Signal-to-noise Research Articles

Sensitive Discrete Frequency Mid-Infrared Absorption Spectroscopy Using Digitally Referenced Detection.

Broadly tunable mid-infrared (IR) lasers, including quantum cascade lasers (QCL), are an asset for vibrational spectroscopy wherein high-intensity, coherent illumination can target specific spectral bands for rapid, direct chemical detection with microscopic localization. These emerging spectrometers are capable of high measurement throughputs with large detector signals from the high-intensity lasers and fast detection speeds as short as a single laser pulse, challenging the decades old benchmarks of Fourier transform infrared spectroscopy. However, noise in QCL emissions limits the feasible acquisition time for high signal-to-noise ratio (SNR) data. Here, we present an implementation that is broadly compatible with many laser-based spectrometer and microscope designs to address these limitations by leveraging high-speed digitizers and dual detectors to digitally reference each pulse individually. Digitally referenced detection (DRD) is shown to improve measurement sensitivity, with broad spectral indifference, regardless of imbalance due to dissimilarities among system designs or component manufacturers. We incorporated DRD into existing instruments and demonstrated its generalizability: a spectrometer with a 10-fold reduction in spectral noise, a microscope with reduced pixel dwell times to as low as 1 pulse while maintaining SNR normally achieved when operating 8-fold slower, and finally, a spectrometer to measure vibrational circular dichroism (VCD) with a ∼ 4-fold reduction in scan times. The approach not only proves versatile and effective but can also be tailored for specific applications with minimal hardware changes, positioning it as a simple and promising module for spectrometer designs using lasers.

Investigation of triaxial cables and microdetectors in small field dosimetry.

Small field dosimetry presents unique challenges with source occlusion, lateral charged particle equilibrium and detector size. As detector volume decreases, signal strength declines while noise increases, deteriorating the signal-to-noise ratio (SNR). This issue may be compounded by triaxial cables connecting detectors to electrometers. However, effects of cables, critical for precision dosimetry, are often overlooked. There is a need to evaluate triaxial cable and detector impacts on SNR in small fields. To evaluate the influence of triaxial cables and microdetectors on signal-to-noise ratios in small-field dosimetry. This study also aims to establish the importance of cable quality assurance for measurement accuracy.Six 9.1 m length triaxial cables from different manufacturers were tested with six microdetectors . A 6 MV photon beam (TrueBeam) was used, with a water phantom at 5 cm depth with 0.5×0.5 cm2 to 10×10 cm2 fields at 600 MU/min. Readings were acquired using cable-detector permutations with a dedicated electrometer. Cables had differing connector types, conductor materials, insulation, and diameter. Detectors had various sensitive volumes, materials, typical signals, and bias voltages.
Normalized FOFs showed 13.4% and 4.6% variation across cables for 0.5×0.5 cm2 and 1×1 cm2 fields, respectively. The maximum estimated error between any cable-detector combinations was 0.2%. No consistent FOF trend was observed with increasing cable diameter, likely due to different types of detectors used. However, absolute FOF differences of 0.9% and 0.3% were noted between cables for 0.5×0.5 cm2 and 1×1 cm2 fields, respectively.
Regular triaxial cable quality assurance is critical for precision small field dosimetry. A national protocol is needed to standardize cable evaluations/calibrations, particularly for small signals from modern detectors. This could enhance measurement accuracy and treatment delivery with advanced small-field radiotherapy techniques that promise improved patient outcomes. Further studies should expand detector and cable models tested across institutions to establish robust quality control guidelines.

A Segmented Sliding Window Reference Signal Reconstruction Method Based on Fuzzy C-Means

Reference signal reconstruction serves as a crucial technique for suppressing multipath interference and noise in the reference channel of passive radar. Aiming at the challenge of detecting Low-Slow-Small (LSS) targets using Digital Terrestrial Multimedia Broadcasting (DTMB) signals, this article proposes a novel segmented sliding window reference signal reconstruction method based on Fuzzy C-Means (FCM). By partitioning the reference signals based on the structure of DTMB signal frames, this approach compensates for frequency offset and sample rate deviation individually for each segment. Additionally, FCM clustering is utilized for symbol mapping reconstruction. Both simulation and experimental results show that the proposed method significantly suppresses constellation diagram divergence and phase rotation, increases the adaptive cancellation gain and signal-to-noise ratio (SNR), and in the meantime reduces the computation cost.

Next-Generation Dual Transceiver FSO Communication System for High-Speed Trains in Neom Smart City

Smart cities like Neom require efficient and reliable transportation systems to support their vision of sustainable and interconnected urban environments. High-speed trains (HSTs) play a crucial role in connecting different areas of the city and facilitating seamless mobility. However, to ensure uninterrupted communication along the rail lines, advanced communication systems are essential to expand the coverage range of each base station (BS) while reducing the handover frequency. This paper presents the dual transceiver free space optical (FSO) communication system as a solution to achieve these objectives in the operational environment of HSTs in Neom city. Our channel model incorporates log-normal (LN) and gamma–gamma (GG) distributions to represent channel impairments and atmospheric turbulence in the city. Furthermore, we integrated the siding loop model, providing valuable insights into the system in real-world scenarios. To assess the system’s performance, we formulated the received signal-to-noise ratio (SNR) of the network under assumed fading conditions. Additionally, we analyzed the system’s bit error rate (BER) analytically and through Monte Carlo simulation. A comparative analysis with reconfigurable intelligent surfaces (RIS) and relay-assisted FSO communications shows the superior coverage area and efficiency of the dual transceiver model. A significant reduction of up to 76% and 99% in the number of required BSs compared to RIS and relay, respectively, is observed. This reduction leads to fewer handovers and lower capital expenditure (CAPEX) costs.

RCVM‐ASS‐CICSKA‐PAPT‐VDF: VLSI design of high‐speed reconfigurable compressed Vedic PAPT‐VDF filter for ECG medical application

AbstractDuring signal acquisition, the signals are impacted by multiple noise sources that must be filtered before any analysis. However, many different filter implementations in VLSI are dispersed among many studies. This study aims to give readers a systematic approach to designing a Pipelined All‐Pass Transformation based Variable digital filter (PAPT‐VDF) to eliminate the high‐frequency noise from ECG data. The modified design emphasizes first‐ and second‐order responses to obtain high‐speed filter realization with high operating frequencies. The addition of adder and multiplier designs to the hardware architecture of a filter design improves performance. The fundamental blocks of the filter design are the adder and multiplier. The adder and multiplier are employed with an Adaptable stage size‐based concatenation, incremented carry‐skip adder (ASS‐CICSKA), and Improved reconfigurable compressed Vedic multiplier (IRCVM). Utilizing the adder design diminishes the delay with enhanced performance because receiving the carry from an incrementation block is not mandatory. In the multiplier design, the compressor and the reconfigurable approach are adapted with a data detector block to detect the redundant input and lower the logic gates' switching activity with less area overhead. The proposed filter design is implemented in vertex 7 FPGA family device, and the performance measures are analyzed regarding area utilization, delay, power, and frequency. Also, by using the denoised signal, the mean square error (MSE), and signal‐to‐noise ratio (SNR) are evaluated in the MATLAB platform.

Optimal parameter estimation for uncertain structural systems under unknown random excitations

An optimal parameter estimation method for uncertain structural systems under unknown random excitations is proposed, which combines the Bayesian inference and stochastic dynamics using geometrically averaged likelihood and optimal response estimation. The general description of optimal parameter estimation problems for uncertain dynamic systems under only stochastic responses measured is presented. The posterior probability density conditional on measured responses is expressed by the likelihood function conditional on system parameters based on Bayes’ theorem. For finite time processes, the parameter estimation problem as the probability integral of conditional means is converted into the optimization problem expressed as maximizing the posterior probability density. A geometrically averaged likelihood function is defined and used for calculating the logarithmic posterior probability density. This estimation can avoid the numerical singularity of the likelihood function and reduce the effects of incomplete posterior probability density and inaccurate prior statistics of unknown random excitations, and then it will be more reasonable and effective. Furthermore, the differential equations for system response means and covariances are derived and solved based on stochastic dynamics theory. The means and covariances conditional on responses at the present instant are expressed by the previous statistics based on optimal response estimation. By combining two results, the analytical expressions of the averaged likelihood function and logarithmic posterior probability density are obtained which will be more reliable and accurate. The proposed optimal estimation method is verified by numerical results for a five-storey frame structure under base random excitation. The estimated results are not affected by the prior statistics errors of random excitation as a factor. For noisy observation, the Kalman filtering is incorporated in the estimation method and hence the estimation is more accurate and robust even for low signal noise ratios. The optimal estimation method has the potential for application to general uncertain systems.

Assessment of Calcaneal Spongy Bone Magnetic Resonance Characteristics in Women: A Comparison between Measures Obtained at 0.3 T, 1.5 T, and 3.0 T

Background: There is a growing interest in bone tissue MRI and an even greater interest in using low-cost MR scanners. However, the characteristics of bone MRI remain to be fully defined, especially at low field strength. This study aimed to characterize the signal-to-noise ratio (SNR), T2, and T2* in spongy bone at 0.3 T, 1.5 T, and 3.0 T. Furthermore, relaxation times were characterized as a function of bone-marrow lipid/water ratio content and trabecular bone density. Methods: Thirty-two women in total underwent an MR-imaging investigation of the calcaneus at 0.3 T, 1.5 T, and 3.0 T. MR-spectroscopy was performed at 3.0 T to assess the fat/water ratio. SNR, T2, and T2* were quantified in distinct calcaneal regions (ST, TC, and CC). ANOVA and Pearson correlation statistics were used. Results: SNR increase depends on the magnetic field strength, acquisition sequence, and calcaneal location. T2* was different at 3.0 T and 1.5 T in ST, TC, and CC. Relaxation times decrease as much as the magnetic field strength increases. The significant linear correlation between relaxation times and fat/water found in healthy young is lost in osteoporotic subjects. Conclusion: The results have implications for the possible use of relaxation vs. lipid/water marrow content for bone quality assessment and the development of quantitative MRI diagnostics at low field strength.

Enhanced Underwater Single Vector-Acoustic DOA Estimation via Linear Matched Stochastic Resonance Preprocessing

Underwater acoustic vector sensors (UAVSs) are increasingly utilized for remote passive sonar detection, but the accuracy of direction-of-arrival (DOA) estimation remains a challenging problem, particularly under low signal-to-noise ratio (SNR) conditions and complex background noise. In this paper, a comprehensive theoretical analysis is conducted on UAVS signal preprocessing subjected to gain-phase uncertainties for average acoustic intensity measurement (AAIM) and complex acoustic intensity measurement (CAIM)-based vector DOA estimation, aiming to explain the theoretical restrictions of intensity-based vector acoustic preprocessing approaches. On this basis, a generalized vector acoustic preprocessing optimization model is established in which the principle can be described as “maximizing the denoising performance under the constraints of an equivalent amplitude-gain response and phase-bias response”. A novel vector acoustic preprocessing method named linear matched stochastic resonance (LMSR) is proposed within the framework of matched stochastic resonance theory, which can naturally guarantee the linear gain-phase restrictions, as well achieving effective denoising performance. Numerical analyses demonstrate the superior vector DOA estimation performance of our proposed LMSR-AAIM and LMSR-CAIM methods in comparison to classical intensity-based AAIM and CAIM methods, especially under low-SNR conditions and non-Gaussian impulsive noise circumstances. Experimental verification conducted in the South China Sea further verifies its the effectiveness for practical application. This work can lay a solid foundation to break through the challenges of underwater remote vector acoustic DOA estimation under low-SNR conditions and complex ocean ambient noise and can provide important guidance for future research work.

In vivo demonstration of a real-time temporal SNR acoustic output adjustment method.

This work proposes a novel method of temporal signal-to-noise ratio (SNR) guided adaptive acoustic output adjustment and demonstrates this approach during in vivo fetal imaging. Acoustic output adjustment is currently the responsibility of sonographers, but ultrasound safety studies show recommended ALARA (As Low As Reasonably Achievable) practices are inconsistently followed. This study explores an automated ALARA method that adjusts the Mechanical Index (MI) output, targeting imaging conditions matching the temporal noise perception threshold. A 28 dB threshold SNR is used as the target SNR, following prior work showing relevant noise quantities are imperceptible once this image data quality level is reached. After implementing adaptive output adjustment on a clinical system, the average MI required to achieve 28 dB SNR in an eleven-volunteer fetal abdomen imaging test ranged from 0.17 to 0.26. The higher MI levels were required when imaging at higher frequencies. During tests with 20-second MI adjustment imaging periods, the degree of motion impacted the adaptive performance. For stationary imaging views, target SNR levels were maintained in 90% of SNR evaluations. When scanning between targets the imaging conditions were more variable, but the target SNR was still maintained in 71% of the evaluations. Given the relatively low MI recommended when performing MI adjustment and the successful adjustment of MI in response to changing imaging conditions, these results encourage adoption of adaptive acoustic output approaches guided by temporal SNR.

Noisy speech impairs retention of previously heard information only at short time scales.

When speech is presented in noise, listeners must recruit cognitive resources to resolve the mismatch between the noisy input and representations in memory. A consequence of this effortful listening is impaired memory for content presented earlier. In the first study on effortful listening, Rabbitt, The Quarterly Journal of Experimental Psychology, 20, 241-248 (1968; Experiment 2) found that recall for a list of digits was poorer when subsequent digits were presented with masking noise than without. Experiment 3 of that study extended this effect to more naturalistic, passage-length materials. Although the findings of Rabbitt's Experiment 2 have been replicated multiple times, no work has assessed the robustness of Experiment 3. We conducted a replication attempt of Rabbitt's Experiment 3 at three signal-to-noise ratios (SNRs). Results at one of the SNRs (Experiment 1a of the current study) were in the opposite direction from what Rabbitt, The Quarterly Journal of Experimental Psychology, 20, 241-248, (1968) reported - that is, speech was recalled more accurately when it was followed by speech presented in noise rather than in the clear - and results at the other two SNRs showed no effect of noise (Experiments 1b and 1c). In addition, reanalysis of a replication of Rabbitt's seminal finding in his second experiment showed that the effect of effortful listening on previously presented information is transient. Thus, effortful listening caused by noise appears to only impair memory for information presented immediately before the noise, which may account for our finding that noise in the second-half of a long passage did not impair recall of information presented in the first half of the passage.

Development of a novel X-ray fluorescence instrument equipped with a noble gas filter.

In X-ray fluorescence (XRF) analysis, which has been used to analyze elements in various samples, it is important to decrease the background (BG) intensity. Generally, BG signals are reduced by inserting metal foils of various types and thicknesses between the X-ray tube and sample as primary X-ray filters. In this study, we developed a unique gas filter-XRF (GF-XRF) instrument to easily reduce the BG effect by changing the pressure of the gas to ensure that the absorption edge of the gas element is slightly lower than the energy of the XRF peak of the target element. The advantage of using a gas is that the gas pressure can be altered easily. To evaluate the performance of this instrument, Ti and Zr were selected as target elements, and Ar and Kr were selected as the filtering gases. When the XRF spectra of the Ti sample were recorded using the Ar gas filter, as the Ar gas pressure increased, the background signal in the energy region of the Ti Kα peak decreased, resulting in an increase in the signal-to-noise ratio (SNR) of that peak. When the Kr gas filter was used, both the SNR and the minimum detection limit of Zr improved. These results demonstrate that the unique GF-XRF instrument is useful for high-sensitivity analyses.

Single-molecule localization microscopy at 2.4-fold resolution improvement with optical lattice pattern illumination

Traditional camera-based single-molecule localization microscopy (SMLM), with its high imaging resolution and localization throughput, has made significant advancements in biological and chemical researches. However, due to the limitation of the fluorescence signal-to-noise ratio (SNR) of a single molecule, its resolution is difficult to reach to 5 nm. Optical lattice produces a nondiffracting beam pattern that holds the potential to enhance microscope performance through its high contrast and penetration depth. Here, we propose a new method named LatticeFLUX which utilizes the wide-field optical lattice pattern illumination for individual molecule excitation and localization. We calculated the Cramér-Rao lower bound of LatticeFLUX resolution and proved that our method can improve the single molecule localization precision by 2.4 times compared with the traditional SMLM. We propose a scheme using 9-frame localization, which solves the problem of uneven lattice light illumination. Based on the experimental single-molecule fluorescence SNR, we coded the image reconstruction software to further verify the resolution enhancement capability of LatticeFLUX on simulated punctate DNA origami, line pairs, and cytoskeleton. LatticeFLUX confirms the feasibility of using 2D structured light illumination to obtain high single-molecule localization precision under high localization throughput. It paves the way for further implementation of ultra-high resolution full 3D structured-light-illuminated SMLM.

Value of vendor-agnostic deep learning image denoising in brain computed tomography: A multi-scanner study.

To evaluate the effect of a vendor-agnostic deep learning denoising (DLD) algorithm on diagnostic image quality of non-contrast cranial computed tomography (ncCT) across five CT scanners.This retrospective single-center study included ncCT data of 150 consecutive patients (30 for each of the five scanners) who had undergone routine imaging after minor head trauma. The images were reconstructed using filtered back projection (FBP) and a vendor-agnostic DLD method. Using a 4-point Likert scale, three readers performed a subjective evaluation assessing the following quality criteria: overall diagnostic image quality, image noise, gray matter-white matter differentiation (GM-WM), artifacts, sharpness, and diagnostic confidence. Objective analysis included evaluation of noise, contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), and an artifact index for the posterior fossa.In subjective image quality assessment, DLD showed constantly superior results compared to FBP in all categories and for all scanners (p<0.05) across all readers. The objective image quality analysis showed significant improvement in noise, SNR, and CNR as well as for the artifact index using DLD for all scanners (p<0.001).The vendor-agnostic deep learning denoising algorithm provided significantly superior results in the subjective as well as in the objective analysis of ncCT images of patients with minor head trauma concerning all parameters compared to the FBP reconstruction. This effect has been observed in all five included scanners. · Significant improvement of image quality for 5 scanners due to the vendor-agnostic DLD. · Subjects were patients with routine imaging after minor head trauma. · Reduction of artifacts in the posterior fossa due to the DLD. · Access to improved image quality even for older scanners from different vendors. · Kapper C, Müller L, Kronfeld A et al. Value of vendor-agnostic deep learning image denoising in brain computed tomography: A multi-scanner study. Fortschr Röntgenstr 2024; DOI 10.1055/a-2290-4781.

Michelson Interferometer-based Tunable Multiwavelength Thulium-doped Fluoride Fiber Laser in S-band for 5G networks

Abstract In this work, a multiwavelength thulium-doped fluoride fiber laser (TDFFL) has been reported for S-band region using the Michelson interferometer as an optical filter. Michelson interferometer was utilized as a comb-like filter, whereas dispersion-compensated fiber (DCF) of 10 km length was used to increase the number of lasing lines and reduce mode competition. Approximately, 61 lasing lines were achieved with a signal-to-noise ratio (SNR) of 40 dB in the wavelength region from 1501.36 to 1505.52 nm within 10 dB of maximum power. The stability of multiwavelength fiber laser was also recorded for the time duration of two hours, and it was found that all lasing lines were stable and equally spaced at the free spectral range (FSR) of 0.07 nm. The variation in optical power was less than 0.5 dB, however the wavelength shift was about 0.01 nm. Tunability of multiwavelength laser has also been observed up to the broader transmission span of 57 nm by incorporating the tunable band pass filter (TBPF). Moreover, FSR was observed to be tunable within the range of 0.02 to 0.14 nm by using the optical delay line (ODL) with a Michelson interferometer. Additionally, the proposed laser can generate frequency signals from 2.65 GHz to 18.54 GHz according to the obtained range of FSR.&amp;#xD;

Transformer-CNN hybrid network for improving PET time of flight prediction.

In positron emission tomography (PET) reconstruction, the integration of Time-of-Flight (TOF) information, known as TOF-PET, has been a major research focus. Compared to traditional reconstruction methods, the introduction of TOF enhances the signal-to-noise ratio (SNR) of images. Precision in TOF is measured by Full Width at Half Maximum (FWHM) and the offset from ground truth, referred to as coincidence time resolution (CTR) and bias. &#xD;This study proposes a network combining Transformer and Convolutional Neural Network (CNN) to utilize TOF information from detector waveforms, using event waveform pairs as inputs. This approach integrates the global self-attention mechanism of Transformer, which focuses on temporal relationships, with the local receptive field of CNN. The combination of global and local information allows the network to assign greater weight to the rising edges of waveforms, thereby extracting valuable temporal information for precise TOF predictions. Experiments were conducted using lutetium yttrium oxyorthosilicate (LYSO) scintillators and silicon photomultiplier (SiPM) detectors. The network was trained and tested using the waveform datasets after cropping.&#xD;Compared to the constant fraction discriminator (CFD), CNN, CNN with attention, Long Short-Term Memory (LSTM) and Transformer, our network achieved an average CTR of 189 ps, reducing it by 82 ps (more than 30%), 13 ps (6.4%), 12 ps (6.0%), 16 ps (7.8%) and 9 ps (4.6%), respectively. Additionally, a reduction of 10.3, 8.7, 6.7 and 4 ps in average bias was achieved compared to CNN, CNN with attention, LSTM and Transformer.&#xD;This work demonstrates the potential of applying the Transformer for PET TOF estimation using real experimental data. Through the integration of both CNN and Transformer with local and global attention, it achieves optimal performance, thereby presenting a novel direction for future research in this field.

Optimizing scan time and bayesian penalized likelihood reconstruction algorithm in copper-64 PET/CT imaging: a phantom study

Objectives: The aim of this study was to evaluate Cu-64 PET phantom image quality using Bayesian Penalized Likelihood (BPL) and Ordered Subset Expectation Maximum with point-spread function modeling (OSEM-PSF) reconstruction algorithms. In the BPL, the regularization parameter β was varied to identify the optimum value for image quality. In the OSEM-PSF, the effect of acquisition time was evaluated to assess the feasibility of shortened scan duration. Methods: A NEMA IEC PET body phantom was filled with known activities of water soluble Cu-64. The phantom was imaged on a PET/CT scanner and was reconstructed using BPL and OSEM-PSF algorithms. For the BPL reconstruction, various β values (150, 250, 350, 450, and 550) were evaluated. For the OSEM-PSF algorithm, reconstructions were performed using list-mode data intervals ranging from 7.5 to 240 s. Image quality was assessed by evaluating the signal to noise ratio (SNR), contrast to noise ratio (CNR), and background variability (BV). Results: The SNR and CNR were higher in images reconstructed with BPL compared to OSEM-PSF. Both the SNR and CNR increased with increasing β, peaking at β = 550. The CNR for all β, sphere sizes and tumor-to-background ratios (TBRs) satisfied the Rose criterion for image detectability (CNR > 5). BPL reconstructed images with β = 550 demonstrated the highest improvement in image quality. For OSEM-PSF reconstructed images with list-mode data duration ≥ 120 s, the noise level and CNR were not significantly different from the baseline 240 s list-mode data duration. Conclusions: BPL reconstruction improved Cu-64 PET phantom image quality by increasing SNR and CNR relative to OSEM-PSF reconstruction. Additionally, this study demonstrated scan time can be reduced from 240 to 120 s when using OSEM-PSF reconstruction while maintaining similar image quality. This study provides baseline data that may guide future studies aimed to improve clinical Cu-64 imaging.

Quick recipes for gravitational-wave selection effects

Accurate modeling of selection effects is a key ingredient to the success of gravitational-wave astronomy. The detection probability plays a crucial role in both statistical population studies, where it enters the hierarchical Bayesian likelihood, and astrophysical modeling, where it is used to convert predictions from population-synthesis codes into observable distributions. We review the most commonly used approximations, extend them, and present some recipes for a straightforward implementation. These include a closed-form expression capturing both multiple detectors and noise realizations written in terms of the so-called Marcum Q-function and a ready-to-use mapping between signal-to-noise ratio (SNR) thresholds and false-alarm rates from state-of-the-art detection pipelines. The bias introduced by approximating the matched filter SNR with the optimal SNR is not symmetric: sources that are nominally below threshold are more likely to be detected than sources above threshold are to be missed. Using both analytical considerations and software injections in detection pipelines, we confirm that including noise realizations when estimating the selection function introduces an average variation of a few %. This effect is most relevant for large catalogs and specific subpopulations of sources at the edge of detectability (e.g. high redshifts).

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

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

The Influence of Anti-scatter Grid Usage for Knee Computerized Radiography

Aims: This experimental study investigated the effect of using an anti-scatter grid in computerized knee radiography (CR) on image quality (IQ) and patient surface radiation dose (Equivalent Surface Air Kerma – Ka,e), measured with an ionization chamber. Place and Duration of Study: The experimental study was conducted between February 2024 and April 2024, in the radiodiagnosis laboratory belonging to the Medical Physics and Radiology Technology courses at the Franciscan University (UFN) in the city of Santa Maria, Rio Grande do Sul. Methodology: Utilizing a semi-anatomical knee phantom to simulate clinical examination conditions, ten images were acquired, with five obtained using technique 1 (70 kV, 200mA, and 20mAs) and another five with technique 2 (70 kVp, 200mA, and 5 mAs), with and without an anti-scatter grid, respectively. The phantom images were digitized in a CR system and quantified using a publicly available automatic analyzer software based on histograms and regions of interest (ROI), defined by signal and noise. The obtained results were used to calculate the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and radiographic contrast (RC), considered IQ descriptors. Results: As a selection criterion, the percentage deviation (D%) was chosen, considering technique 1 as the reference concerning technique 2. It was observed that technique 1 showed an SNR 1.20%, RC 3.86%, and Ka,e 73.68% higher than technique 2; on the other hand, technique 2 indicated a CNR 4.76% higher compared to technique 1. Conclusion: It is concluded that technique 2 without an anti-scatter grid may be preferable when considering the principle of optimization, where the dose is significantly reduced without a significant loss in IQ descriptors.

Improved stent sharpness evaluation with Super-Resolution deep learning reconstruction in coronary computed tomography angiography.

This study aimed to assess the impact of super-resolution deep learning reconstruction (SR-DLR) on coronary computed tomography angiography (CCTA) image quality and blooming artifacts from coronary artery stents in comparison to conventional methods, including hybrid iterative reconstruction (HIR) and deep learning-based reconstruction (DLR). A retrospective analysis included sixty-six CCTA patients from July to November 2022. Major coronary arteries were evaluated for image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). Stent sharpness was quantified using 10-90% edge rise slope (ERS) and 10-90% edge rise distance (ERD). Qualitative analysis employed a 5-point scoring system to assess overall image quality, image noise, vessel wall, and stent structure. SR-DLR demonstrated significantly lower image noise compared to HIR and DLR. SNR and CNR were notably higher in SR-DLR. Stent ERS was significantly improved in SR-DLR, with mean ERD values of 0.70 ± 0.20 mm for SR-DLR, 1.13 ± 0.28 mm for HIR, and 0.85 ± 0.26 mm for DLR. Qualitatively, SR-DLR scored higher in all categories. SR-DLR produces images with lower image noise, leading to improved overall image quality, compared with HIR and DLR. SR-DLR is a valuable image reconstruction algorithm for enhancing the spatial resolution and sharpness of coronary artery stents without being constrained by hardware limitations. The overall image quality was significantly higher in SR-DLR, resulting in sharper coronary artery stents compared to HIR and DLR.