Sufficient Signal-to-noise Ratio Research Articles

Improving pulsed laser induced fluorescence distribution function analysis through matched filter signal processing.

Laser induced fluorescence is used to measure argon ion heating during magnetic reconnection in the PHase Space MApping experiment (PHASMA). Sufficient signal-to-noise ratio (SNR) of the processed signal with pulsed laser injection is a delicate balance between saturation of the absorption line and injecting enough laser power to overcome the spontaneous emission of the plasma at the fluorescence wavelength. Averaging over many laser pulses and integrating over the fluorescence lifetime improves the SNR of the processed signal (processed SNR) when the SNR of the laser pulse time series is small (pulse SNR), but for laser powers small enough to avoid saturation, averaging over hundreds of pulses is needed to obtain an appreciable processed SNR over the entire Doppler-broadened absorption line. Here, we describe a matched filter processing method that significantly improves the SNR of the final measurement with fewer shots averaged. Investigation of simulated measurements validated by experimental results suggests that the matched filter method provides up to a 20% improvement in the processed SNR, resulting in less uncertainty in distribution function fits.

Reliable Long-Term Serial Evaluation of Cochlear Function Using Pulsed Distortion-Product Otoacoustic Emissions: Analyzing Levels and Pressure Time Courses.

To date, there is no international standard on how to use distortion-product otoacoustic emissions (DPOAEs) in serial measurements to accurately detect changes in the function of the cochlear amplifier due, for example, to ototoxic therapies, occupational noise, or the development of regenerative therapies. The use of clinically established standard DPOAE protocols for serial monitoring programs appears to be hampered by multiple factors, including probe placement and calibration effects, signal-processing complexities associated with multiple sites of emission generation as well as suboptimal selection of stimulus parameters. Pulsed DPOAEs were measured seven times within 3 months for f2 = 1 to 14 kHz and L2 = 25 to 80 dB SPL in 20 ears of 10 healthy participants with normal hearing (mean age = 32.1 ± 9.7 years). L1 values were computed from individual optimal-path parameters derived from the corresponding individual DPOAE level map in the first test session. Three different DPOAE metrics for evaluating the functional state of the cochlear amplifier were investigated with respect to their test-retest reliability: (1) the interference-free, nonlinear-distortion component level (LOD), (2) the time course of the DPOAE-envelope levels, LDP(t), and (3) the squared, zero-lag correlation coefficient () between the time courses of the DPOAE-envelope pressures, pDP(t), measured in two sessions. The latter two metrics include the two main DPOAE components and their state of interference. Collated over all sessions and frequencies, the median absolute difference for LOD was 1.93 dB and for LDP(t) was 2.52 dB; the median of was 0.988. For the low (f2 = 1 to 3 kHz), mid (f2 = 4 to 9 kHz), and high (f2 = 10 to 14 kHz) frequency ranges, the test-retest reliability of LOD increased with increasing signal to noise ratio (SNR). On the basis of the knowledge gained from this study on the test-retest reliability of pulsed DPOAE signals and the current literature, we propose a DPOAE protocol for future serial monitoring applications that takes into account the following factors: (1) separation of DPOAE components, (2) use of individually optimal stimulus parameters, (3) SNR of at least 15 dB, (4) accurate pressure calibration, (5) consideration of frequency- and level-dependent test-retest reliabilities and corresponding reference ranges, and (6) stimulus levels L2 that are as low as possible with sufficient SNR to capture the nonlinear functional state of the cochlear amplifier operating at its highest gain.

Improved reconstruction for highly accelerated propeller diffusion 1.5T clinical MRI.

Propeller fast-spin-echo diffusion magnetic resonance imaging (FSE-dMRI) is essential for the diagnosis of Cholesteatoma. However, at clinical 1.5T MRI, its signal-to-noise ratio (SNR) remains relatively low. To gain sufficient SNR, signal averaging (number of excitations, NEX) is usually used with the cost of prolonged scan time. In this work, we leveraged the benefits of Locally Low Rank (LLR) constrained reconstruction to enhance the SNR. Furthermore, we enhanced both the speed and SNR by employing Convolutional Neural Networks (CNNs) for the accelerated PROPELLER FSE-dMRI on a 1.5T clinical scanner. Residual U-Net (RU-Net) was found to be efficient for propeller FSE-dMRI data. It was trained to predict 2-NEX images obtained by Locally Low Rank (LLR) constrained reconstruction and used 1-NEX images obtained via simplified reconstruction as the inputs. The brain scans from healthy volunteers and patients with cholesteatoma were performed for model training and testing. The performance of trained networks was evaluated with normalized root-mean-square-error (NRMSE), structural similarity index measure (SSIM), and peak SNR (PSNR). For 4 × under-sampled with 7 blades data, online reconstruction appears to provide suboptimal images-some small details are missing due to high noise interferences. Offline LLR enables suppression of noises and discovering some small structures. RU-Net demonstrated further improvement compared to LLR by increasing 18.87% of PSNR, 2.11% of SSIM, and reducing 53.84% of NRMSE. Moreover, RU-Net is about 1500 × faster than LLR (0.03 vs. 47.59s/slice). The LLR remarkably enhances the SNR compared to online reconstruction. Moreover, RU-Net improves propeller FSE-dMRI as reflected in PSNR, SSIM, and NRMSE. It requires only 1-NEX data, which allows a 2 × scan time reduction. In addition, its speed is approximately 1500 times faster than that of LLR-constrained reconstruction.

Highly Customizable 3D Microelectrode Arrays for In Vitro and In Vivo Neuronal Tissue Recordings.

Planar microelectrode arrays (MEAs) for - in vitro or in vivo - neuronal signal recordings lack the spatial resolution and sufficient signal-to-noise ratio (SNR) required for a detailed understanding of neural network function and synaptic plasticity. To overcome these limitations, a highly customizable three-dimensional (3D) printing process is used in combination with thin film technology and a self-aligned template-assisted electrochemical deposition process to fabricate 3D-printed-based MEAs on stiff or flexible substrates. Devices with design flexibility and physical robustness are shown for recording neural activity in different in vitro and in vivo applications, achieving high-aspect ratio 3D microelectrodes of up to 33:1. Here, MEAs successfully record neural activity in 3D neuronal cultures, retinal explants, and the cortex of living mice, thereby demonstrating the versatility of the 3D MEA while maintaining high-quality neural recordings. Customizable 3D MEAs provide unique opportunities to study neural activity under regular or various pathological conditions, both in vitro and in vivo, and contribute to the development of drug screening and neuromodulation systems that can accurately monitor the activity of large neural networks over time.

Time-resolved dynamic computational modeling of human EEG recordings reveals gradients of generative mechanisms for the MMN response.

Despite attempts to unify the different theoretical accounts of the mismatch negativity (MMN), there is still an ongoing debate on the neurophysiological mechanisms underlying this complex brain response. On one hand, neuronal adaptation to recurrent stimuli is able to explain many of the observed properties of the MMN, such as its sensitivity to controlled experimental parameters. On the other hand, several modeling studies reported evidence in favor of Bayesian learning models for explaining the trial-to-trial dynamics of the human MMN. However, direct comparisons of these two main hypotheses are scarce, and previous modeling studies suffered from methodological limitations. Based on reports indicating spatial and temporal dissociation of physiological mechanisms within the timecourse of mismatch responses in animals, we hypothesized that different computational models would best fit different temporal phases of the human MMN. Using electroencephalographic data from two independent studies of a simple auditory oddball task (n = 82), we compared adaptation and Bayesian learning models' ability to explain the sequential dynamics of auditory deviance detection in a time-resolved fashion. We first ran simulations to evaluate the capacity of our design to dissociate the tested models and found that they were sufficiently distinguishable above a certain level of signal-to-noise ratio (SNR). In subjects with a sufficient SNR, our time-resolved approach revealed a temporal dissociation between the two model families, with high evidence for adaptation during the early MMN window (from 90 to 150-190 ms post-stimulus depending on the dataset) and for Bayesian learning later in time (170-180 ms or 200-220ms). In addition, Bayesian model averaging of fixed-parameter models within the adaptation family revealed a gradient of adaptation rates, resembling the anatomical gradient in the auditory cortical hierarchy reported in animal studies.

Predicted effects of image acquisition and analysis conditions on DTMRI tractography-based muscle architecture estimates.

To quantify the effects of the intrinsic signal pattern, image acquisition conditions, and data analysis conditions on diffusion-tensor MRI (DTMRI) tractography-based muscle architecture estimates using a sampling-reconstruction assessment framework. Numerical models of muscles were constructed with realistic architectural properties. DTMRI signals were computed at signal-to-noise ratio (SNR) of 24-96 and common voxel sizes. Fiber tracking was performed, and the results were compared with the known architectural properties. SNR exerted the most significant impact on the outcome. The outcome variables approached asymptotes at SNR ≈ 54. Large in-plane voxel dimensions reduced the similarity between reconstructed fibers and the known architectural properties. Higher order polynomials helped reconstruct fibers with more complicated geometry but overfit noise for less complex geometries. The intrinsic fiber curvature also affected the robustness of polynomial smoothing to SNR. Other conditions, such as the fiber dimensionality, voxel aspect ratio, and slice thickness, did not affect the outcomes. SNR ≥ 54 is recommended for accurate muscle architecture characterization using DTMRI. Averaged across all simulated conditions, the greatest percent errors under SNR = 54 were -5.6% and -4.0% for the pennation angle and fiber-tract length estimates, respectively. For fiber tracts with intermediate intrinsic curvature, the greatest percent error for the curvature estimate was 9.8% for SNR = 54. Smaller in-plane voxel size (≤1.5 mm) is preferred to minimize the estimation error in architectural properties. If necessary, slice thickness may be adjusted within typical ranges to achieve sufficient SNR when slices are aligned near the fiber direction. Third-order polynomial fitting is appropriate for smoothing fiber tracts.

Denoising magnetic resonance spectroscopy (MRS) data using stacked autoencoder for improving signal-to-noise ratio and speed of MRS.

While magnetic resonance imaging (MRI) provides high resolution anatomical images with sharp soft tissue contrast, magnetic resonance spectroscopy (MRS) enables non-invasive detection and measurement of biochemicals and metabolites. However, MRS has low signal-to-noise ratio (SNR) when concentrations of metabolites are in the range of millimolar. Standard approach of using a high number of signal averaging (NSA) to achieve sufficient SNR comes at the cost of a long acquisition time. We propose to use deep-learning approaches to denoise MRS data without increasing NSA. This method has potential to reduce the acquisition time as well as improve SNR and quality of spectra, which could enhance the diagnostic value and broaden the clinical applications of MRS. The study was conducted using data collected from the brain spectroscopy phantom and human subjects. We utilized a stack auto-encoder (SAE) network to train deep learning models for denoising low NSA data (NSA=1, 2, 4, 8, and 16) randomly truncated from high SNR data collected with high NSA (NSA=192), which were also used to obtain the ground truth. We applied both self-supervised and fully-supervised training approaches and compared their performance of denoising low NSA data based on improvement in SNR. To prevent overfitting, the SAE network was trained in a patch-based manner. We then tested the denoising methods on noise-containing data collected from the phantom and human subjects, including data from brain tumor patients. We evaluated their performance by comparing the SNR levels and mean squared errors (MSEs) calculated for the whole spectra against high SNR "ground truth", as well as the value of chemical shift of N-acetyl-aspartate (NAA) before and after denoising. With the SAE model, the SNR of low NSA data (NSA=1) obtained from the phantom increased by 28.5% and the MSE decreased by 42.9%. For low NSA data of the human parietal and temporal lobes, the SNR increased by 32.9% and the MSE decreased by 63.1%. In all cases, the chemical shift of NAA in the denoised spectra closely matched with the high SNR spectra without significant distortion to the spectra after denoising. Furthermore, the denoising performance of the SAE model was more effective in denoising spectra with higher noise levels. The reported SAE denoising method is a model-free approach to enhance the SNR of MRS data collected with low NSA. With the denoising capability, it is possible to acquire MRS data with a few NSA, shortening the scan time while maintaining adequate spectroscopic information for detecting and quantifying the metabolites of interest. This approach has the potential to improve the efficiency and effectiveness of clinical MRS data acquisition by reducing the scan time and increasing the quality of spectroscopic data.

An Improved Multi-Frame Coherent Integration Algorithm for Heterogeneous Radar

This paper proposes an improved multi-frame coherent integration algorithm to improve the detection performance of weak targets in heterogeneous radar. In the detection of weak targets, integration within a single frame may fail to provide sufficient signal-to-noise ratio (SNR) gain. In this case, multi-frame coherent integration is an effective solution. However, radar parameters may be different across frames (i.e., heterogeneous radar) in some practical situations, leading to a mismatch of Doppler frequencies and the fixed phases, which poses difficulties to multi-frame coherent integration. To calibrate the ranges and Doppler frequencies of heterogenous multi-frame echoes, this paper firstly employs an improved Keystone Transform (KT). Compared to conventional KT, the improved KT aligns inter-frame carrier frequencies by applying varying degrees of slow-time rescaling based on the carrier frequencies of each frame, and aligns inter-frame Pulse Repetition Frequencies (PRF) through a unified global slow-time resampling. Secondly, this paper derives the explicit expressions of the fixed-phase terms and adopts a method based on fractional range bins, thus achieving explicit compensation for mismatched phases. Finally, heterogenous multi-frame coherent integration is achieved through slow-time fast Fourier transform. The effectiveness of the proposed algorithm is validated by simulation analyses. Compared to existing entropy-based methods, the proposed algorithm demonstrates higher robustness and lower computational complexity, making it more effective in detecting weak targets under low SNR conditions.

A Comprehensive Noise Model for Wideband and High Frame Rate Electrical Impedance Tomography Systems

Clinical applications call for comprehensive performances of electrical impedance tomography (EIT) system, including sufficient signal-to-noise ratio (SNR), high frame-per-second (Fps), and wide bandwidth. In this article, thorough analyses of switching noise in an EIT system are presented. The energy of switching noise is split into two parts: spurs and extra quantization noise of analog-to-digital converter (ADC). Based on the characteristics of switching noise, a comprehensive noise model of EIT systems with digital matched filter, is proposed. The impacts of switching noise, frame rate, and bandwidth of an EIT system are considered in the noise model. Simulation and experiment conducted in a laboratory-built EIT system are employed to validate the noise model. Test results show that the EIT system can obtain an average SNR greater than 94dB with a frame rate of 31Fps and a bandwidth of 2MHz, and an average SNR greater than 90dB with a frame rate of 31 Fps and a bandwidth of 10MHz, under the guidance of the proposed model. Overall, this work provides a novel and comprehensive noise model by including the impacts of switching noise, frame rate, and bandwidth, which can serve as a guide to improve the comprehensive performance of an EIT system for clinical applications.

Pulsed stimulated Brillouin microscopy.

Stimulated Brillouin scattering is an emerging technique for probing the mechanical properties of biological samples. However, the nonlinear process requires high optical intensities to generate sufficient signal-to-noise ratio (SNR). Here, we show that the SNR of stimulated Brillouin scattering can exceed that of spontaneous Brillouin scattering with the same average power levels suitable for biological samples. We verify the theoretical prediction by developing a novel scheme using low duty cycle, nanosecond pulses for the pump and probe. A shot noise-limited SNR over 1000 was measured with a total average power of 10 mW for 2 ms or 50 mW for 200 µs integration on water samples. High-resolution maps of Brillouin frequency shift, linewidth, and gain amplitude from cells in vitro are obtained with a spectral acquisition time of 20 ms. Our results demonstrate the superior SNR of pulsed stimulated Brillouin over spontaneous Brillouin microscopy.

Deep learning-based protoacoustic signal denoising for proton range verification

Proton therapy is a type of radiation therapy that can provide better dose distribution compared to photon therapy by delivering most of the energy at the end of range, which is called the Bragg peak (BP). The protoacoustic technique was developed to determine the BP locations in vivo, but it requires a large dose delivery to the tissue to obtain a high number of signal averaging (NSA) to achieve a sufficient signal-to-noise ratio (SNR), which is not suitable for clinical use. A novel deep learning-based technique has been proposed to denoise acoustic signals and reduce BP range uncertainty with much lower doses. Three accelerometers were placed on the distal surface of a cylindrical polyethylene (PE) phantom to collect protoacoustic signals. In total, 512 raw signals were collected at each device. Device-specific stack autoencoder (SAE) denoising models were trained to denoise the noise-containing input signals, which were generated by averaging only 1, 2, 4, 8, 16, or 24 raw signals (low NSA signals), while the clean signals were obtained by averaging 192 raw signals (high NSA). Both supervised and unsupervised training strategies were employed, and the evaluation of the models was based on mean squared error (MSE), SNR, and BP range uncertainty. Overall, the supervised SAEs outperformed the unsupervised SAEs in BP range verification. For the high accuracy detector, it achieved a BP range uncertainty of 0.20 ± 3.44 mm by averaging over 8 raw signals, while for the other two low accuracy detectors, they achieved the BP uncertainty of 1.44 ± 6.45 mm and −0.23 ± 4.88 mm by averaging 16 raw signals, respectively. This deep learning-based denoising method has shown promising results in enhancing the SNR of protoacoustic measurements and improving the accuracy in BP range verification. It greatly reduces the dose and time for potential clinical applications.

Learning-based super-resolution interpolation for sub-Nyquist sampled laser speckles

Information retrieval from visually random optical speckle patterns is desired in many scenarios yet considered challenging. It requires accurate understanding or mapping of the multiple scattering process, or reliable capability to reverse or compensate for the scattering-induced phase distortions. In whatever situation, effective resolving and digitization of speckle patterns are necessary. Nevertheless, on some occasions, to increase the acquisition speed and/or signal-to-noise ratio (SNR), speckles captured by cameras are inevitably sampled in the sub-Nyquist domain via pixel binning (one camera pixel contains multiple speckle grains) due to finite size or limited bandwidth of photosensors. Such a down-sampling process is irreversible; it undermines the fine structures of speckle grains and hence the encoded information, preventing successful information extraction. To retrace the lost information, super-resolution interpolation for such sub-Nyquist sampled speckles is needed. In this work, a deep neural network, namely SpkSRNet, is proposed to effectively up sample speckles that are sampled below 1/10 of the Nyquist criterion to well-resolved ones that not only resemble the comprehensive morphology of original speckles (decompose multiple speckle grains from one camera pixel) but also recover the lost complex information (human face in this study) with high fidelity under normal- and low-light conditions, which is impossible with classic interpolation methods. These successful speckle super-resolution interpolation demonstrations are essentially enabled by the strong implicit correlation among speckle grains, which is non-quantifiable but could be discovered by the well-trained network. With further engineering, the proposed learning platform may benefit many scenarios that are physically inaccessible, enabling fast acquisition of speckles with sufficient SNR and opening up new avenues for seeing big and seeing clearly simultaneously in complex scenarios.

High-frequency ultrasound point-of-care device to quantify myopia-induced microstructural changes in the anterior sclera.

To develop a point-of-care (POC) device using high-frequency ultrasound (US) for evaluating microstructural changes in the anterior sclera associated with myopia. The proposed POC device must satisfy four primary requirements for effective clinical use: the measurement component is handheld; the software must be simple and provide real-time feedback; patient safety and health data security requirements set forth by relevant governing bodies must be satisfied and the measurement data must have sufficient signal-to-noise ratio (SNR) and repeatability. Radiofrequency (RF) echo data acquired by the POC device will be processed using our quantitative US methods to characterise tissue microstructure and biomechanical properties. All stated requirements have been met in the developed POC device. The high-frequency transducer is housed in a custom, 3D-printed, pen-like holder that allows for easy measurements of the anterior sclera. Custom software provides a simple interface for data acquisition, real-time data display and secure data storage. Exposimetry measurements of the US pressure field indicate device compliance with United States Food and Drug Administration limits for ophthalmic US. In vivo measurements on a volunteer suggest the RF data SNR and acquisition consistency are suitable for quantitative analysis. A fully functioning POC device using high-frequency US has been created for evaluating the microstructure of the anterior sclera. Planned studies using the POC device to scan the eyes of myopia patients will help clarify how the anterior sclera microstructure may be affected by myopia. If effective, this portable, inexpensive and user-friendly system could be an important part of routine eye examinations.

Hierarchical Bayesian method for constraining the neutron star equation of state with an ensemble of binary neutron star postmerger remnants

Binary neutron star (BNS) post-merger gravitational-wave emission can occur in the aftermath of a BNS merger -- provided the system avoids prompt collapse to a black hole -- as a quasistable hypermassive remnant experiences quadrupolar oscillations and non-axisymmetric deformations. The post-merger gravitational-wave spectrum possesses a characteristic peak frequency that has been shown to be dependent on the binary chirp mass and the neutron star equation of state (EoS), rendering post-merger gravitational waves a powerful tool for constraining neutron star composition. Unfortunately, the BNS post-merger signal is emitted at high ($\gtrsim 1.5$ kHz) frequencies, where ground-based gravitational wave detectors suffer from reduced sensitivity. It is therefore unlikely that post-merger signals will be detected with sufficient signal-to-noise ratio (SNR) until the advent of next-generation detectors. However, by employing empirical relations derived from numerical relativity simulations, we can combine information across an ensemble of BNS mergers, allowing us to obtain EoS constraints with many low-SNR signals. We present a hierarchical Bayesian method for deriving constraints on $R_{1.6}$, the radius of a 1.6$\mathrm{M_{\odot}}$ neutron star, through an ensemble analysis of binary neutron star mergers. We apply this method to simulations of the next two LIGO-Virgo-KAGRA observing runs, O4 and O5, as well as an extended 4-year run at A+ sensitivity, demonstrating the potential of our approach to yield EoS information from the post-merger signal with current-generation detectors. The A+ 4-year scenario is predicted to improve the constraint on $R_{1.6}$ from the currently available multimessenger-based 95\% credible interval (C.I.) uncertainty of $R_{1.6}=12.07^{+0.98}_{-0.77}$ km to $R_{1.6}=11.91^{+0.80}_{-0.56}$ km, a 22% reduction of the 95% C.I. width.

Usability of magnetic resonance images acquired at a novel low-field 0.55T scanner for brain radiotherapy treatment planning.

Low-field magnetic resonance imaging (MRI) may offer specific advantages over high-field MRI, e.g. lower susceptibility-dependent distortions and simpler installation. The study aim was to evaluate if a novel 0.55T MRI scanner provides sufficient image accuracy and quality for radiotherapy (RT) treatment planning. The geometric accuracy of images acquired at a low-field MRI scanner was evaluated in phantom measurements regarding gradient non-linearity-related distortions. Patient-induced B0-susceptibility changes were investigated via B0-field-mapping in ten volunteers. Patients were positioned in RT-setup using a 3D-printed insert for the head/neck-coil that was tested for sufficient signal-to-noise-ratio (SNR). The suitability of the MRI-system for detection of metastases was evaluated in eleven patients. In comparison to diagnostic images, acquired at ≥1.5T, three physicians evaluated the detectability of metastases by counting them in low- and high-field-images, respectively. The phantom measurements showed a high imaging fidelity after 3D-distortion-correction with (1.2±0.9) mm geometric distortion in 10cm radius from isocentre. At the edges remaining distortions were greater than at 1.5T. The mean susceptibility-induced distortions in the head were (0.05±0.05) mm and maximum 0.69mm. SNR analysis showed that optimised positioning of RT-patients without signal loss in the head/neck-coil was possible with the RT-insert. No significant differences (p=0.48) in detectability of metastases were found. The 0.55T MRI system provided sufficiently geometrically accurate and high-resolution images that can be used for RT-planning for brain metastases. Hence, modern low-field MRI may contribute to simply access MRI for RT-planning after further investigations.

MRI: Evaluating the Application of FOCUS-MUSE Diffusion-Weighted Imaging in the Pancreas in Comparison With FOCUS, MUSE, and Single-Shot DWIs.

Diffusion-weighted imaging (DWI) is a useful technique to detect pancreatic lesion. In DWIs, field-of-view optimized and constrained undistorted single-shot (FOCUS) can improve the spatial resolution and multiplexed sensitivity-encoding (MUSE) can gain a high signal-to-noise ratio (SNR). Based on the advantage of FOCUS and MUSE, a new DWI sequence-named FOCUS-MUSE DWI (FOCUS combined with MUSE)-was developed to delineate the pancreas. To investigate the reliability of FOCUS-MUSE DWI compared to FOCUS, MUSE and single-shot (SS) DWI via the systematical evaluation of the apparent diffusion coefficient (ADC) measurements, SNR and image quality. Prospective. A total of 33 healthy volunteers and 9 patients with pancreatic lesion. A 3.0 T scanner. FOCUS-MUSE DWI, FOCUS DWI, MUSE DWI, SS DWI. For volunteers, ADC and SNR were measured by two readers in the pancreatic head, body, and tail. For all subjects, the diagnostic image quality score was assessed by three other readers on above four DWIs. Paired-sample T-test, intraclass correlation (ICC), Bland-Altman method, Friedman test, Dunn-Bonferroni post hoc test and kappa coefficient. A significance level of 0.05 was used. FOCUS-MUSE DWI had the best intersession repeatability of ADC measurements (head: 59.53, body: 101.64, tail: 42.30) among the four DWIs, and also maintained the significantly highest SNR (reader 1 [head: 19.68 ± 3.23, body: 23.42 ± 5.00, tail: 28.85 ± 4.96], reader 2 [head: 19.93 ± 3.52, body: 23.02 ± 5.69, tail: 29.77 ± 6.33]) except for MUSE DWI. Furthermore, it significantly achieved better image quality in volunteers (median value: 4 score) and 9 patients (most in 4 score). FOCUS-MUSE DWI improved the reliability of pancreatic images with the most stable ADC measurement, best image quality score and sufficient SNR among four DWIs. 2 TECHNICAL EFFICACY: Stage 2.

Optimal Power Splitting of Wireless Information and Power Transmission Using a Novel Dual-Channel Rectenna

Traditionally, the signal power is divided equally between dual functions in wireless information and power transmission (WIPT), which, intuitively, is not an optimal solution since the power sensitivities for communication and charging node pose different constraints for signals. To address this challenge, a two-channel rectenna with an asymmetrical coupler feeding network (ACFN) has been proposed for WIPT under the power-splitting and time-sharing (PS and TS) schemes. The proposed two-port rectenna consists of a receiving antenna integrated with ACFN where one output port is connected to a rectifying circuit, whereas the other is used for information recovery. Different from the conventional PS scheme with equal division, the proposed rectenna can adjust ratios of power at two outputs. That is, routing high power to the radio frequency (RF)-dc port, and transferring low power but of a sufficient signal-to-noise-ratio (SNR) to the port being used for signal demodulation/decoding. As a result, the simulated/measured maximum RF-dc conversion efficiency of the rectenna can reach up to 73.9%/70.4% for quadrature phase shift keying (QPSK) modulated signals and 63.9%/60.7% for continuous wave (CW) signals. In addition, good isolation between the two output ports ensures low interference against information decoding.

Fiducial visibility on planar images during motion-synchronized tomotherapy treatments

Objective. Synchrony® is a motion management system on the Radixact® that uses planar kV radiographs to locate the target during treatment. The purpose of this work is to quantify the visibility of fiducials on these radiographs. Approach. A custom acrylic slab was machined to hold 8 gold fiducials of various lengths, diameters, and orientations with respect to the imaging axis. The slab was placed on the couch at the imaging isocenter and planar radiographs were acquired perpendicular to the custom slab with varying thicknesses of acrylic on each side. Fiducial signal to noise ratio (SNR) and detected fiducial position error in millimeters were quantified. Main Results. The minimum output protocol (100 kVp, 0.8 mAs) was sufficient to detect all fiducials on both Radixact configurations when the thickness of the phantom was 20 cm. However, no fiducials for any protocol were detected when the phantom was 50 cm thick. The algorithm accurately detected fiducials on the image when the SNR was larger than 4. The MV beam was observed to cause RFI artifacts on the kV images and to decrease SNR by an average of 10%. Significance. This work provides the first data on fiducial visibility on kV radiographs from Radixact Synchrony treatments. The Synchrony fiducial detection algorithm was determined to be very accurate when sufficient SNR is achieved. However, a higher output protocol may need to be added for use with larger patients. This work provided groundwork for investigating visibility of fiducial-free solid targets in future studies and provided a direct comparison of fiducial visibility on the two Radixact configurations, which will allow for intercomparison of results between configurations.

Efficient implementation of x-ray ghost imaging based on a modified compressive sensing algorithm

Towards efficient implementation of x-ray ghost imaging (XGI), efficient data acquisition and fast image reconstruction together with high image quality are preferred. In view of radiation dose resulted from the incident x-rays, fewer measurements with sufficient signal-to-noise ratio (SNR) are always anticipated. Available methods based on linear and compressive sensing algorithms cannot meet all the requirements simultaneously. In this paper, a method based on a modified compressive sensing algorithm with conjugate gradient descent method (CGDGI) is developed to solve the problems encountered in available XGI methods. Simulation and experiments demonstrate the practicability of CGDGI-based method for the efficient implementation of XGI. The image reconstruction time of sub-second implicates that the proposed method has the potential for real-time XGI.

A Modified Noise Model of Electrical Impedance Tomography System by Considering Colored Noises

Sufficient signal-to-noise ratio (SNR) is critical to guarantee the satisfactory performance of an electrical impedance tomography (EIT) system. In this article, a thorough SNR analysis of EIT systems is presented. Besides the well-known random noise, the influences of colored noises, such as switching noise, low-frequency noise, and harmonic noise, are considered. By investigating the colored noises, a novel noise model of electrical impedance tomography systems is first proposed, which is validated by both simulation and experiment conducted in a laboratory-built electrical impedance tomography system. Finally, different conventional methods to reduce the colored noises are discussed and associated circuit-level considerations are evaluated by applying a front-end isolation structure in the laboratory-built EIT system. Overall, this work provides a more practical noise model of electrical impedance tomography systems by including the colored noises. It can serve as a guide to improve the SNR and therefore the performance of an electrical impedance tomography system for high-demanding biomedical applications.