Deviation Of Noise Research Articles

Adjoint model for estimating material parameters based on microstructure evolution during spinodal decomposition

Data assimilation techniques are attracting increasing attention because they enable researchers to estimate material parameters by integrating an advanced computational model with experimental data of microstructure evolution. In this study, we develop an adjoint model to integrate a phase-field model for spinodal decomposition with time-series measurement data of compositional field maps to estimate the unknown parameters in the phase-field model. As a case study, simultaneous estimation of six parameters (Gibbs energy parameters, gradient energy coefficient, etc.) in the phase-field model is considered. To confirm the effectiveness of the developed adjoint model, numerical tests called ``twin experiments'' are conducted using synthetic measurement data prepared in advance through phase-field simulation. In the twin experiments, the optimum estimates of six model parameters of interest are shown to coincide with true values, indicating that several model parameters can be successfully estimated. Furthermore, the effects of the standard deviation of measurement noise $(\ensuremath{\sigma})$ and the time interval of measurements $(\mathrm{\ensuremath{\Delta}}{t}_{\mathrm{meas}.}^{\ensuremath{'}})$ on uncertainties of optimum estimates of parameters $({\ensuremath{\sigma}}^{\mathrm{est}})$ are examined by conducting twin experiments. It is shown that there is a positive correlation between $\ensuremath{\sigma}$ and ${\ensuremath{\sigma}}^{\mathrm{est}}$ or between $\mathrm{\ensuremath{\Delta}}{t}_{\mathrm{meas}.}^{\ensuremath{'}}$ and ${\ensuremath{\sigma}}^{\mathrm{est}}$, which is essential information for designing experiments to estimate model parameters. The developed adjoint model is assumed to be useful for estimating unknown parameters (e.g., Gibbs energy parameters of a nonequilibrium phase) using time-series measurement data of microstructure evolution during spinodal decomposition.

Assessment of Noise Signature for a Cavitating Centrifugal Pump

AbstractIn recent days, sophisticated instruments have emerged to obtain an online measurement of performance parameters from centrifugal pump of different kinds and the signals can be directed to the hands of pump users through mobile applications. With this in mind, a centrifugal pump of low specific speed was chosen for cavitation studies from 80% to 120% of nominal flowrate and for three different speeds. An assessment was carried out for cavitation noise signature from those operating conditions of that pump. The result of cavitation noise based on peak magnitude as well as average revealed nature in relation to cavitation coefficient, and it greatly depends on the flowrate with respect to nominal flowrate. The noise envelope for the flowrate at best efficiency and above was having a similar trend whereas at flows less than the nominal, it was totally different. So the criteria for finding the deviation in noise cannot be uniform for all flowrates. In this paper, the method adapted was to impose a trend line to the measured cavitation noise information and to find out the deviation with respect to normal operating conditions. It was concluded that detection of abnormality in pumps due to cavitation effects requires the current operating condition to be diagnosed first and then proper criteria for deviation in noise has to be imposed.

Оценка рекомендуемых и допустимых значений показателей изобразительного качества по материалам, полученным различными аэрофотосъемочными системами для целей картографирования

The results of fine quality indicators assessment for images, obtained with various aerial surveying systems for mapping purposes are presented. The values of parameters that characterize technical conditions, technical parameters of airborne imaging and technologic settings for imagery postprocessing, natural conditions of aerial surveying are determined. The author presents a comparative analysis of fine quality indicators assessment both for photos, the quality of which was found to be satisfactory as production technical control results and those rejected due to some relevant indicators (haze, blurring, etc) and accepted unsuitable for creating cartographic products based on them. It is shown, that some quality indicators, such as spatial resolution, sharpness and the degree (standard deviation) of random noise, are advisable to be determined at performing factory calibration of aerial cameras and included in certificate as well as other calibration parameters as metrological characteristics of the obtained imagery. Acceptable values of fine quality indicators, obtained during joint analysis of previous theoretical researches results and experimental verification, based on images, obtained by various aerial surveying systems for mapping purposes were generalized and recommended during the research.

A path planning model based on nested regular hexagons using weighted centroid localization

SummaryIn many wireless sensor network (WSN) applications, the location of a sensor node is crucial for determining where the event or situation of interest occurred. Therefore, localization is one of the critical challenges in WSNs. Mobile anchor node assisted localization (MANAL) is one of the promising solutions for the localization of statically deployed sensors. The main problem in MANAL localization is that the path planning of the mobile anchor (MA) node should be done so that the localization error in the network will be minimal and that all unknown nodes in the network are covered. This paper proposes a new path planning approach called nested hexagons curves (NHexCurves) for MANAL. NHexCurves guarantees that it will receive messages from at least three non‐collinear anchors to locate all unknown nodes in the network. The proposed model has compared six different path planning schemes in the literature using weighted centroid localization (WCL). In these comparisons, first of all, localization errors of the models are compared using some statistical concepts. Second, the variation of the localization error according to parameters such as resolution (R) and the standard deviation of noise (σ) is observed. Then, with similar approaches, the standard deviation of errors, localization ratio, scalability performances, and finally, path lengths of the models are examined. The simulation results show that the NHexCurves static path planning model proposed in this study stands out compared to other models with high localization error and localization ratio performance, especially at low resolutions, due to its path design. At the same time, the lowest error values according to σ are obtained with the proposed model among all models considered.

Metal Artifact Reduction With Tin Prefiltration in Computed Tomography: A Cadaver Study for Comparison With Other Novel Techniques.

With the aging population and thus rising numbers of orthopedic implants (OIs), metal artifacts (MAs) increasingly pose a problem for computed tomography (CT) examinations. In the study presented here, different MA reduction techniques (iterative metal artifact reduction software [iMAR], tin prefilter technique, and dual-energy CT [DECT]) were compared. Four human cadaver pelvises with OIs were scanned on a third-generation DECT scanner using tin prefilter (Sn), dual-energy (DE), and conventional protocols. Virtual monoenergetic CT images were generated from DE data sets. Postprocessing of CT images was performed using iMAR. Qualitative (bony structures, MA, image noise) image analysis using a 6-point Likert scale and quantitative image analysis (contrast-to-noise ratio, standard deviation of background noise) were performed by 2 observers. Statistical testing was performed using Friedman test with Nemenyi test as a post hoc test. The iMAR Sn 150 kV protocol provided the best overall assessability of bony structures and the lowest subjective image noise. The iMAR DE protocol and virtual monochromatic image (VMI) ± iMAR achieved the most effective metal artifact reduction (MAR) (P < 0.05 compared with conventional protocols). Bony structures were rated worse in VMI ± iMAR (P < 0.05) than in tin prefilter protocols ± iMAR. The DE protocol ± iMAR had the lowest contrast-to-noise ratio (P < 0.05 compared with iMAR standard) and the highest image noise (P < 0.05 compared with iMAR VMI). The iMAR reduced MA very efficiently. When considering MAR and image quality, the iMAR Sn 150 kV protocol performed best overall in CT images with OI. The iMAR generated new artifacts that impaired image quality. The DECT/VMI reduced MA best, but experienced from a lack of resolution of bony fine structures.

Adaptive Real-Time Wavelet Denoising Architecture Based on Feedback Control Loop

The discrete wavelet transform is commonly used as a denoising step for many applications, like biomedical applications which are usually suffering from low SNR of the recorded signal. However, the choice of appropriate threshold value for DWT coefficients plays significant role in reconstructing the denoised signal. This paper presents a design of real-time wavelet denoising architecture which is suitable for wide range of real-time denoising applications. In this design, an adaptive thresholding approach based on feedback control loop is proposed to make the architecture more applicable for real-time wavelet denoising. This thresholding method considers a noise level estimator module based on first detail coefficients level 𝑑1 to calculate the unknown standard deviation of background noise. The proposed architecture is developed using MATLAB to simulate the suggested denoising method. The performance of the proposed denoising method is studied in terms of integral gain 𝐺 of feedback control and window size 𝑀 with respect to the improvement in SNR and settling time. The results imply that the proposed denoising architecture is suitable for real-time denoising applications with acceptable improvement in SNR approximately 8 dB.

Image Denoising Using Sparse Representation and Principal Component Analysis

This study proposes an image denoising algorithm based on sparse representation and Principal Component Analysis (PCA). The proposed algorithm includes the following steps. First, the noisy image is divided into overlapped [Formula: see text] blocks. Second, the discrete cosine transform is applied as a dictionary for the sparse representation of the vectors created by the overlapped blocks. To calculate the sparse vector, the orthogonal matching pursuit algorithm is used. Then, the dictionary is updated by means of the PCA algorithm to achieve the sparsest representation of vectors. Since the signal energy, unlike the noise energy, is concentrated on a small dataset by transforming into the PCA domain, the signal and noise can be well distinguished. The proposed algorithm was implemented in a MATLAB environment and its performance was evaluated on some standard grayscale images under different levels of standard deviations of white Gaussian noise by means of peak signal-to-noise ratio, structural similarity indexes, and visual effects. The experimental results demonstrate that the proposed denoising algorithm achieves significant improvement compared to dual-tree complex discrete wavelet transform and K-singular value decomposition image denoising methods. It also obtains competitive results with the block-matching and 3D filtering method, which is the current state-of-the-art for image denoising.

Parameter Resolution of the Estimation Methods for Power Law Indices

The accuracy of parameter estimation plays an important role in economic and social models and experiments. Parameter resolution is the capability of an estimation algorithm to distinguish different parameters effectively under given noise level, which can be used to select appropriate algorithm for experimental or empirical data. We use a flexible distinguishing criterion and present a framework to compute the parameter resolution by bootstrap and simulation, which can be used in different models and algorithms, even for non-Gaussian noises. The parameter resolutions are computed for power law models and corresponding algorithms. For power law signal, with the increase of SNR, parameter resolution is finer; with the decrease of parameter, the resolution is finer. The standard deviation of noise and parameter resolution satisfies the linear relation; it relates to interval estimation naturally if the estimation algorithm is asymptotically normal. For power law distribution, parameter and resolution satisfy the linear relation, and experimental slope and theoretical slope tend to be consistent when significance level approaches zero. Last, we select an algorithm with finer resolution to estimate the Pareto index for the Forbes list of global rich data in recent 10 years and analyze the changes in the gap between the rich and the poor.

RANDOM NOISE ASSESSMENT IN AERIAL AND SATELLITE IMAGES

Abstract. Random noise in aerial and satellite images is one of the factors, decreasing their quality. The noise level assessment in images is paid not enough attention. The method of numerical estimation of random image noise is considered. The object of the study is the image noise estimating method, based on harmonic analysis. The capability of using this method for aerial and satellite image quality assessment is considered. The results of the algorithm testing on model data and on real satellite images with different terrain surfaces are carried out. The accuracy estimating results for calculating the root-mean-square deviation (RMS) of random image noise by the harmonic analysis method are shown.

DDPG Algoritmasında Bulunan Ornstein–Uhlenbeck Gürültüsünün Standart Sapmasının Araştırılması

Reinforcement learning is a learning method that many creatures often unwittingly use to gain abilities such as eating and walking. Inspired by this learning method, machine learning researchers have reduced this learning method to subheadings as value learning and policy learning. In this study, the noise standard deviation of the deep deterministic policy gradient (DDPG) method, which is one of the policy learning algorithms, was examined to solve inverse kinematics of a 2 degrees-of-freedom planar robot. For this examination, 8 different functions were determined depending on the maximum value of the output of the action artificial neural network. Created artificial neural networks were trained by using these functions in 1000 iterations with 200 steps in each iteration. After the training, the statistical difference between the groups was examined and it was found that there was no statistical difference between the three best groups. For this reason, the best three groups were retrained 2500 iterations and 200 steps and tested for 100 different test scenarios after the training. After testing, the inverse kinematic equation of the 2 degrees-of-freedom planar robot with minimal errors was obtained with the help of artificial neural networks. In the light of the results, the importance of the choice of the standard deviation of noise and the correct range of selection was presented for researchers who will work in this field.

A method for noise attenuation of straightness measurement based on laser collimation

The accuracy and stability of the straightness measurement based on laser collimation are severely affected by atmospheric turbulence, especially at long working distances. To address this problem, a method for noise attenuation of straightness measurement based on laser collimation is proposed in this paper. The turbulence shield was designed to reduce intensity of atmospheric turbulence on the beam path and weaken its impact on the straightness measurement. Subsequently, the effectiveness of the turbulence shield was simulated by the method of multi-phase screen. To verify the simulation results, the straightness measurement experiment was carried out on the laser five-degree-of-freedom (5-DOF) measurement system. The experimental results showed that the standard deviations of horizontal and vertical straightness noise were reduced by 95.6% and 87.4%, respectively. The experimental results were basically consistent with the simulation results. The turbulence shield effectively improved the accuracy and stability of straightness measurement based on laser collimation.

Analysis of BDS/GPS Signals’ Characteristics and Navigation Accuracy for a Geostationary Satellite

The utilization of Global Navigation Satellite System (GNSS) is becoming an attractive navigation approach for geostationary orbit (GEO) satellites. A high-sensitivity receiver compatible with Global Position System (GPS) developed by the United States and BeiDou Navigation Satellite System (BDS) developed by China has been used in a GEO satellite named TJS-5 to demonstrate feasibility of real-time navigation. According to inflight data, the GNSS signal characteristics including availability, position dilution of precision (PDOP), carrier-to-noise ratio (C/N0), observations quantity and accuracy are analyzed. The mean number of GPS and GPS + BDS satellites tracked are 7.4 and 11.7 and the mean PDOP of GPS and GPS + BDS are 10.24 and 3.91, respectively. The use of BDS significantly increases the number of available navigation satellites and improves the PDOP. The number of observations with respect to C/N0 is illustrated in detail. The standard deviation of the pseudorange noises are less than 4 m, and the corresponding carrier phase noises are mostly less than 8 mm. We present the navigation performance using only GPS observations and GPS + BDS observations combination at different weights through comparisons with the precision reference orbits. When GPS combined with BDS observations, the root mean square (RMS) of the single-epoch least square position accuracy can improve from 32.1 m to 16.5 m and the corresponding velocity accuracy can improve from 0.238 m/s to 0.165 m/s. The RMS of real-time orbit determination position accuracy is 5.55 m and the corresponding velocity accuracy is 0.697 mm/s when using GPS and BDS combinations. Especially, the position accuracy in x-axis direction reduced from 7.24 m to 4.09 m when combined GPS with BDS observations.

Design of Nonlinear Autoregressive Exogenous Model Based Intelligence Computing for Efficient State Estimation of Underwater Passive Target.

In this study, an intelligent computing paradigm built on a nonlinear autoregressive exogenous (NARX) feedback neural network model with the strength of deep learning is presented for accurate state estimation of an underwater passive target. In underwater scenarios, real-time motion parameters of passive objects are usually extracted with nonlinear filtering techniques. In filtering algorithms, nonlinear passive measurements are associated with linear kinetics of the target, governing by state space methodology. To improve tracking accuracy, effective feature estimation and minimizing position error of dynamic passive objects, the strength of NARX based supervised learning is exploited. Dynamic artificial neural networks, which contain tapped delay lines, are suitable for predicting the future state of the underwater passive object. Neural networks-based intelligence computing is effectively applied for estimating the real-time actual state of a passive moving object, which follows a semi-curved path. Performance analysis of NARX based neural networks is evaluated for six different scenarios of standard deviation of white Gaussian measurement noise by following bearings only tracking phenomena. Root mean square error between estimated and real position of the passive target in rectangular coordinates is computed for evaluating the worth of the proposed NARX feedback neural network scheme. The Monte Carlo simulations are conducted and the results certify the capability of the intelligence computing over conventional nonlinear filtering algorithms such as spherical radial cubature Kalman filter and unscented Kalman filter for given state estimation model.

Identifying the sensitivity of GPS to non-tidal loadings at various time resolutions: examining vertical displacements from continental Eurasia

We examine the sensitivity of the Global Positioning System (GPS) to non-tidal loading for a set of continental Eurasia permanent stations. We utilized daily vertical displacements available from the Nevada Geodetic Laboratory (NGL) at stations located at least 100 km away from the coast. Loading-induced predictions of displacements of earth’s crust are provided by the Earth-System-Modeling Group of the GFZ (ESMGFZ). We demonstrate that the hydrological loading, supported by barystatic sea-level changes to close the global mass budget (HYDL + SLEL), contributes to GPS displacements only in the seasonal band. Non-tidal atmospheric loading, supported by non-tidal oceanic loading (NTAL + NTOL), correlates positively with GPS displacements for almost all time resolutions, including non-seasonal changes from 2 days to 5 months, which are often considered as noise, intra-seasonal and seasonal changes with periods between 4 months and 1.4 years, and, also, inter-annual signals between 1.1 and 3.0 years. Correcting the GPS vertical displacements by NTAL leads to a reduction in the time series variances, evoking a whitening of the GPS stochastic character and a decrease in the standard deviation of noise. Both lead, on average, to an improvement in the uncertainty of the GPS vertical velocity by a factor of 2. To reduce its impact on the GPS displacement time series, we recommend that NTAL is applied at the observation level during the processing of GPS observations. HYDL might be corrected at the observation level or remain in the data and be applied at the stage of time series analysis.

A Fast Gridless algorithm for harmonics and interharmonics estimation

The increasing use of non-linear loads may cause harmonics and interharmonics components in power system signals. The methods used for estimating these components must consider different requirements for practical applications, such as low error rate, robustness to noise and fundamental frequency deviation, needlessness of previous knowledge of the model order, and short execution time. This work proposes a new algorithm called Anti-Leakage Matching Pursuit (ALMP) that can estimate harmonics and interharmonics mainly under noise and frequency deviations. The algorithm is based on the Matching Pursuit method, and its principal stage consists of solving a non-linear least squares problem. The proposed method was compared to other recent techniques, such as Matrix Pencil Method (MPM), Fast Matching Pursuit (FMP) and Harmonics and Interharmonics components Estimation based on Signal Sparse Decomposition (HIESSD), besides the most used algorithm – the Discrete Fourier Transform (DFT). The proposed algorithm obtained results equivalent to the existing algorithms for harmonics and interharmonics estimation, without requiring prior model order selection. It also proved to be more robust to frequency deviation and presented a reduced execution time than the other algorithms.

Fault Indexing Parameter Based Fault Detection in Induction Motor via MCSA with Wiener Filtering

Fault detection in an induction motor, particularly at premature stage has become necessary to avoid unexpected damage in industrial process. In this paper, an approach to detect the early stage faults in induction machine using motor current signature analysis (MCSA) is presented. It is proposed to estimate the fault severity from stator current using noise cancelation by an adaptive filter (Wiener filter). Wavelet De-noising technique is implemented to reduce the effect of noise floor in noise canceled stator current. Different categories of bearing faults, broken rotor fault and stator inter turn faults in induction motor are estimated with and without de-nosing using pre-fault component cancelation (Noise cancelation). In addition, fault index based on standard deviation (SD) and simple square integral (SSI) value of noise canceled stator current are proposed. The proposed fault detection topology is examined using simulations and experiments on a 3HP, 1HP and 0.5HP induction motors for bearing, broken rotor and stator inter turn faults respectively.

Investigation of the limit of discovery using tile-based Fisher ratio analysis with comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry

Comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC-TOFMS) is followed by tile-based Fisher ratio (F-ratio) analysis to investigate the “limit of discovery” for low concentration levels of sulfur-containing compounds in JP8 jet fuel. A mixture of 14 sulfur-containing compounds was spiked at 30 ppm, 15 ppm, 3 ppm and 1.5 ppm into the neat fuel prior to GC×GC-TOFMS analysis with a “reversed” column format (aka polar first dimension (1D) and non-polar second dimension (2D) column). Prior standard implementation of tile-based F-ratio analysis utilized an average F-ratio requiring a minimum of 3 mass channels (m/z) with the highest F-ratios. Herein, we explore the notion that use of the top F-ratio m/z for hitlist ranking is superior to the standard implementation for analytes near their limit-of-quantitation (LOQ), defined as an analyte concentration that produces a signal equal to ten times the standard deviation of the baseline noise (10σn). Hitlist ranking comparisons revealed that using only the top F-ratio m/z resulted in impressive improvements in discoverability for the low concentration comparisons. Specifically, for the 3 ppm versus neat hitlist, 1,4-oxathiane (LOQ = 2.5 ppm) improved from hit 114 via standard F-ratio analysis, to hit 25. For the 1.5 ppm versus neat hitlist, 2-propylthiophene (LOQ = 0.64 ppm) improved from hit 59 to 17, benzo[b]thiophene (LOQ = 1.1 ppm) from hit 98 to 28, and 2,5-dimethylthiophene (LOQ = 1.3 ppm) from hit 262 to 39. Additional hitlist ranking comparisons revealed the importance of proper tile size selection, as analyte discoverability deteriorated upon using either an inappropriately too small or too large of a tile.

Searching for gravitational waves via Doppler tracking by future missions to Uranus and Neptune

ABSTRACT The past year has seen numerous publications underlining the importance of a space mission to the ice giants in the upcoming decade. Proposed mission plans involve a ∼10 yr cruise time to the ice giants. This cruise time can be utilized to search for low-frequency gravitational waves (GWs) by observing the Doppler shift caused by them in the Earth–spacecraft radio link. We calculate the sensitivity of prospective ice giant missions to GWs. Then, adopting a steady-state black hole binary population, we derive a conservative estimate for the detection rate of extreme mass ratio inspirals (EMRIs), supermassive black hole (SMBH), and stellar mass binary black hole (sBBH) mergers. We link the SMBH population to the fraction of quasars fbin resulting from Galaxy mergers that pair SMBHs to a binary. For a total of 10 40-d observations during the cruise of a single spacecraft, $\mathcal {O}(f_\mathrm{bin})\sim 0.5$ detections of SMBH mergers are likely, if Allan deviation of Cassini-era noise is improved by ∼102 in the 10−5 − 10−3 Hz range. For EMRIs the number of detections lies between $\mathcal {O}(0.1) \ \mathrm{ and} \ \mathcal {O}(100)$. Furthermore, ice giant missions combined with the Laser Interferometer Space Antenna (LISA) would improve the localization by an order of magnitude compared to LISA by itself.

Low-Cost Fluorescence Sensor for Ammonia Measurement in Livestock Houses.

Measurements of ammonia with inexpensive and reliable sensors are necessary to obtain information about e.g., ammonia emissions. The concentration information is needed for mitigation technologies and documentation of existing technologies in agriculture. A flow-based fluorescence sensor to measure ammonia gas was developed. The automated sensor is robust, flexible and made from inexpensive components. Ammonia is transferred to water in a miniaturized scrubber with high transfer efficiency (>99%) and reacts with o-phthalaldehyde and sulfite (pH 11) to form a fluorescent adduct, which is detected with a photodiode. Laboratory calibrations with standard gas show good linearity over a dynamic range from 0.03 to 14 ppm, and the detection limit of the analyzer based on three-times the standard deviation of blank noise was approximately 10 ppb. The sampling frequency is 0.1 to 10 s, which can easily be changed through serial commands along with UV LED current and filter length. Parallel measurements with a cavity ring-down spectroscopy analyzer in a pig house show good agreement (R2 = 0.99). The fluorescence sensor has the potential to provide ammonia gas measurements in an agricultural environment with high time resolution and linearity over a broad range of concentrations.

Improved visualization of Liver Metastases Adjacent to Vessels using Hepatobiliary Phase Gadoxetic-acid-enhanced Single Shot Inversion-Recovery Gradient-Echo (IRGRE) MR Imaging

Objectives: To quantitatively compare contrast differences between liver metastases and vessels on Hepatobiliary Phase (HP) gadoxetic-acid-enhanced 2-Dimensional (2D) single-shot Inversion Recovery Gradient-Echo (IRGRE) and 3D Fat Saturated GRE (FSGRE) magnetic resonance images. Methods: The study included 55 consecutive patients who had HP FSGRE and IRGRE (39 at 1.5T, 16 at 3T) for liver metastases evaluation obtained 20 minutes after gadoxetic-acid administration. Thirty-eight patients had metastases (23 at 1.5T, 15 at 3T). Regions of interest were drawn measuring signal intensity (SI) of the largest lesion, normal liver, inferior vena cava, and background noise. Signal-to-noise (SNR) and contrast-to-noise ratio (CNR), respectively, were calculated as SI divided by standard deviation (SD) of background noise, and (S1-S2)/SD, where S1 and S2 represented SI for tissue 1 and tissue 2. Statistical analysis was via the Wilcoxon Sign Rank test. Results: Median liver-SNR for all 55 cases was greater for FSGRE than for IRGRE, as was lesion-SNR in the 38 with lesions (p&lt;0.05). Vessel-SNR was greater for IRGRE than for FSGRE at 3T (p&lt;0.05), with no difference between techniques at 1.5T (p&gt;0.05). Median lesion/vessel contrast and CNR for the 38 cases with metastases at 1.5T and the 15 at 3T were, in absolute value, greater for IRGRE than for FSGRE (p&lt;0.05). While both techniques yielded high lesion-liver contrast and CNR, only IRGRE provided consistently high lesion-vessel contrast and CNR (p&lt;0.05). Conclusion: Hepatobiliary-phase contrast difference between liver metastases and vessels was significantly and substantially greater for IRGRE than for FSGRE, potentially facilitating improved lesion detection using IRGRE.