High SNR Research Articles

GRASS. II. Simulations of Potential Granulation Noise Mitigation Methods

We present an updated version of the GRanulation And Spectrum Simulator (GRASS) which now uses an expanded library of 22 solar lines to empirically model time-resolved spectral variations arising from solar granulation. We show that our synthesis model accurately reproduces disk-integrated solar line profiles and bisectors, and we quantify the intrinsic granulation-driven radial-velocity (RV) variability for each of the 22 lines studied. We show that summary statistics of bisector shape (e.g., bisector inverse slope) are strongly correlated with the measured anomalous, variability-driven RV at high pixel signal-to-noise ratio SNR and spectral resolution. Further, the strength of the correlations varies both line by line and with the summary statistic used. These correlations disappear for individual lines at the typical spectral resolutions and SNRs achieved by current extremely precise radial velocity spectrographs; so we use simulations from GRASS to demonstrate that they can, in principle, be recovered by selectively binning lines that are similarly affected by granulation. In the best-case scenario (high SNR and large number of binned lines), we find that a ≲30% reduction in the granulation-induced root mean square RV can be achieved, but that the achievable reduction in variability is most strongly limited by the spectral resolution of the observing instrument. Based on our simulations, we predict that existing ultra-high-resolution spectrographs, namely, ESPRESSO and PEPSI, should be able to resolve convective variability in other, bright stars.

A preliminary study of super-resolution deep learning reconstruction with cardiac option for evaluation of endovascular-treated intracranial aneurysms.

To investigate the usefulness of super-resolution deep learning reconstruction (SR-DLR) with cardiac option in the assessment of image quality in patients with stent-assisted coil embolization, coil embolization, and flow-diverting stent placement compared with other image reconstructions. This single-centre retrospective study included 50 patients (mean age, 59 years; range, 44-81 years; 13 men) who were treated with stent-assisted coil embolization, coil embolization, and flow-diverting stent placement between January and July 2023. The images were reconstructed using filtered back projection (FBP), hybrid iterative reconstruction (IR), and SR-DLR. The objective image analysis included image noise in the Hounsfield unit (HU), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and full width at half maximum (FWHM). Subjectively, two radiologists evaluated the overall image quality for the visualization of the flow-diverting stent, coil, and stent. The image noise in HU in SR-DLR was 6.99 ± 1.49, which was significantly lower than that in images reconstructed with FBP (12.32 ± 3.01) and hybrid IR (8.63 ± 2.12) (P < .001). Both the mean SNR and CNR were significantly higher in SR-DLR than in FBP and hybrid IR (P < .001 and P < .001). The FWHMs for the stent (P < .004), flow-diverting stent (P < .001), and coil (P < .001) were significantly lower in SR-DLR than in FBP and hybrid IR. The subjective visual scores were significantly higher in SR-DLR than in other image reconstructions (P < .001). SR-DLR with cardiac option is useful for follow-up imaging in stent-assisted coil embolization and flow-diverting stent placement in terms of lower image noise, higher SNR and CNR, superior subjective image analysis, and less blooming artifact than other image reconstructions. SR-DLR with cardiac option allows better visualization of the peripheral and smaller cerebral arteries. SR-DLR with cardiac option can be beneficial for CT imaging of stent-assisted coil embolization and flow-diverting stent.

Performance analysis of fixed gain relay-assisted vertical UWOC systems with generalized misalignment fading model

Underwater wireless optical communication (UWOC) as a potential underwater transmission technology has attracted increasing attention due to its high speed, low delay, and wide range of applications. In addition to absorption and scattering, ocean turbulence fading and misalignment fading seriously affect system performance. Thus, the relay assistance methods have been presented as an effective technique for fading mitigation in optical channels. In this paper, we propose a fixed gain amplify-and-forward (AF) relay-assisted vertical UWOC system with a generalized misalignment fading model over multi-layer cascaded gamma-gamma turbulence channels. For the relay-assisted system, the cumulative distribution function (CDF) and the probability density function (PDF) of the end-to-end instantaneous signal-to-noise ratio (SNR) are derived with the help of the Meijer-G function and the bivariate Fox-H function. Subsequently, based on these statistical analyses of SNR, we derive the closed-form expressions of the outage probability and ergodic capacity. Furthermore, we provide a very tight asymptotic expression for the outage probability in terms of simple functions under high SNR, and the diversity order is analyzed. Finally, results from Monte Carlo simulations are used to validate our derived results. Numerical results for different relay position deployments and alignment error levels reveal that the relay-assisted vertical UWOC system performs better when the relay node is located near the source, while the increase of alignment error level can damage the system performance.

Programmable DNA Nanomachine Integrated with Electrochemically Controlled Atom Transfer Radical Polymerization for Antibody Detection at Picomolar Level.

The quantitative detection of antibodies is crucial for the diagnosis of infectious and autoimmune diseases, while the traditional methods experience high background signal noise and restricted signal gain. In this work, we have developed a highly efficient electrochemical biosensor by constructing a programmable DNA nanomachine integrated with electrochemically controlled atom transfer radical polymerization (eATRP). The sensor works by binding the target antidigoxin antibody (anti-Dig) to the epitope of the recognization probe, which then initiates the cascaded strand displacement reaction on a magnetic bead, leading to the capture of cupric oxide (CuO) nanoparticles through magnetic separation. After CuO was dissolved, the eATRP initiators were attached to the electrode based on the CuΙ-catalyzed azide-alkyne cycloaddition. The subsequent eATRP reaction results in the formation of long electroactive polymers (poly-FcMMA), producing an amplified current response for sensitive detection of anti-Dig. This method achieved a detection limit at clinically relevant picomolar concentration in human serum, offering a sensitive, convenient, and cost-effective tool for detecting various biomarkers in a wide range of applications.

Acceleration of uterine 3D T2-weighted imaging by compressed SENSE-a multicentre study.

To find the optimal acceleration factor (AF) of the compressed SENSE (CS) technique for uterine isotropic high-resolution 3D T2-weighted imaging (3D-ISO-T2WI). A total of 91 female volunteers from the First Affiliated Hospital of Dalian Medical University, Chu Hsien-I Memorial Hospital and Tianjin Institute of Endocrinology, and The Fourth Hospital of Harbin were recruited. A total of 44 volunteers received uterus sagittal 3D-ISO-T2WI scans on 3.0T MRI device with different CS AFs (including SENSE3, CS3, CS4, CS5, CS6, and CS7), 51 received 3D-ISO-T2WI scans with different degrees of fat suppression (none, light, moderate, and severe), while 4 volunteers received both series of scans. Image quality was subjectively evaluated with a 3-point scoring system. Junction zone signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and myometrial SNR were also calculated. Intraclass correlation coefficients were used to analyse the consistency of the measurement results by 2 observers. Analysis of variance test or Friedman rank sum test was used to compare the differences in subjective scores, SNR, and CNR under different AFs/different degrees of fat suppression. Images by AFs of CS3, CS4, and CS5 had the highest SNR and CNR. Among them, CS5 had the shortest scan time. CS5 also had one of the highest subjective scores. There was no significant difference in SNR and CNR among images acquired with different degrees of fat suppression. Also, images with moderate fat suppression had the highest subjective scores. The CS5 combined with moderate fat suppression is recommended for routine female pelvic 3D-ISO-T2WI scan. The CS5 has the highest image quality and has the shortest scan time, which is the best AF. Moderate fat suppression has the highest subjective scores. The CS5 and moderate fat suppression are the best combination for a female pelvic 3D-ISO-T2WI scan.

Estimation of static and dynamic functional connectivity in resting-state fMRI using zero-frequency resonator.

Resting-state functional magnetic resonance imaging (rs-fMRI) is increasingly being used to infer the functional organization of the brain. Blood oxygen level-dependent (BOLD) features related to spontaneous neuronal activity, are yet to be clearly understood. Prior studies have hypothesized that rs-fMRI is spontaneous event-related and these events convey crucial information about the neuronal activity in estimating resting state functional connectivity (FC). Attempts have been made to extract these temporal events using a predetermined threshold. However, the thresholding methods in addition to being very sensitive to noise, may consider redundant events or exclude the low-valued inflection points. Here, we extract the event-related temporal onsets from the rs-fMRI time courses using a zero-frequency resonator (ZFR). The ZFR reflects the transient behavior of the BOLD events at its output. The conditional rate (CR) of the BOLD events occurring in a time course with respect to a seed time course is used to derive static FC. The temporal activity around the estimated events called high signal-to-noise ratio (SNR) segments are also obtained in the rs-fMRI time course and are then used to compute static and dynamic FCs during rest. Coactivation pattern (CAP) is the dynamic FC obtained using the high SNR segments driven by the ZFR. The static FC demonstrates that the ZFR-based CR distinguishes the coactivation and non-coactivation scores well in the distribution. CAP analysis demonstrated the stable and longer dwell time dominant resting state functional networks with high SNR segments driven by the ZFR. Static and dynamic FC analysis underpins that the ZFR-driven temporal onsets of BOLD events derive reliable and consistent FCs in the resting brain using a subset of the time points.

Directly power system harmonics estimation using Equilibrium Optimizer

In power systems, the frequency values of current and voltage signals are expected to be at the fundamental frequency. Harmonic components may occur due to the effect of non-linear loads. Accurate estimation of harmonic components is important in determining the precautions to be taken. In this study, Equilibrium Optimizer (EO) based harmonic estimator has been proposed and applied to solve the harmonic and sub/inter-harmonic estimation problems. In addition to estimation amplitude, phase and frequency, unlike the literature, DC offset decaying values of power signals have been estimated with proposed method. Most common used synthetic stationary signals and dynamic signal with different noisy levels have been used in order to shown effectiveness. From the outputs, it has been seen that the proposed method gives high resolution results and has an impressive performance even if the parameters of the harmonics increase and/or the harmonics have high SNR levels. Moreover, obtained results indicate the EO-based estimator can robustly and accurately estimate all unknown or specified parameters of the harmonics simultaneously with high accuracy and higher resolution.

Phylogenomic analyses of ochrophytes (stramenopiles) with an emphasis on neglected lineages

Ochrophyta is a photosynthetic lineage that crowns the phylogenetic tree of stramenopiles, one of the major eukaryotic supergroups. Due to their ecological impact as a major primary producer, ochrophytes are relatively well-studied compared to the rest of the stramenopiles, yet their evolutionary relationships remain poorly understood. This is in part due to a number of missing lineages in large-scale multigene analyses, and an apparently rapid radiation leading to many short internodes between ochrophyte subgroups in the tree. These short internodes are also found across deep-branching lineages of stramenopiles with limited phylogenetic signal, leaving many relationships controversial overall. We have addressed this issue with other deep-branching stramenopiles recently, and now examine whether contentious relationships within the ochrophytes may be resolved with the help of filling in missing lineages in an updated phylogenomic dataset of ochrophytes, along with exploring various gene filtering criteria to identify the most phylogenetically informative genes. We generated ten new transcriptomes from various culture collections and a single-cell isolation from an environmental sample, added these to an existing phylogenomic dataset, and examined the effects of selecting genes with high phylogenetic signal or low phylogenetic noise. For some previously contentious relationships, we find a variety of analyses and gene filtering criteria consistently unite previously unstable groupings with strong statistical support. For example, we recovered a robust grouping of Eustigmatophyceae with Raphidophyceae-Phaeophyceae-Xanthophyceae while Olisthodiscophyceae formed a sister-lineage to Pinguiophyceae. Selecting genes with high phylogenetic signal or data quality recovered more stable topologies. Overall, we find that adding under-represented groups across different lineages is still crucial in resolving phylogenetic relationships, and discrete gene properties affect lineages of stramenopiles differently. This is something which may be explored to further our understanding of the molecular evolution of stramenopiles.

Improving intracranial aneurysms image quality and diagnostic confidence with deep learning reconstruction in craniocervical CT angiography.

The diagnostic impact of deep learning computed tomography (CT) reconstruction on intracranial aneurysm (IA) remains unclear. To quantify the image quality and diagnostic confidence on IA in craniocervical CT angiography (CTA) reconstructed with DEep Learning Trained Algorithm (DELTA) compared to the routine hybrid iterative reconstruction (HIR). A total of 60 patients who underwent craniocervical CTA and were diagnosed with IA were retrospectively enrolled. Images were reconstructed with DELTA and HIR, where the image quality was first compared in noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). Next, two radiologists independently graded the noise appearance, arterial sharpness, small vessel visibility, conspicuity of calcifications that may present in arteries, and overall image quality, each with a 5-point Likert scale. The diagnostic confidence on IAs of various sizes was also graded. Significantly lower noise and higher SNR and CNR were found on DELTA than on HIR images (all P < 0.05). All five subjective metrics were scored higher by both readers on the DELTA images (all P < 0.05), with good to excellent inter-observer agreement (κ = 0.77-0.93). DELTA images were rated with higher diagnostic confidence on IAs compared to HIR (P < 0.001), particularly for those with size ≤3 mm, which were scored 4.5 ± 0.6 versus 3.4 ± 0.8 and 4.4 ± 0.7 versus 3.5 ± 0.8 by two readers, respectively. The DELTA shows potential for improving the image quality and the associated confidence in diagnosing IA that may be worth consideration for routine craniocervical CTA applications.

Computed chest radiography for total body irradiation: image quality and clinical feasibility

Objective. In myeloablative total body irradiation (TBI), lung shielding blocks are used to reduce the dose to the lungs and hence decrease the risk of radiation pneumonitis. Some centers are still using mega-Volt (MV) imaging with dedicated silver halide-based films during simulation and treatment for lung delineation and position verification. However, the availability of these films has recently become an issue. This study examines the clinical performance of a computed radiography (CR) solution in comparison to radiographic films and potential improvement of image quality by filtering and post-processing. Approach. We compared BaFBrI-based CR plates to radiographic films. First, images of an aluminum block were analyzed to assess filter impact on scatter reduction. Secondly, a dedicated image quality phantom was used to assess signal linearity, signal-to-noise ratio (SNR), contrast and spatial resolution. Ultimately, a clinical performance study involving two impartial observers was conducted on an anthropomorphic chest phantom, employing visual grading analysis (VGA). Various filter materials and positions as well as post-processing were examined, and the workflow between CR and film was compared. Main results. CR images exhibited high SNR and linearity but demonstrated lower spatial and contrast resolution when compared to film. However, filtering improved contrast resolution and SNR, while positioning filters inside the cassette additionally enhanced sharpness. Image processing improved VGA scores, while additional filtering also resulted in higher spine visibility scores. CR shortened TBI simulation by over 10 minutes for one patient, alongside a dose reduction by order of 0.1 Gy. Significance. This study highlights potential advantages of shifting from conventional radiographic film to CR for TBI. Overall, CR with the incorporation of processing and filtering proves to be suitable for TBI chest imaging. When compared to radiographic film, CR offers advantages such as reduced simulation time and dose delivery, re-usability of image plates and digital workflow integration.

A fragment-based algorithm for identifying pan-cancer differential methylation markers from cfDNA.

e22507 Background: Methylation-based sequencing has been developed as a promising approach for Multi-Cancer Early Detection (MCED). Conventionally, cancer-specific methylation markers were identified by comparing paired tumor and adjacent normal tissues using traditional algorithm DSS (Dispersion shrinkage for sequencing data). However, the clinical applicability is limited by the unsatisfactory sensitivity in early-stage disease. Hence, we included blood specific markers derived from paired tissue and blood samples and developed a novel fragment-based algorithm in cfDNA with a better signal to noise ratio (SNR) than DSS to improve the sensitivity of the assay. Methods: A total of 391 plasma samples and 433 tissue samples from 7 cancer types were subjected to whole genome bisulfite sequencing (WGBS) (Table). The proportion of stage I-III and stage IV was 3.5:1 among 7 cancer types. The average sequencing depth was 30X. For each bin, the proportion of fully methylated (M) / un-methylated (U) fragments, defined with methylation level ≥85% / ≤15%, was computed individually. One-sided Wilcoxon rank sum test was employed to test whether cancer cfDNA have significantly (FDR &lt; 0.001) higher proportion of U/M fragments than control cfDNA. Results: To evaluate the performance of fragment-based algorithm in cfDNA, the false positive was assessed by resampling from healthy subjects. The healthy cfDNA were randomly assigned 20 times as pseudo case-control groups of 30 /170. The number of markers among healthy samples was recorded as NFalse Positive (NFP), while the number of markers between cancer and healthy samples was recorded as NPositive (NP). SNR was defined as (NP-NFP)/NFP. We calculated SNR using the fragment-based algorithm and achieved SNR of 135.2 for colorectal cancer, compared to SNR of 2.2 using DSS. The improved SNR was also reproduced in other cancer types. Concordance between plasma and tissue markers was 30.09%-95.92% (median 66.72%) among 7 cancer types, which was related to the proportion of ctDNA for each cancer type. Plasma markers which show weak signals in tissue are defined as blood-specific markers. These markers were enriched in immune-related pathways, suggesting a correlation to the immune response of cancer patients. Conclusions: We developed a novel fragment-based algorithm that achieves much higher SNR than DSS in cfDNA. We also identified blood-specific methylation markers which may improve the sensitivity of early cancer detection in clinical. [Table: see text]

Achievable secrecy rate analysis of OFDM‐based multi‐user untrusted relay networks with imperfect CSI

AbstractThis study investigates the impact of imperfect channel state information (CSI), caused by channel estimation errors and feedback delays, on the physical‐layer security performance of multi‐user relay networks. More specifically, a multi‐user untrusted half/full‐duplex (HD/FD) uni/bi‐directional wireless amplify‐and‐forward (AF) relay network is studied, adopting orthogonal frequency division multiplexing (OFDM). In order to minimize the information leakage, it is considered that a limited number of friendly jammers affect the untrusted relay. In this context, the achievable secrecy rate of the relay network is theoretically analyzed and optimization problems are formulated and solved for the untrusted relay's location and power allocation. Monte Carlo simulations are conducted to validate the theoretical findings, revealing that channel estimation errors cause coding gain losses for low signal‐to noise ratio (SNR) while their impact becomes negligible for high SNR. Also, a large number of user‐pairs achieves higher secrecy rate while FD relaying further improves the secrecy performance. Finally, bi‐directional relaying provides higher achievable secrecy rate over uni‐directional relaying.

ECG Signal Denoising Using 1D Convolutional Neural Network

Electrocardiogram (ECG) signals are crucial for monitoring cardiac activity and diagnosing various cardiovascular conditions. However, these signals are often contaminated by different types of noise, such as baseline wander, muscle artifacts, and power line interference, which can obscure critical information and hinder accurate diagnosis. This study used a 1-Dimensional Convolutional Neural Network (1D CNN) architecture with seven convolutional layers for denoising ECG signals. The model utilizes a fully convolutional autoencoder approach, comprising an encoder that transforms noisy input signals into compact feature representations and a decoder that reconstructs the cleaned signals. The proposed architecture was tested using the MIT-BIH Noise Stress Test Database, which includes ECG recordings with simulated noise conditions. Performance evaluation metrics such as Mean Squared Error (MSE), Signal-to-Noise Ratio (SNR), and Mean Absolute Deviation (MAD) were used to assess the model's effectiveness. Results showed a low MSE of 0.034, a high SNR of 15.8 dB, and a MAD of 0.754, indicating significant noise reduction and high-quality signal reconstruction. These findings demonstrate that the 1D CNN architecture effectively reduces various types of noise in ECG signals, thereby enhancing signal quality and facilitating more accurate analysis and diagnosis. The model's ability to maintain the integrity of crucial ECG features while removing noise suggests its potential utility in clinical applications for improving cardiovascular disease diagnosis

Enhancement of pH imaging of light addressable potentiometric sensor by colloidal spherical lens array

Visualizing the distribution of pH in a solution is an important basis for monitoring reaction processes and metabolic changes in micro-biochemical species. Light addressable potentiometric sensor (LAPS), as a simple structure, flexible imaging, low-cost and label-free electrochemical sensor, has been widely used to pH imaging. However, its spatiotemporal resolution is greatly affected by the frequency of modulation light, thus limiting its imaging performance. In this study, LAPS device modified by well-ordered and low-cost polystyrene (PS) colloidal monolayer as spherical lens array is proposed to obtain higher available modulation frequency of the maximum photoresponse. The morphology, optical properties and the electric field distribution of PS colloidal spherical lens array were tested and analyzed. pH sensing, imaging of uniform pH solution, the spatiotemporal pH imaging and dynamic imaging of enzymatic reactions of LAPS modified by PS colloidal spherical lens array were tested and evaluated. The experimental results show that LAPS modified by PS colloidal spherical lens array not only has acceptable pH sensing, but also widens the modulation frequency of the maximum photoresponse to 40 kHz. LAPS modified by colloidal spherical lens array modulated at the frequency of 40 kHz shows acceptable sensitivity and linearity, good reproducibility and stability, high SNR and high-spatiotemporal pH imaging capability. This work can be suitable for high-spatiotemporal monitoring of the changes in electrolyte environmental pH caused by the reaction and metabolism of the biochemical species.

Statistical spatial information based power optimization algorithm for multi-user massive MIMO systems

Power optimization has a significant role in multi-user massive multiple-input and multiple-output (mMIMO) wireless communication systems, whereas the acquisition of instantaneous channel state information (I-CSI) is a serious challenge in practical scenarios. The statistical spatial information (SPI), which contains angle-of-departures (AoDs) and corresponding large-scale fading factors, is slowly varying. The power optimization based on SPI to improve system performance needs to be explored for multi-user mMIMO systems. In this paper, two novel algorithms for SPI-based power optimization are proposed to maximize the ergodic sum-rate (ESR) under total power constraint for the system. Specifically, the minorization-maximization-based power optimization (MM-PO) algorithm and the closed-form power optimization (CF-PO) algorithm are proposed utilizing the minorization-maximization (MM) method and the fractional programming (FP) method, respectively. Moreover, the ESR is expressed based on the extracted SPI. The approximate expression of the ESR is derived. The complexity of the algorithms depends on the number of users, and the algorithms are scalable in the system. The proposed algorithms can effectively achieve the advantages of SPI while being robust to the uncertainty of I-CSI. Simulation results verify the ESR of our two proposed algorithms is 1.47 and 1.4 times higher than that of the equal power allocation algorithm at a high SNR when the number of antenna and user are equal to 64 and 20, respectively.

Combining Dipole and Loop Coil Elements for 7 T Magnetic Resonance Studies of the Human Calf Muscle.

Combining proton and phosphorus magnetic resonance spectroscopy offers a unique opportunity to study the oxidative and glycolytic components of metabolism in working muscle. This paper presents a 7 T proton calf coil design that combines dipole and loop elements to achieve the high performance necessary for detecting metabolites with low abundance and restricted visibility, specifically lactate, while including the option of adding a phosphorus array. We investigated the transmit, receive, and parallel imaging performance of three transceiver dipoles with six pair-wise overlap-decoupled standard or twisted pair receive-only coils. With a higher SNR and more efficient transmission decoupling, standard loops outperformed twisted pair coils. The dipoles with standard loops provided a four-fold-higher image SNR than a multinuclear reference coil comprising two proton channels and 32% more than a commercially available 28-channel proton knee coil. The setup enabled up to three-fold acceleration in the right-left direction, with acceptable g-factors and no visible aliasing artefacts. Spectroscopic phantom measurements revealed a higher spectral SNR for lactate with the developed setup than with either reference coil and fewer restrictions in voxel placement due to improved transmit homogeneity. This paper presents a new use case for dipoles and highlights their advantages for the integration in multinuclear calf coils.

Investigation in image quality and immediate patient safety using pre-dual-flow injection for low-contrast dose spectral pulmonary artery CT angiography

PurposeThe patient safety of iodine contrast-enhanced pulmonary artery CT angiography (CTPA) is widely concerned. This study aimed to investigate the image quality and immediate patient safety of spectral CTPA using a lower-contrast dose pre-dual-flow injection method. MethodsThis retrospective study included 120 patients with suspected pulmonary embolisms who received spectral CTPA between February and December 2022. Patients were divided into normal contrast injection (Group A, n=60) and pre-dual-flow group (Group B, n=60). CT values of pulmonary arteries (PAs) at different levels, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), arteriovenous separation performance, and beam hardening artifact (BHA) index of two sets of images were measured or calculated. The subjective image quality and immediate patient safety were also scored using the three-point method. ResultsGroup B had a contrast dose reduction by 42.5 % (60 vs. 34.5 mL). Radiation exposure dose was not statistically different between the two groups (P>0.05). CT values of different-level PAs on group B images were higher than those on group A images (P<0.05). Group B images had higher SNR and CNR, better arteriovenous separation between PA trunk and pulmonary vein, and lower BHA index on soft tissue and PA (all P<0.05). For subjective evaluation of image quality, group B had a better score in beam hardening artifact (P<0.05). For immediate patient safety, the score in comfortability was statistically higher in group B, with P<0.05. ConclusionsComparing with the normal injection method, pre-dual-flow spectral CTPA with a lower contrast dose injected results in better image quality and shows potential in patient-safety promotion.

Ultra-high resolution coronary CT angiography on photon-counting detector CT: bi-centre study on the impact of quantum iterative reconstruction on image quality and accuracy of stenosis measurements

PurposeTo assess the impact of different quantum iterative reconstruction (QIR) levels on objective and subjective image quality of ultra-high resolution (UHR) coronary CT angiography (CCTA) images and to determine the effect of strength levels on stenosis quantification using photon-counting detector (PCD)-CT. MethodA dynamic vessel phantom containing two calcified lesions (25 % and 50 % stenosis) was scanned at heart rates of 60, 80 and 100 beats per minute with a PCD-CT system. In vivo CCTA examinations were performed in 102 patients. All scans were acquired in UHR mode (slice thickness0.2 mm) and reconstructed with four different QIR levels (1–4) using a sharp vascular kernel (Bv64). Image noise, signal-to-noise ratio (SNR), sharpness, and percent diameter stenosis (PDS) were quantified in the phantom, while noise, SNR, contrast-to-noise ratio (CNR), sharpness, and subjective quality metrics (noise, sharpness, overall image quality) were assessed in patient scans. ResultsIncreasing QIR levels resulted in significantly lower objective image noise (in vitro and in vivo: both p < 0.001), higher SNR (both p < 0.001) and CNR (both p < 0.001). Sharpness and PDS values did not differ significantly among QIRs (all pairwise p > 0.008). Subjective noise of in vivo images significantly decreased with increasing QIR levels, resulting in significantly higher image quality scores at increasing QIR levels (all pairwise p < 0.001). Qualitative sharpness, on the other hand, did not differ across different levels of QIR (p = 0.15). ConclusionsThe QIR algorithm may enhance the image quality of CCTA datasets without compromising image sharpness or accurate stenosis measurements, with the most prominent benefits at the highest strength level.

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.

Accurate Blood Pressure Measurement Using Smartphone's Built-in Accelerometer

Efficient blood pressure (BP) monitoring in everyday contexts stands as a substantial public health challenge that has garnered considerable attention from both industry and academia. Commercial mobile phones have emerged as a promising tool for BP measurement, benefitting from their widespread popularity, portability, and ease of use. Most mobile phone-based systems leverage a combination of the built-in camera and LED to capture photoplethysmography (PPG) signals, which can be used to infer BP by analyzing the blood flow characteristics. However, due to low Signal-to-Noise (SNR), various factors such as finger motion, improper finger placement, skin tattoos, or fluctuations in environmental lighting can distort the PPG signal. These distortions consequentially affect the performance of BP estimation. In this paper, we introduce a novel sensing system that utilizes the built-in accelerometer of a mobile phone to capture seismocardiography (SCG) signals, enabling accurate BP measurement. Our system surpasses previous mobile phone-based BP measurement systems, offering advantages such as high SNR, ease of use, and power efficiency. We propose a triple-stage noise reduction scheme, integrating improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), recursive least squares (RLS) adaptive filter, and soft-thresholding, to effectively reconstruct high-quality heartbeat waveforms from initially contaminated raw SCG signals. Moreover, we introduce a data augmentation technique encompassing normalization coupled with temporal-sliding, effectively augmenting the diversity of the training sample set. To enable battery efficiency on smartphone, we propose a resource-efficient deep learning model that incorporates resource-efficient convolution, shortcut connections, and Huber loss. We conduct extensive experiments with 70 volunteers, comprising 35 healthy individuals and 35 individuals diagnosed with hypertension, under a user-independent setting. The excellent performance of our system demonstrates its capacity for robust and accurate daily BP measurement.