarity Index (SSIM), and Peak Signal-to-Noise Ratio (PSNR). The results showed MAE values of 27.37±3.35 Hounsfield Units (HU) for high-energy CT (H-CT) and 24.57±3.35HU for low-energy CT (L-CT), SSIM values of 0.74±0.22 for H-CT and 0.78±0.22 for L-CT, and PSNR values of 18.51±4.55 decibels (dB) for H-CT and 18.91±4.55 dB for L-CT. Significance. The study demonstrates the efficacy of the deep learning model in producing high-quality synthetic CE-DECT images, which significantly benefits radiation therapy planning. This approach provides a valuable alternative imaging solution for facilities lacking DECT scanners and for patients who are unsuitable for iodine contrast imaging, thereby enhancing the reach and effectiveness of advanced imaging in cancer treatment planning., (Creative Commons Attribution license.)

Objective . Positron Emission Tomography and Magnetic Resonance Imaging (PET-MRI) systems can obtain functional and anatomical scans. But PET suffers from a low signal-to-noise ratio, while MRI are time-consuming. To address time-consuming, an effective strategy involves reducing k-space data collection, albeit at the cost of lowering image quality. This study aims to leverage the inherent complementarity within PET-MRI data to enhance the image quality of PET-MRI. Approach . A novel PET-MRI joint reconstruction model, termed MC-Diffusion, is proposed in the Bayesian framework. The joint reconstruction problem is transformed into a joint regularization problem, where data fidelity terms of PET and MRI are expressed independently. The regular term, the derivative of the logarithm of the joint probability distribution of PET and MRI, employs a joint score-based diffusion model for learning. The diffusion model involves the forward diffusion process and the reverse diffusion process. The forward diffusion process adds noise to transform a complex joint data distribution into a known joint prior distribution for PET and MRI simultaneously, resembling a denoiser. The reverse diffusion process removes noise using a denoiser to revert the joint prior distribution to the original joint data distribution, effectively utilizing joint probability distribution to describe the correlations of PET and MRI for improved quality of joint reconstruction. Main results . Qualitative and quantitative improvements are observed with the MC-Diffusion model. Comparative analysis against LPLS and Joint ISAT-net on the ADNI dataset demonstrates superior performance by exploiting complementary information between PET and MRI. The MC-Diffusion model effectively enhances the quality of PET and MRI images. Significance . This study employs the MC-Diffusion model to enhance the quality of PET-MRI images by integrating the fundamental principles of PET and MRI modalities and leveraging their inherent complementarity. Furthermore, utilizing the diffusion model to learn the joint probability distribution of PET and MRI, thereby elucidating their latent correlation, facilitates a more profound comprehension of the priors obtained through deep learning, contrasting with black-box prior or artificially constructed structural similarities., (© 2024 Institute of Physics and Engineering in Medicine.)

Objective. We propose a nonparametric figure of merit, the contrast equivalent distance CED, to measure contrast directly from clinical images. Approach. A relative brightness distance δ is calculated by making use of the order statistic of the pixel values. By multiplying δ with the grey value range R , the mean brightness distance MBD is obtained. From the MBD, the CED and the distance-to-noise ratio DNR can be derived. The latter is the ratio of the MBD and a previously suggested nonparametric measure τ for the noise. Since the order statistic is independent of the spatial arrangement of the pixel values, the measures can be obtained directly from clinical images. We apply the new measures to mammography images of an anthropomorphic phantom and of a phantom with a step wedge as well as to CT images of a head phantom. Main results. For low-noise images of a step wedge, the MBD is equivalent to the conventional grey value distance. While this measure permits the evaluation of clinical images, it is sensitive to noise. Therefore, noise has to be quantified at the same time. When the ratioσ/τof the noise standard deviation σ to τ is available, validity limits for the CED as a measure of contrast can be established. The new figures of merit can be calculated for entire images as well as on regions of interest (ROI) with an edge length not smaller than 32 px. Significance. The new figures of merit are suited to quantify the quality of clinical images without relying on the assumption of a linear, shift-invariant system. They can be used for any kind of greyscale image, provided the ratioσ/τcan be estimated. This will hopefully help to achieve the optimisation of image quality vs dose required by radioprotection laws., (Creative Commons Attribution license.)

Objective. Sparse-view dual-energy spectral computed tomography (DECT) imaging is a challenging inverse problem. Due to the incompleteness of the collected data, the presence of streak artifacts can result in the degradation of reconstructed spectral images. The subsequent material decomposition task in DECT can further lead to the amplification of artifacts and noise. Approach. To address this problem, we propose a novel one-step inverse generation network (OIGN) for sparse-view dual-energy CT imaging, which can achieve simultaneous imaging of spectral images and materials. The entire OIGN consists of five sub-networks that form four modules, including the pre-reconstruction module, the pre-decomposition module, and the following residual filtering module and residual decomposition module. The residual feedback mechanism is introduced to synchronize the optimization of spectral CT images and materials. Main results. Numerical simulation experiments show that the OIGN has better performance on both reconstruction and material decomposition than other state-of-the-art spectral CT imaging algorithms. OIGN also demonstrates high imaging efficiency by completing two high-quality imaging tasks in just 50 seconds. Additionally, anti-noise testing is conducted to evaluate the robustness of OIGN. Significance. These findings have great potential in high-quality multi-task spectral CT imaging in clinical diagnosis., (© 2024 Institute of Physics and Engineering in Medicine.)

Objective. In helical tomotherapy, image-guided radiotherapy employs megavoltage computed tomography (MVCT) for precise targeting. However, the high voltage of megavoltage radiation introduces substantial noise, significantly compromising MVCT image clarity. This study aims to enhance MVCT image quality using a deep learning-based denoising method. Approach. We propose an unpaired MVCT denoising network using a coupled generative adversarial network framework (DeCoGAN). Our approach assumes that a universal latent code within a shared latent space can reconstruct any given pair of images. By employing an encoder, we enforce this shared-latent space constraint, facilitating the conversion of low-quality (noisy) MVCT images into high-quality (denoised) counterparts. The network learns the joint distribution of images from both domains by leveraging samples from their respective marginal distributions, enhanced by adversarial training for effective denoising. Main Results. Compared to an analytical algorithm (BM3D) and three deep learning-based methods (RED-CNN, WGAN-VGG and CycleGAN), the proposed method excels in preserving image details and enhancing human visual perception by removing most noise and retaining structural features. Quantitative analysis demonstrates that our method achieves the highest peak signal-to-noise ratio and Structural Similarity Index Measurement values, indicating superior denoising performance. Significance. The proposed DeCoGAN method shows remarkable MVCT denoising performance, making it a promising tool in the field of radiation therapy., (© 2024 Institute of Physics and Engineering in Medicine.)

Objective. The use of uniform phantoms to assess the influence of x-ray scatter and antiscatter grids on x-ray angiography and fluoroscopy image quality disregards the influence of spatially variable x-ray attenuation of patients. The purpose of this work was to measure scatter to primary ratio (SPR) and antiscatter grid SNR improvement factor ( K SNR ) using experimental conditions which better mimic patient imaging conditions. Approach. Three adult-sized anthropomorphic phantoms were used. AP and lateral projection images of the thorax and abdomen were acquired with and without an antiscatter grid. Grids with ratio 15:1 and 29:1 (r15, r29) and x-ray fields of view 20, 25 (thorax) and 32, 42 cm (abdomen) were tested. Combined with a-priori measurements of grid scatter and primary transmission fractions, these images were used to calculate 2D SPR and K SNR maps. Main results. Results demonstrated that measurements by uniform phantom do not describe the complex 2D SPR and K SNR distributions associated with anthropomorphic phantoms. The regions of the images with the lowest primary x-ray intensity (greatest attenuation) had the highest SPR and the highest K SNR attributable to the grids. Considering all conditions, the 95th percentile of the SPR maps was in the range 42%-185% greater than the median values and that of the K SNR maps was 4%-20% higher than the median values. The combined influences of SID 120 vs. 107 cm and r29 vs. r15 grid resulted in K SNR in the range 1.05-1.49. Significance. Performance of anti-scatter grids using anatomically complex phantoms highlights the substantial variation of SPR and K SNR within 2D images. Also, this work demonstrates the benefit of the prototype r29 grid for thoracic and abdominal angiography imaging conditions is substantial, especially for large patients and radiodense image regions., (© 2024 Institute of Physics and Engineering in Medicine.)

Objective. With the introduction of spectral CT techniques into the clinic, the imaging capacities of CT were expanded to multiple energy levels. Due to a variety of factors, the acquired signal in spectral CT datasets is shared between these images. Conventional image quality metrics assume independence between images which is not preserved within spectral CT datasets, limiting their utility for characterizing energy selective images. The purpose of this work was to develop a metrology to characterize energy selective images by incorporating the shared information between images within a spectral CT dataset. Approach. The signal-to-noise ratio (SNR) was extended into a multivariate space where each image within a spectral CT dataset was treated as a separate information channel. The general definition was applied to the specific case of contrast to define a multivariate contrast-to-noise ratio (CNR). The matrix contained two types of terms: a conventional CNR term which characterized image quality within each image in the spectral CT dataset and covariance weighted CNR (Covar-CNR) which characterized the contrast in each image relative to the covariance between images. Experimental data from an investigational photon-counting CT scanner was used to demonstrate the insight of this metrology. A cylindrical water phantom containing vials of iodine and gadolinium (2, 4, and 8 mg ml -1 ) was imaged under conditions of variable tube current, tube voltage, and energy threshold. Two image series (threshold and bin images) containing two images each were defined based upon the contribution of photons to reconstructed images. Analysis of variance (ANOVA) was calculated between CNR terms and image acquisition variables. A multivariate regression was then fitted to experimental data. Main Results. Image type had a major difference on how Covar-CNR values were distributed. Bin images had a slightly higher mean and wider standard deviation (Covar-CNR lo : 3.38 ±17.25, Covar-CNR hi : 5.77 ± 30.64) compared to threshold images (Covar-CNR lo : 2.08 ±1.89, Covar-CNR hi : 3.45 ± 2.49) across all conditions. ANOVA found that each acquisition variable had a significant relationship with both Covar-CNR terms. The multivariate regression model suggested that material concentration had the largest impact on all CNR terms. Signficance. In this work, we described a theoretical framework to extend the SNR to a multivariate form that is able to characterize images independently and also provide insight regarding the relationship between images. Experimental data was used to demonstrate the insight that this metrology provides about image formation factors in spectral CT., (© 2024 Institute of Physics and Engineering in Medicine.)

Objective . To assess the performance of a new antiscatter grid design in interventional cardiology for image quality improvement and dose reduction using experimental measurements and Monte Carlo (MC) simulation. Approach. Experimental measurements were performed on an angiography system, using a multi-layered tissue simulating composite phantom made from of poly(methyl methacrylate), aluminium and expanded polystyrene (2/0.2/0.7 cm). The total phantom thickness ranged from 20.3 cm to 40.6 cm. Four conditions were compared; (A) 105 cm source-image receptor distance (SID) without grid, ( B i ) 105 cm SID with grid ratio ( r ) and strip density ( N ) ( r 15 N 80), ( B ii ) 120 cm SID without grid, and (B iii ) 120 cm SID with high ratio grid ( r ), defined by the signal-to-noise ratio, was compared from the N 80). The system efficiency ( η ), defined by the signal-to-noise ratio, was compared from the B conditions against case A . These conditions were also simulated with MC techniques, allowing additional phantom compositions to be explored. Weighted image quality improvement factor ( η w ( u )) was studied experimentally at a specific spatial frequency due to the SID change. Images were simulated with an anthropomorphic chest phantom for the different conditions, and the system efficiency was compared for the different anatomical regions. Main results. Good agreement was found between the η and η w ( u ) methods using both measured and simulated data, with average relative differences between 2%-11%. Case B iii provided higher η values compared to A , and B i for thicknesses larger than 20.3 cm. In addition, case B iii also provided higher η values for high attenuating areas in the anthropomorphic phantom, such as behind the spine. Significance. The new antiscatter grid design provided higher system efficiency compared to the standard grid for the parameters explored in this work., (© 2024 Institute of Physics and Engineering in Medicine.)

Objective. Despite the demonstrated benefits of hyperspectral formalism for stem effect corrections in the context of fiber dose measurements, this approach has not been yet translated into volumetric measurements where cameras are typically used for their distinguishing spatial resolution. This work investigates demosaicing algorithms for polychromatic cameras based spectral imaging. Approach. The scintillation and Cherenkov signals produced in a radioluminescent phantom are imaged by a polychromatic camera and isolated using the spectral formalism. To do so, five demosaicing algorithms are investigated from calibration to measurements: a clustering method and four interpolation algorithms. The resulting accuracy of scintillation and Cherenkov images is evaluated with measurements of the differences (mean ± standard deviation) between the obtained and expected signals from profiles drawn across a scintillation spot. Signal-to-noise ratio and signal-to-background ratio are further measured and compared in the resulting scintillation images. Finally, the resulting differences on the scintillation signal from a 0.2 × 0.2 cm2 region-of-interest (ROI) were reported. Main results. Clustering, OpenCV, bilinear, Malvar and Menon demosaicing algorithms respectively yielded differences of 3 ± 5%, 1 ± 3%, 1 ± 3%, 1 ± 2% and 2 ± 4% in the resulting scintillation images. For the Cherenkov images, all algorithms provided differences below 1%. All methods enabled measurements over the detectability (SBR > 2) and sensitivity (SNR > 5) thresholds with the bilinear algorithm providing the best SNR value. Clustering, OpenCV, bilinear, Malvar and Menon demosaicing algorithms respectively provided differences on the ROI analysis of 7 ± 5%, 3 ± 2%, 3 ± 2%, 4 ± 2%, 7 ± 3%. Significance. Radioluminescent signals can accurately be isolated using a single polychromatic camera. Moreover, demosaicing using a bilinear kernel provided the best results and enabled Cherenkov signal subtraction while preserving the full spatial resolution of the camera. [ABSTRACT FROM AUTHOR]

Self-starting Q -switching, Q -switched mode-locking and mode-locking operation modes are achieved sequentially in an all-fiber erbium-doped fiber laser with thulium-doped fiber saturable absorber for the first time. The central wavelengths of Q -switching, Q -switched mode-locking and mode-locking operation modes are 1569.7 nm, 1570.9 nm, and 1572 nm, respectively. The mode-locking operation of the proposed fiber laser generates stable dark soliton with a repetition rate of 0.99 MHz and signal-to-noise ratio of 65 dB. The results validate the capability of generating soliton pulse by doped fiber saturable absorber. Furthermore, the proposed fiber laser is beneficial to the applications of optical communication and signal processing system. [ABSTRACT FROM AUTHOR]

We present the results from a spectroscopic survey using the MOSFIRE near-infrared spectrograph on the 10 m Keck telescope to search for Ly α emission from candidate galaxies at z ∼ 9â€"10 in four of the CANDELS fields (GOODS-N, EGS, UDS, and COSMOS). We observed 11 target galaxies, detecting Ly α from one object in ∼8.1 hr of integration, at z = 8.665 ± 0.001 with an integrated signal-to-noise ratio > 7. This galaxy is in the CANDELS Extended Groth Strip (EGS) field and lies physically close (3.5 physical Mpc [pMpc]) to another confirmed galaxy in this field with Ly α detected at z = 8.683. The detection of Ly α suggests the existence of large (∼1 pMpc) ionized bubbles fairly early in the reionization process. We explore the ionizing output needed to create bubbles of this size at this epoch and find that such a bubble requires more than the ionizing power provided by the full expected population of galaxies (by integrating the UV luminosity function down to M UV = âˆ'13). The Ly α we detect would be able to escape the predominantly neutral intergalactic medium at this epoch if our detected galaxy is inhabiting an overdensity, which would be consistent with the photometric overdensity previously identified in this region by Finkelstein et al. This implies that the CANDELS EGS field is hosting an overdensity at z = 8.7 that is powering one or more ionized bubbles, a hypothesis that will be imminently testable with forthcoming James Webb Space Telescope observations in this field. [ABSTRACT FROM AUTHOR]

Integral field spectroscopy of high-redshift galaxies has become a powerful tool for understanding their dynamics and evolutionary states. However, in the case of gravitationally lensed systems, it has proved difficult to model both lensing and intrinsic kinematics in a way that takes full advantage of the information available in the spectral domain. In this paper, we introduce a new method for pixel-based source reconstruction that alters standard regularization schemes for two-dimensional (2D) data in a way that leverages kinematic information in a physically motivated but flexible fashion, and that is better suited to the three-dimensional (3D) nature of integral field data. To evaluate the performance of this method, we compare its results to those of a more traditional 2D nonparametric approach using mock Atacama Large Millimeter/submillimeter Array (ALMA) observations of a typical high-redshift dusty star-forming galaxy. We find that 3D regularization applied to an entire data cube reconstructs a source’s intensity and velocity structure more accurately than 2D regularization applied to separate velocity channels. Cubes reconstructed with 3D regularization also have more uniform noise and resolution properties and are less sensitive to the signal-to-noise ratio of individual velocity channels than the results of 2D regularization. Our new approach to modeling integral field observations of lensed systems can be implemented without making restrictive a priori assumptions about intrinsic kinematics, and opens the door to new observing strategies that prioritize spectral resolution over spatial resolution (e.g., for multiconfiguration arrays like ALMA). [ABSTRACT FROM AUTHOR]

In this paper, a method for fault diagnosis in casing cutters is proposed. A vibration signal acquisition circuit for use in a high-temperature environment was designed, and a casing cutter measurement model was established, including a model of the casing cutter in a trouble-free state and two other common fault states. The vibration characteristics of the model were analyzed. A fault feature enhancement model based on enhancement of the signal to noise ratio and sparse representation, which effectively solves the fault diagnosis problem caused by the limited installation location and the limited performance of the vibration measurement at high temperature, was also designed. The MobileNet-V3-Small convolutional neural network (CNN) model was improved by reducing the basic blocks of the continuous homogeneous structure in the original model, and the Squeeze and Excitation structure expanded to the global level to obtain a lightweight CNN fault recognition model. The effectiveness and efficiency of the proposed method were validated by various experiments. [ABSTRACT FROM AUTHOR]

We report on the latest results of a Parkes multibeam survey for pulsars and dispersed radio bursts in the Large Magellanic Cloud (LMC). We conducted both periodicity and single-pulse searches at a much larger range of trial dispersion measures (DMs) than previously searched. We detected 229 single pulses with signal-to-noise ratio (S/N) > 7 that were classified by the deep-learning network FETCH as being real (with >90% likelihood), of which nine were from the known giant-pulse-emitting pulsar PSR B0540âˆ'69. Two possibly repeating sources were detected with DMs suggesting that they lie within the LMC, but these require confirmation. Only three of the 220 unknown pulses had S/N > 8, and the DM distribution for these detected pulses follows an exponential falloff with increasing DM and does not show any excess of signals at DM values expected for the LMC. These features suggest that the detected pulses are not likely to be real, although they are visually compelling. We also report the discovery of a new pulsar (PSR J0556âˆ'67) in our periodicity search. This pulsar has a spin period of 791 ms, a DM of 71 cmâˆ'3 pc, an estimated 1400 MHz flux density of ∼0.12 mJy, and shows no evidence of binary motion. Future observations may be able to confirm whether any of the weak but promising candidates in our single-pulse and periodicity searches, including our two possible repeaters, are real or not. [ABSTRACT FROM AUTHOR]

The possibility that primordial black holes (PBHs) form some part of dark matter has been considered for a long time but poorly constrained over a wide mass range. Fast radio bursts (FRBs) are bright radio transients with millisecond duration. Their lensing effect has been proposed to be one of the cleanest probes for constraining the presence of PBHs in the stellar-mass window. In this paper, we first apply the normalized cross-correlation algorithm to search and identify candidates for lensed FRBs in the latest public FRB observations, i.e., 593 FRBs, which mainly consist of the first Canadian Hydrogen Intensity Mapping Experiment FRB catalog, and then derive constraints on the abundance of PBHs from the null search result of the lensing signature. For a monochromatic mass distribution, the fraction of dark matter made up of PBHs could be constrained to ≤87% for ≥500 M ⊙ at the 95% confidence level by assuming signal-to-noise ratios dependent on the flux ratio threshold for each FRB and that apparently one-off events are intrinsic single bursts. This result would be improved by a factor of 3 when a conventional constant flux ratio threshold is considered. Moreover, we derive constraints on PBHs with a log-normal mass function naturally predicted by some popular inflation models and often investigated with gravitational-wave detections. We find that, in this mass distribution scenario, the constraint from the current public FRB observations is relatively weaker than the one from gravitational-wave detections. It is foreseen that upcoming complementary multimessenger observations will yield considerable constraints on the possibilities of PBHs in this intriguing mass window. [ABSTRACT FROM AUTHOR]

Stellar abundances and ages afford the means to link chemical enrichment to galactic formation. In the Milky Way, individual element abundances show tight correlations with age, which vary in slope across ([Fe/H]–[α/Fe]). Here, we step from characterizing abundances as measures of age, to understanding how abundances trace properties of stellar birth environment in the disk over time. Using measurements from ∼27,000 APOGEE stars (R = 22,500, signal-to-noise ratio > 200), we build simple local linear models to predict a sample of elements (X = Si, O, Ca, Ti, Ni, Al, Mn, Cr) using (Fe, Mg) abundances alone, as fiducial tracers of supernovae production channels. Given [Fe/H] and [Mg/H], we predict these elements, [X/H], to about double the uncertainty of their measurements. The intrinsic dispersion, after subtracting measurement errors in quadrature is ≈0.015–0.04 dex. The residuals of the prediction (measurement − model) for each element demonstrate that each element has an individual link to birth properties at fixed (Fe, Mg). Residuals from primarily massive-star supernovae (i.e., Si, O, Al) partially correlate with guiding radius. Residuals from primarily supernovae Ia (i.e., Mn, Ni) partially correlate with age. A fraction of the intrinsic scatter that persists at fixed (Fe, Mg), however, after accounting for correlations, does not appear to further discriminate between birth properties that can be traced with present-day measurements. Presumably, this is because the residuals are also, in part, a measure of the typical (in)-homogeneity of the disk's stellar birth environments, previously inferred only using open cluster systems. Our study implies at fixed birth radius and time that there is a median scatter of ≈0.01–0.015 dex in elements generated in supernovae sources. [ABSTRACT FROM AUTHOR]

Quantitative polymerase chain reaction (qPCR) is the most important quantitative sensing technique for pathogens, especially for emerging pandemics such as the coronavirus outbreak this year. The qPCR chip and device were investigated to meet the unmet needs of ultrafast inspection time, high accuracy, and small system volume. Therein, the fluorescence intensity was the most important signal in qPCR quantification of DNA amplifications, which is essential not only in the confirmative diagnosis of positive or negative infection, but also in the assessment of viral load for therapeutic and quarantine decision making. As the target DNAs got amplified, the interaction of fluorescence dye and double strand DNA will generate fluorescence signal proportional to amplified DNA in the intensity when excited by certain wavelength. A miniature spectro-detector was employed to receive the fluorescence scattering for digital output of the intensity in the qPCR chip in this study, and the optical simulation and actual experimental design and results according to the optical simulation results were performed to study the effect of the stray light shutter (SLS) in the improvement of the signal in fluorescence detection. The analysis results showed that the signal-to-noise ratio (SNR) of the fluorescence can be enhanced significantly for five times of the control using the SLS with a shape of extended component aperture, where the protruding structure was positioned away from the center. The experimental results showed that fluorescence intensity can be enhanced by 15.50% and 9.86% when adding the above shape of SLS in resin- and in glass-based chip, respectively. The results also demonstrated that the optical setup had good stability and repeatability in fluorescence detection, and variation was less than 1.00%. Our results can provide important reference to the development of qPCR chip to obtain the high SNR fluorescence signal in DNA quantification process. [ABSTRACT FROM AUTHOR]

Using data from a year-long dedicated campaign to observe bright stars, we study the crosstalk channels present in the GPC1 camera. By analyzing these data, we construct a data set that checks source stars on almost every CCD of every chip within the camera against all possible crosstalk destinations. We use a clustering algorithm to find potential crosstalk occurrences, and then also check all possible combinations (driven by the hardware layout) by eye. This results in a total of 640 rules, with a flux attenuation factor ranging from 2.5 Ă— 102 for the bright end to 2.5 Ă— 104 at the faint end. The average value of m crossâ€" m src ≈ âˆ'10.25 corresponds to an attenuating factor of 1.25 Ă— 104, which produces crosstalk ghosts with an average signal-to-noise ratio of 0.64 ± 0.1 on the bright images. We find no evidence of crosstalk signals between CCDs not connected in the hardware setup. The distribution of attenuation factors is also found to be dependent on crosstalk movement. A clear dependence on cell column offsets is found, consistent with the idea that the source star charge is progressively attenuated during the traversal of cell readout lines. While we can see the trends, the uncertainties on aperture magnitude measurements are large at this stage. [ABSTRACT FROM AUTHOR]

Objective. The main objective of this study was to assess the feasibility of lowering the hardware requirements for fast neural electrical impedance tomography (EIT) in order to support the distribution of this technique. Specifically, the feasibility of replacing the commercial modules present in the existing high-end setup with compact and cheap customized circuitry was assessed. Approach. Nerve EIT imaging was performed on rat sciatic nerves with both our standard ScouseTom setup and a customized version in which commercial benchtop current sources were replaced by custom circuitry. Electrophysiological data and images collected in the same experimental conditions with the two setups were compared. Data from the customized setup was subject to a down-sampling analysis to simulate the use of a recording module with lower specifications. Main results. Compound action potentials (573 ± 287 ÎĽ V and 487 ± 279 ÎĽ V, p =0.28) and impedance changes (36 ± 14 ÎĽ V and 31 ± 16 ÎĽ V, p =0.49) did not differ significantly when measured using commercial high-end current sources or our custom circuitry, respectively. Images reconstructed from both setups showed neglibile (<1voxel, i.e. 40 ÎĽ m) difference in peak location and a high degree of correlation (R 2 = 0.97). When down-sampling from 24 to 16 bits ADC resolution and from 100 to 50 KHz sampling frequency, signal-to-noise ratio showed acceptable decrease (<âˆ'20%), and no meaningful image quality loss was detected (peak location difference <1voxel, pixel-by-pixel correlation R 2 = 0.99). Significance : The technology developed for this study greatly reduces the cost and size of a fast neural EIT setup without impacting quality and thus promotes the adoption of this technique by the neuroscience research community. [ABSTRACT FROM AUTHOR]

Objective. The generation of quantification maps and weighted images in synthetic MRI techniques is based on complex fitting equations. This process requires longer image generation times. The objective of this study is to evaluate the feasibility of deep learning method for fast reconstruction of synthetic MRI. Approach. A total of 44 healthy subjects were recruited and random divided into a training set (30 subjects) and a testing set (14 subjects). A multiple-dynamic, multiple-echo (MDME) sequence was used to acquire synthetic MRI images. Quantification maps (T1, T2, and proton density (PD) maps) and weighted (T1W, T2W, and T2W FLAIR) images were created with MAGiC software and then used as the ground truth images in the deep learning (DL) model. An improved multichannel U-Net structure network was trained to generate quantification maps and weighted images from raw synthetic MRI imaging data (8 module images). Quantitative evaluation was performed on quantification maps. Quantitative evaluation metrics, as well as qualitative evaluation were used in weighted image evaluation. Nonparametric Wilcoxon signed-rank tests were performed in this study. Main results. The results of quantitative evaluation show that the error between the generated quantification images and the reference images is small. For weighted images, no significant difference in overall image quality or signal-to-noise ratio was identified between DL images and synthetic images. Notably, the DL images achieved improved image contrast with T2W images, and fewer artifacts were present on DL images than synthetic images acquired by T2W FLAIR. Significance. The DL algorithm provides a promising method for image generation in synthetic MRI techniques, in which every step of the calculation can be optimized and faster, thereby simplifying the workflow of synthetic MRI techniques. [ABSTRACT FROM AUTHOR]

Within an imaging instrument's field of view, there may be many observational targets of interest. Similarly, within a spectrograph's bandpass, there may be many emission lines of interest. The brightness of these targets and lines can be orders of magnitude different, which poses a challenge to instrument and mission design. A single exposure can saturate the bright emission and/or have a low signal-to-noise ratio (S/N) for faint emission. Traditional high dynamic range (HDR) techniques solve this problem by either combining multiple sequential exposures of varied duration or splitting the light to different sensors. These methods, however, can result in the loss of science capability, reduced observational efficiency, or increased complexity and cost. The simultaneous HDR method described in this paper avoids these issues by utilizing a special type of detector whose rows can be read independently to define zones that are then composited, resulting in areas with short or long exposure measured simultaneously. We demonstrate this technique for the Sun, which is bright on disk and faint off disk. We emulated these conditions in the lab to validate the method. We built an instrument simulator to demonstrate the method for a realistic solar imager and input. We then calculated S/Ns, finding a value of 45 for a faint coronal mass ejection and 200 for a bright one, both at 3.5 —meeting or far exceeding the international standard for digital photography that defines an S/N of 10 as acceptable and 40 as excellent. Future missions should consider this type of hardware and technique in their trade studies for instrument design. [ABSTRACT FROM AUTHOR]

Eccentricity has emerged as a potentially useful tool for helping to identify the origin of black hole mergers. However, eccentric templates can be computationally very expensive owing to the large number of harmonics, making statistical analyses to distinguish formation channels very challenging. We outline a method for estimating the signal-to-noise ratio (S/N) for inspiraling binaries at lower frequencies such as those proposed for LISA and DECIGO. Our approximation can be useful more generally for any quasi-periodic sources. We argue that surprisingly, the S/N evaluated at or near the peak frequency (of the power) is well approximated by using a constant-noise curve, even if in reality the noise strain has power-law dependence. We furthermore improve this initial estimate over our previous calculation to allow for frequency dependence in the noise to expand the range of eccentricity and frequency over which our approximation applies. We show how to apply this method to get an answer accurate to within a factor of 2 over almost the entire projected observable frequency range. We emphasize this method is not a replacement for detailed signal processing. The utility lies chiefly in identifying theoretically useful discriminators among different populations and providing fairly accurate estimates for how well they should work. This approximation can furthermore be useful for narrowing down parameter ranges in a computationally economical way when events are observed. We furthermore show a distinctive way to identify events with extremely high eccentricity where the signal is enhanced relative to naive expectations on the high-frequency end. [ABSTRACT FROM AUTHOR]

The spontaneous scalarization during the stellar core collapse in the massive scalarâ€"tensor theories of gravity introduces extra polarizations (on top of the plus and cross modes) in gravitational waves, whose amplitudes are determined by several model parameters. Observations of such scalarization-induced gravitational waveforms therefore offer valuable probes into these theories of gravity. Considering a triple-scalar interactions in such theories, we find that the self-coupling effects suppress the magnitude of the scalarization and thus reduce the amplitude of the associated gravitational wave signals. In addition, the self-interacting effects in the gravitational waveform are shown to be negligible due to the dispersion throughout the astrophysically distant propagation. As a consequence, the gravitational waves observed on the Earth feature the characteristic inverse-chirp pattern. Although not with the on-going ground-based detectors, we illustrate that the scalarization-induced gravitational waves may be detectable at a signal-to-noise ratio level of O (100) with future detectors, such as Einstein Telescope and Cosmic Explorer. [ABSTRACT FROM AUTHOR]

Telomerase plays an important role in maintaining the length of telomere during cell division and is recognized as a new kind of biomarkers for cancer diagnosis. In this work, we present a brand new telomerase detection strategy based on a DNA points accumulation for imaging in nanoscale topography (DNA-PAINT) like strategy. With an extraordinary spatial resolution (∼10 nm), the DNA-PAINT based strategy offers several advantages. First, it avoids complicated polymerase chain reaction and electrophoresis procedures. Second, it enables super resolution imaging of the reaction products with a high signal-to-noise ratio and facilitates the location of telomeric elongation sites on the single particle level, which results in a high sensitivity. Third, the detection scheme of the DNA-PAINT strategy allows direct in situ visualization of the telomeric elongation process, which has never been achieved before. All these advantages make the DNA-PAINT telomerase detection strategy significant for dynamic investigation of telomerase related physiological processes as well as cancer diagnosis. [ABSTRACT FROM AUTHOR]

We propose an algorithm, referred to as the pulsar phase and standard deviation (PPSD), to mitigate transient radio frequency interference (RFI) in pulsar observations. PPSD uses the model for pulsar time of arrival to identify pulsar phase and extract the pulse profile to protect the original pulsar profile. PPSD sets a threshold based on the statistics empirical rule to label the transient RFI in the off-pulse data until all unlabelled off-pulse data obeys the white Gaussian noise (WGN) distribution. The transient RFI data is then substituted with WGN. Finally, we use PPSD to process the pulsar observation data obtained from the NanShan 25 m Radio Telescope. Our results show that PPSD can effectively mitigate the transient RFI and improve the signal-to-noise ratio of the pulsar observations. [ABSTRACT FROM AUTHOR]

Background. An electrocardiogram (ECG) is an effective and non-invasive indicator for the detection and prevention of arrhythmia. ECG signals are susceptible to noise contamination, which can lead to errors in ECG interpretation. Therefore, ECG pretreatment is important for accurate analysis. Methods. The ECG data used are from CPSC2018, and the noise signal is from MIT-BIH Noise Stress Test Database. In the experiment, the signal-to-noise ratio (SNR), the root mean square error (RMSE), and the correlation coefficient P are used to evaluate the performance of the network. The method proposed is divided into two stages. In the first stage, a U de -net model is designed for ECG signal denoising to eliminate noise. The DR-net model in the second stage is used to reconstruct the ECG signal and to correct the waveform distortion caused by noise removal in the first stage. In this paper, the U de -net and the DR-net are constructed by the convolution method to achieve end-to-end mapping from noisy ECG signals to clean ECG signals. Result. In SNR, RMSE and P indicators, U de -net + DR-net proposed in this paper can achieve the best performance compared with the other five schemes (FCN, U-net etc). In the three data sets, SNR can be increased by 11.61 dB, 13.71 dB and 14.40 dB and RMSE can be reduced by 10.46 × 10âˆ'2, 21.55 × 10âˆ'2 and 15.98 × 10âˆ'2. Conclusions. Despite the contradictory results, the proposed two-stages method can achieve both the elimination of noise and the preservation of effective details to a large extent of the signals. The proposed method has good application prospects in clinical practice. [ABSTRACT FROM AUTHOR]

When attempting to detect faults in grounding grids, the electromagnetic induction method is hampered by the failure of fault diagnosis based on magnetic flux density detection. An electromagnetic induction impedance method is proposed to diagnose corrosion faults by detecting the induced impedance. A non-destructive testing prototype was developed. The sensor of the prototype consists of biaxial sensing coils, a frequency selection module, and a dual-channel synchronous demodulator. The biaxial sensing coils are configured as four identical coils forming a cross. This symmetrical structure realizes the detection of unknown topological structures in grounding grids. The frequency selection module improves the signal-to-noise ratio of the system. The digital demodulator synchronously extracts the phase information in two orthogonal directions. The key technical indicators of the prototype, such as the frequency characteristics, the amplitude accuracy, the phase accuracy, the interference resistance, and the amplitude consistency between channels are tested. Experiments were carried out on a real-size physical model, corroded samples, and an actual operating substation. The results confirm the feasibility of the method and the prototype for the detection of topological structures, disconnections, and faults in grounding grids. [ABSTRACT FROM AUTHOR]

In this paper, an optical fiber length measurement system based on phase-modulated pulse time of flight method is proposed. The fiber length is calculated by measuring the flight time of the phase-modulated pulse in the fiber under test. The measurement system is based on the interference structure of Mach–Zehnder hybrid Sagnac to obtain stable interference effects. To improve the signal-to-noise ratio of the system, the phase-generated carrier technique is utilized to effectively eliminate the parasitic Rayleigh scattering noise along the optical fiber. Finally, a balance of long measurement distance and high measurement precision is achieved in the system, and a measurement resolution of 0.2 m is realized within the measurement range of 100 km. [ABSTRACT FROM AUTHOR]

This study presents the design, fabrication, and testing of a novel piezoelectric micro-electro-mechanical-systems microphone with the partially removed lead zirconate titanate (PZT) unimorph structure for signal-to-noise ratio (SNR) enhancement. To demonstrate the feasibility of the presented concept, the unimorph cantilever array with the patterned (triangular shape) PZT film on top of a rectangular Si layer is simulated, fabricated, and tested. In comparison, the rectangular and triangular unimorph cantilever arrays with fully covered PZT film are also investigated. The proposed and reference cantilever arrays have the same foot print for fair comparison. Simulations show the proposed design has the highest output electrical energy which indicates the proposed design could successfully enhance the SNR. Moreover, acoustic measurements in the standard anechoic box show the proposed Si cantilever with patterned PZT film design could significantlyimprove the SNR. As compared to the reference microphone with triangular PZT/Si unimorph cantilever, the proposed design shows a 9.8 dB enhancement in SNR. Finally, the influences of initial structure deflections (due to the thin film residual stresses) on the low frequency responses for proposed and reference microphone designs are also investigated. [ABSTRACT FROM AUTHOR]

Understanding common envelope (CE) evolution is an outstanding problem in binary evolution. Although the CE phase is not driven by gravitational wave (GW) emission, the inspiraling binary emits GWs that passively trace CE dynamics. Detecting this GW signal would provide direct insight into gas-driven physics. Even a non-detection might offer invaluable constraints. We investigate the prospects of detection of a Galactic CE by LISA. While the dynamical phase of the CE is likely sufficiently loud for detection, it is short and thus rare. We focus instead on the self-regulated phase that proceeds on a thermal timescale. Based on population-synthesis calculations and the (unknown) signal duration in the LISA band, we expect ∼0.1â€"100 sources in the Galaxy during the mission duration. We map the GW observable parameter space of frequency f GW and its derivative f ̇ GW , remaining agnostic on the specifics of the inspiral and find that signals with signal-to-noise ratios > 10 are possible if the CE stalls at separations such that f GW ≳ 2 Ă— 10âˆ'3 Hz. We investigate the possibility of misidentifying the signal with other known sources. If the second derivative f ̈ GW can also be measured, the signal can be distinguished from other sources using a GW braking index. Alternatively, coupling LISA with electromagnetic observations of peculiar red giant stars and/or infrared and optical transients, might allow for the disentangling of a Galactic CE from other Galactic and extragalactic GW sources. [ABSTRACT FROM AUTHOR]

Pipelines are widely used to transport water, oil, and gas. Severe corrosion of pipes can cause leakage of natural gas and result in an explosion or even more severe disasters. Magnetostrictive sensors based on ultrasonic guided waves have been validated as feasible for non-destructive testing of pipes with advantages of long-range inspection, high signal to noise ratio, with high testing efficiency. Using a patch made from a highly magnetostrictive material dramatically enhances the performance of these sensors; however, mounting this patch of smart material on a pipe requires using strong epoxy glue. Plus, applying the glue on the pipe and waiting for it to dry is time-consuming. After completing the test, leaving the pipe's smart material could be hazardous and initiate electrochemical corrosion. Thus, smart material must be removed after the test. Nonetheless, it is complicated to remove smart material from the pipe's surface after the glue dries. Considering such complications, as a solution to this problem, in this paper, the novel design of the pneumatic magnetostrictive patch transducer (PMPT) is proposed that benefits from the advantages of the patch in the magnetostrictive sensor but also eliminates such drawbacks. The performance of PMPT was validated by theoretical analysis and experimental investigation. The flexible, fast feature and portable configuration of the proposed PMPT make it easy to be applied on in-service pipes in different diameters with corrosion surfaces or coating. [ABSTRACT FROM AUTHOR]

In the process of detecting the amplitude and phase of the weak signal based on digital lock-in amplifier (DLIA), a low-pass filter (LPF) with an extremely low cut-off frequency is required. Even so, the effect is often not good. The detection results of amplitude and phase of weak signal are distorted and fluctuate sharply. To eliminate the difficulty of LPF design, and to reduce the distortion and output fluctuation of detection results of DLIA, this paper proposes a weak signal detection method based on integral average digital lock-in amplifier (IADLIA), which can be used for weak signal detection under strong noise. This method optimizes the structure of the traditional DLIA based on the principle of cross-correlation detection. And then this paper analyzes the influence of the cut-off frequency and signal-to-noise ratio of the LPF on the detection accuracy and output stability of the weak signal amplitude and phase. Finally, the corresponding weak-signal amplitude at the bearing rotating frequency is accurately detected by the proposed method, and the accurate identification of bearing faults is realized. The experimental results show that the IADLIA is characterized by stable output, strong anti-interference ability and high output accuracy. The proposed method can accurately detect the amplitude and phase information of the weak signal in a strong noise environment, get rid of the structure of LPF, and the overall performance is better than the traditional DLIA. [ABSTRACT FROM AUTHOR]

The Chinese very long baseline interferometry net (CVN) has applied the delta differential one-way ranging (delta-DOR) radiometric tracking technique to determine the orbit of a lunar and a deep-space spacecraft. In the past observations, CVN had sometimes encountered a difficult observation situation that the DOR signals were extremely weak or severely interfered. The CVN FX correlator, usually cannot process these extremely weak DOR signals. In order to enhance the weak signal processing capability of CVN, here we report an alternative method called 'local correlation'. By this method, the distinct DOR signals structure features of the narrow bandwidth sinusoidal tone and their coherent phase, are used to extract the exact frequency and phase of each tone. We have adopted the method on the weak signal case, then compared with the results obtained from CVN FX correlator. Analysis result shows the local correlation method can process DOR tone at lower signal-to-noise ratios such as 1 dBHz. The method also has advantages of high anti-interference, low computation cost and high efficiency. Furthermore, the delta-DOR delay obtained from local correlation can improve the orbit accuracy and reduce the arc length required by the orbit determination after the trajectory course maneuver. [ABSTRACT FROM AUTHOR]