True Fraction Research Articles

Application of functional magnetic resonance imaging to evaluate renal structure and function in type 2 cardiorenal syndrome

BackgroundThere is a lack of diagnostic non-invasive imaging technology for assessing the early structural and functional changes of the kidney in type 2 cardiorenal (CRS) patients. This study aims to explore the value of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) for clinical application in type 2 CRS patients, to provide imaging markers for the assessment of kidney damage.MethodsThis is a retrospective observational clinical study conducted in Nanjing, China. The clinical characteristics, including age, gender, medical history, laboratory results, and ultrasound and magnetic resonance imaging results were collected from the electronic medical record. Thirty-one patients with type 2 CRS, 20 patients with chronic heart failure (HF) and 20 healthy controls were enrolled and divided into type 2 CRS, HF and control groups. All the participants underwent magnetic resonance imaging (MRI) scanning. The apparent diffusion coefficient (ADC) value and IVIM-DWI parameters including true diffusion coefficient (D), pseudodiffusion coefficient (D*), and perfusion fraction (f) were obtained. The correlation between estimated glomerular filtration rate (eGFR), renal size and imaging parameters was evaluated by Spearman correlation analysis.ResultsADC and D of the renal cortex in patients with type 2 CRS were lower than those in the healthy control group. ADC and f in the HF group were lower than those in the control group. D was positively correlated with the length (r = 0.3752, P = 0.0013) and transverse diameter (r = 0.3258, P = 0.0056) of the kidney. ADC (r = 0.2964, P = 0.0121) and D (r = 0.3051, P = 0.0097) were positively correlated with eGFR. Renal cortical ADC and D values could distinguish type 2 CRS patients from the healthy controls with area under the curve (AUC) of 0.723 and 0.706, respectively.ConclusionThe ADC and D values were not only correlated with renal function, but also had lower levels in type 2 CRS. The IVIM-DWI parameter D was also related to kidney size, but further research is needed to determine whether it can be used as a novel imaging marker for type 2 CRS.

Grading of clear cell renal cell carcinoma by using monoexponential, biexponential, and stretched exponential diffusion-weighted MR imaging.

To evaluate the diagnostic accuracy of monoexponential, biexponential and stretched-exponential diffusion-weighted imaging (DWI) models in the grading of clear cell renal cell carcinoma (ccRCC). Fifty-one patients with pathologically proven ccRCC underwent DWI with fifteen b factors (0, 10, 30, 50, 70, 100, 150, 200, 300, 400, 600, 800, 1000, 1500, 2000 sec/mm²) on a 3.0T MR scanner. The isotropic apparent diffusion coefficient (ADC), true diffusion coefficient (ADCslow), pseudodiffusion coefficient (ADCfast), and fraction of perfusion (f) were derived from DWI using a biexponential model. The water diffusion heterogeneity index (α) and distributed diffusion coefficient (DDC) were derived from DWI using a stretched-exponential model. All values were calculated for the solid area of tumors and compared between high-grade and low-grade ccRCC. The Mann-Whitney U test and receiver operating characteristic (ROC) analysis were used for statistical analysis. The DeLong test was performed to compare the ROC curves. The mean ADC, DDC, ADCslow and α values were significantly lower in high-grade ccRCC than in low-grade ccRCC (P< 0.01). However, the ADCfast and f were not significantly different between the two groups (P > 0.05). According to the ROC analyses, the AUC for α was 0.941, which was significantly greater than those of the other parameters, with a sensitivity of 100% and a specificity of 84.2%. The DeLong test showed that there were significant differences in the ROCs among ADCfast/ADC, ADCfast/α, f/ADCslow, ADCfast/ADCslow, f/α, DDC/α, and f/ADC. Diffusion-related parameters (ADC, DDC, ADCslow and α) could be used to distinguish between low- and high-grade ccRCC. The α derived from the stretched-exponential model may be the most promising parameter for grading ccRCC.

Radio Afterglows from Tidal Disruption Events: An Unbiased Sample from ASKAP RACS

Late-time (∼a year) radio follow-up of optically discovered tidal disruption events (TDEs) is increasingly resulting in detections at radio wavelengths, and there is growing evidence for this late-time radio activity to be common to the broad class of subrelativistic TDEs. Detailed studies of some of these TDEs at radio wavelengths are also challenging the existing models for radio emission. Using all-sky multiepoch data from the Australian Square Kilometre Array Pathfinder (ASKAP), taken as a part of the Rapid ASKAP Continuum Survey (RACS), we searched for radio counterparts to a sample of optically discovered TDEs. We detected late-time emission at RACS frequencies (742–1032 MHz) in five TDEs, reporting the independent discovery of radio emission from TDE AT 2019ahk and extending the time baseline out to almost 3000 days for some events. Overall, we find that at least 22−11+15% of the population of optically discovered TDEs has detectable radio emission in the RACS survey, while also noting that the true fraction can be higher given the limited cadence (two epochs separated by ∼3 yr) of the survey. Finally, we project that the ongoing higher-cadence (∼2 months) ASKAP Variable and Slow Transients survey can detect ∼20 TDEs in its operational span (4 yr), given the current rate from optical surveys.

Exploring the dynamical evolution of Cepheid multiplicity in star clusters and its implications for B-star multiplicity at birth

Context. Classical Cepheid variable stars provide a unique probe of binary evolution in intermediate-mass stars over the course of several tens to hundreds of Myr. In addition, understanding binary evolution with the inclusion of cluster dynamics is desirable for obtaining a more complete picture of these stars, especially as they play a vital role in distance determinations. Aims. We studied the binary and multiple properties of Cepheids, assuming that all mid-B stars form in binaries inside star clusters. We also estimated the birth multiplicity of mid-B stars by comparing the observed multiplicity statistics of Cepheids with models based on particular assumptions. Methods. The clusters were modelled with the NBODY6 code, including synthetic stellar and binary evolutionary tracks. The Cepheids were identified from their position on the Hertzsprung-Russell diagram. Results. The dynamical cluster environment results in a higher binary fraction among the Cepheids that remain in star clusters (≈60%) than among the Cepheids which have escaped to the field (≈35%). The fraction of Cepheids in triples (≈30% and ≈10% in clusters and field, respectively) follows the same trend. In clusters, the binary, triple, and multiple fraction decreases with increasing cluster mass. More massive clusters have binaries of shorter orbital periods than lower mass clusters and field Cepheids. Mergers are very common with ≈30% of mid-B stars not evolving to Cepheids because of the interaction with their companion. Approximately 40% of Cepheids have merged with their companion, and the merger event impacts stellar evolution, so that ≈25% of all Cepheids occur at an age by more than 40% different than what would be expected from their mass and the current cluster age; the expected age of Cepheids can differ from the age of their host cluster. Our models predict that one in five Cepheids is the result of a merger between stars with mass below the lower mass limit for Cepheids; in clusters, these objects occur substantially later than expected from their mass. Approximately 10% of binary Cepheids have a different companion from the zero-age main sequence (ZAMS) one, and ≈3 to 5% of all Cepheids have a compact companion (≈0.15% of all Cepheids are accompanied by a black hole). Conclusions. The binary fraction derived from our simulations (42%) underestimates the observed binary Cepheid fraction by approximately a factor of 2. This suggests that the true multiplicity fraction of B-stars at birth could be substantially larger than unity and, thus, that mid-B stars may typically form in triple and higher order systems.

The robustness in identifying and quantifying high-redshift bars using JWST observations

Understanding the methodological reliability in identifying and quantifying high-redshift bars is essential for studying their evolution with the James Webb Space Telescope (JWST). We used nearby spiral galaxies to generate simulated images at various resolutions and signal-to-noise ratios, and obtained the simulated galaxy images observed in the Cosmic Evolution Early Release Science (CEERS) survey. Through a comparison of measurements before and after image degradation, we show that the bar measurements for massive galaxies remain robust against noise. While the measurement of the bar position angle remains unaffected by resolution, the measured bar ellipticity is significantly underestimated in low-resolution images. The size measurement is barely affected on average as long as the intrinsic bar size abar, true &gt; 2 × FWHM. To address these effects, correction functions are derived. We also find that bar detections remain effective at ∼100% when the abar, true/FWHM is above 2, below which the rate drops sharply, quantitatively validating the effectiveness of using abar, true &gt; 2 × FWHM as a bar detection threshold. We analyzed a set of simulated CEERS images and took into account observational effects and plausible galaxy (and bar-size) evolution models. We show that a significant (and misleading) reduction in the detected bar fraction with increasing redshift would apparently result even if the true bar fraction remained constant. Our results underscore the importance of disentangling the true bar fraction evolution from resolution effects and bar size growth.

Effect of spatial scale, color infrared and sample size on learning poverty from aerial images

There is a growing amount of literature that focuses on using machine learning algorithms to predict poverty from satellite and aerial images on a low regional level, particularly for countries without a high-quality official statistical system. The data used for annotating images and training an algorithm are generally based on sample surveys. In The Netherlands, statistics on income and poverty are derived from tax registers resulting in a complete enumeration of the Dutch population. In this paper, we use this complete enumeration to simulate to which extent satellite or aerial images can predict poverty on low regional levels. After geocoding these households, aerial images are annotated and a deep learning algorithm is trained to predict poverty. The precision of the predictions is evaluated by comparing it with the true poverty fractions known from tax registers. The effect of different spatial scales (1-ha vs. 25-ha images), spectral bands (RGB vs. CIR), and sample sizes for the training set are compared. It is discussed how this information can be used in the production of low regional statistics on poverty in countries where high-quality official statistical systems are lacking.

Reproducibility study of intravoxel incoherent motion and apparent diffusion coefficient parameters in normal pancreas.

The consistency of pancreatic apparent diffusion coefficient (ADC) values and intravoxel incoherent motion (IVIM) parameter values across different magnetic resonance imaging (MRI) devices significantly impacts the patient's diagnosis and treatment. To explore consistency in image quality, ADC values, and IVIM parameter values among different MRI devices in pancreatic examinations. This retrospective study was approved by the local ethics committee, and informed consent was obtained from all participants. In total, 22 healthy volunteers (10 males and 12 females) aged 24-61 years (mean, 28.9 ± 2.3 years) underwent pancreatic diffusion-weighted imaging using 3.0T MRI equipment from three vendors. Two independent observers subjectively scored image quality and measured the pancreas's overall ADC values and signal-to-noise ratios (SNRs). Subsequently, regions of interest (ROIs) were delineated for the IVIM parameters (true diffusion coefficient, pseudo-diffusion coefficient, and perfusion fraction) using post-processing software. These ROIs were on the head, body, and tail of the pancrease. The subjective image ratings were assessed using the kappa consistency test. Intraclass correlation coefficients (ICCs) and mixed linear models were used to evaluate each device's quantitative parameter values. Finally, a pairwise analysis of IVIM parameter values across each device was performed using Bland-Altman plots. The Kappa value for the subjective ratings of the different observers was 0.776 (P < 0.05). The ICC values for inter-observer and intra-observer agreements for the quantitative parameters were 0.803 [95% confidence interval (CI): 0.684-0.880] and 0.883 (95%CI: 0.760-0.945), respectively (P < 0.05). The ICCs for the SNR between different devices was comparable (P > 0.05), and the ICCs for the ADC values from different devices were 0.870, 0.707, and 0.808, respectively (P < 0.05). Notably, only a few statistically significant inter-device agreements were observed for different IVIM parameters, and among those, the ICC values were generally low. The mixed linear model results indicated differences (P < 0.05) in the f-value for the pancreas head, D-value for the pancreas body, and D-value for the pancreas tail obtained using different MRI machines. The Bland-Altman plots showed significant variability at some data points. ADC values are consistent among different devices, but the IVIM parameters' repeatability is moderate. Therefore, the variability in the IVIM parameter values may be associated with using different MRI machines. Thus, caution should be exercised when using IVIM parameter values to assess the pancreas.

Improvement and generalization of ABCD method with Bayesian inference

To find New Physics or to refine our knowledge of the Standard Model at the LHC is an enterprise that involves many factors, such as the capabilities and the performance of the accelerator and detectors, the use and exploitation of the available information, the design of search strategies and observables, as well as the proposal of new models. We focus on the use of the information and pour our effort in re-thinking the usual data-driven ABCD method to improve it and to generalize it using Bayesian Machine Learning techniques and tools. We propose that a dataset consisting of a signal and many backgrounds is well described through a mixture model. Signal, backgrounds and their relative fractions in the sample can be well extracted by exploiting the prior knowledge and the dependence between the different observables at the event-by-event level with Bayesian tools. We show how, in contrast to the ABCD method, one can take advantage of understanding some properties of the different backgrounds and of having more than two independent observables to measure in each event. In addition, instead of regions defined through hard cuts, the Bayesian framework uses the information of continuous distribution to obtain soft-assignments of the events which are statistically more robust. To compare both methods we use a toy problem inspired by pp\to hh\to b\bar b b \bar bpp→hh→bb‾bb‾, selecting a reduced and simplified number of processes and analysing the flavor of the four jets and the invariant mass of the jet-pairs, modeled with simplified distributions. Taking advantage of all this information, and starting from a combination of biased and agnostic priors, leads us to a very good posterior once we use the Bayesian framework to exploit the data and the mutual information of the observables at the event-by-event level. We show how, in this simplified model, the Bayesian framework outperforms the ABCD method sensitivity in obtaining the signal fraction in scenarios with 1% and 0.5% true signal fractions in the dataset. We also show that the method is robust against the absence of signal. We discuss potential prospects for taking this Bayesian data-driven paradigm into more realistic scenarios.

Analysis of the ion collection model in the ExB probe

An ExB probe is a pass-band velocity filter for an ion beam. The ExB probe measurement is related to the ion velocity distribution function (IVDF); however, the finite pass-band filter window size leads to differences in the true IVDF and measured ExB probe spectrum. We derive for the first time an analytical ExB probe transmittancy matrix and use it to examine differences in the IVDF and the probe spectrum. The probe spectra are compared to a synthetically defined test IVDF to study how probe geometry and ion species affect the differences. It is found that the difference in the probe spectrum and the true IVDF is dependent on the ion species, and the deviation is larger for heavier ions. The peak velocity of the spectrum was shifted by up to 13%, and the velocity spread was broadened by up to 276%. The relative ion species fraction is calculated from the probe spectra by two different methods and compared to the true fraction. Using the approach of this work, direct integration of the spectrum resulted in a 2% difference, while a more common approach from literature overestimated one ion species by 184%.

Comparison of conventional diffusion-weighted imaging, diffusion kurtosis imaging and intravoxel incoherent motion in characterization of sonographically indeterminate adnexal masses.

To evaluate the role of conventional diffusion weighted imaging, diffusion kurtosis imaging (DKI), and intravoxel incoherent motion (IVIM) in distinguishing benign from malignant adnexal masses. 38 patients with 45 adnexal masses were enrolled in this prospective study and assessed with multiparametric MRI, including the IVIM-DKI sequence, on a 3T MRI system. The mean apparent diffusion coefficient (ADC) from conventional DWI, the apparent diffusion coefficient derived from DKI (Dapp), the apparent kurtosis coefficient (Kapp), true diffusion coefficient (Dt), perfusion fraction (f) and pseudo-diffusion coefficient (Dp) were measured. The mean ADC, Dapp, and Dt were significantly higher in benign adnexal masses than in malignant adnexal masses (p < 0.001). f and Dp were also significantly higher in benign adnexal masses, with p values of 0.026 and 0.002, respectively. Kapp was higher in malignant masses (p < 0.001). Among mean ADC, Dapp, and Dt, mean ADC had the highest area under the curve (AUC) of 0.885. However, no statistically significant differences were observed between the ROCs of various diffusion parameters. The mean ADC, Dapp, and Kapp are useful parameters in discriminating between benign and malignant adnexal masses. Dt derived from IVIM also helps in distinguishing benign and malignant adnexal masses; however, no incremental role of IVIM and DKI over ADC could be identified in our study.

Spatio-temporal void fraction visualization in air-water two-phase flow regime transitions by combination of convolutional neural network and long short-term memory implemented into multiple current-voltage (MCV-CNN_LSTM)

Spatio-temporal void fraction in air-water two-phase flow regime transitions has been visualized by combination of convolutional neural network and long short-term memory implemented into multiple -current-voltage (MCV-CNN_LSTM). The MCV-ML is composed of two components, which are MCV-CNN_LSTM training and evaluation. In the first component, four steps are carried out as 1) true void fraction α measurement was conducted by wire-mesh sensor (WMS) as objective variables, 2) simulated MCV voltage U calculation by simulation of MCV measurement for explanatory variable, 3) CNN_LSTM training instance generation, and 4) CNN_LSTM model training and testing. In the second component, two additional steps are experimentally conducted which are 5) actual MCV voltage V measurement by MCV for input of trained ML, and 6) spatio-temporal void fraction α prediction. For the dataset generation, the experiment was conducted on air-water two phase flow regime transitions in vertical pipe with an inner diameter of 25 mm and a length of 350 mm from the elbow. The superficial velocity of liquid and gas phase are varied to present the flow regime transition of bubbly to slug flow. As a result, the estimated spatio-temporal void fraction αˆ by MCV-CNN_LSTM enables the visualization of detailed gas distribution in vertical upward two-phase flow. The spatial averaged instantaneous void fraction measured by WMS and estimated by MCV-CNN_LSTM show qualitative agreement in normalized cross correlation (INCC) of 0.364 on its temporal variation.

Functional MRI assessment of microstructural and perfusion changes in the kidneys of rats with intrauterine growth restriction

To explore the value of functional magnetic resonance imaging (MRI) techniques, including intravoxel incoherent motion (IVIM), T1 mapping, and T2 mapping, in assessing the microstructural and perfusion changes in the kidneys of rats with intrauterine growth restriction (IUGR). An IUGR rat model was established through a low-protein diet during pregnancy. Offspring from pregnant rats on a low-protein diet were randomly divided into an IUGR 8-week group and an IUGR 12-week group, while offspring from pregnant rats on a normal diet were divided into a normal 8-week group and a normal 12-week group (n=8 for each group). The apparent diffusion coefficient (ADC), true diffusion coefficient (Dt), pseudo-diffusion coefficient (D*), perfusion fraction (f), T1 value, and T2 value of the renal cortex and medulla were compared, along with serum creatinine and blood urea nitrogen levels among the groups. The Dt value in the renal medulla was higher in the IUGR 12-week group than in the IUGR 8-week group, and the D* value in the renal medulla was lower in the IUGR 12-week group than in both the normal 12-week group and the IUGR 8-week group (P<0.05). The T1 value in the renal medulla was higher than in the cortex in the IUGR 8-week group, and the T1 value in the renal medulla was higher in the IUGR 12-week group than in both the IUGR 8-week group and the normal 12-week group, with the cortical T1 value in the IUGR 12-week group also being higher than that in the normal 12-week group (P<0.05). The T2 values in the renal medulla were higher than those in the cortex across all groups (P<0.05). There were no significant differences in the T2 values of either the cortex or medulla among the groups (P>0.05). There were no significant differences in serum creatinine and blood urea nitrogen levels among the groups (P>0.05). Glomerular hyperplasia and hypertrophy without significant fibrotic changes were observed in the IUGR 8-week group, whereas glomerular atrophy, cystic stenosis, and interstitial inflammatory infiltration and fibrosis were seen in the IUGR 12-week group. IVIM MRI can be used to assess and dynamically observe the microstructural and perfusion damage in the kidneys of IUGR rats. MRI T1 mapping can be used to evaluate kidney damage in IUGR rats, and the combination of MRI T1 mapping and T2 mapping can further differentiate renal fibrosis in IUGR rats.

Correlation between diffusion-weighted image-derived parameters and dynamic contrast-enhanced magnetic resonance imaging-derived parameters in the orofacial region.

Diffusion-weighted imaging (DWI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) are widely used in the orofacial region. Furthermore, quantitative analyses have proven useful. However, a few reports have described the correlation between DWI-derived parameters and DCE-MRI-derived parameters, and the results have been controversial. To evaluate the correlation among parameters obtained by DWI and DCE-MRI and to compare them between benign and malignant lesions. Fifty orofacial lesions were analysed. The apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudodiffusion coefficient (D*) and perfusion fraction (f) were estimated by DWI. For DCE-MRI, TK model analysis was performed to estimate physiological parameters, for example, the influx forward volume transfer constant into the extracellular-extravascular space (EES) (Ktrans) and fractional volumes of EES and plasma components (ve and vp). Both ADC and D showed a moderate positive correlation with ve (ρ = 0.640 and 0.645, respectively). Ktrans showed a marginally weak correlation with f (ρ = 0.296), while vp was not correlated with f or D*; therefore, IVIM perfusion-related parameters and TK model perfusion-related parameters were not straightforward. Both D and ve yielded high diagnostic power between benign lesions and malignant tumours with areas under the curve (AUCs) of 0.830 and 0.782, respectively. Both D and ve were reliable parameters that were useful for the differential diagnosis. In addition, the true diffusion coefficient (D) was affected by the fractional volume of EES.

Role of intravoxel incoherent motion MRI in diagnosis and assessment of invasiveness of urothelial bladder carcinoma

BackgroundUrinary bladder cancer is a common malignancy. Preoperative accurate judgment of the degree of tissue invasion is crucial to determine the plan of therapy. We aimed in the current study to evaluate the role of intravoxel incoherent motion (IVIM) MRI and apparent diffusion coefficient (ADC) map in differentiating muscle invasive from non-muscle invasive carcinomas.ResultsValues of IVIM-derived true diffusion coefficient (D), pseudo-diffusion coefficient (D*) and perfusion fraction (F) and ADC were lower in muscle invasive carcinomas than non-invasive masses with p values < 0.001 and 0.001, respectively. In MIBC, ADC was (0.9 × 10−3 mm2/s), D was (0.8 × 10−3 mm2/s), D* was (6.9 × 10−3 mm2/s) and F was (16.9%). In NMIBC, ADC was (1 × 10−3 mm2/s), D was (1.1 × 10−3 mm2/s), D* was (10.2 × 10−3 mm2/s) and F was (17.7%). Mass D cut off values, sensitivity and specificity and AUC were < 0.9, 83.93, 96.30 and 0.945, respectively. Mass ADC cut off values, sensitivity and specificity and AUC were < 1.09, 87.50, 51.85 and 0.721, respectively. The degree of agreement between IVIM parameters and the pathological staging was assessed by Kappa test. The best agreement was detected for mass D (K = 0.796), while the mass ADC and wall beside mass D showed moderate and fair agreement (k = 0.418 and 0.325, respectively).ConclusionsIVIM MRI parameters and ADC map can be utilized to detect the degree of tissue invasion caused by bladder cancer.

Evaluating Mediastinal Lymph Node Metastasis of Non-Small Cell Lung Cancer Using Mono-exponential, Bi-exponential, and Stretched-exponential Models of Diffusion-weighted Imaging.

To explore and compare the diagnostic values of mono-exponential, bi-exponential, and stretched-exponential diffusion-weighted imaging (DWI) parameters of primary lesions and lymph nodes (LNs) to predict mediastinal LN metastasis in patients with non-small cell lung cancer. Sixty-one patients with non-small cell lung cancer underwent preoperative magnetic resonance imaging, including multiple b -value DWI. The DWI parameters, including apparent diffusion coefficient (ADC) from a mono-exponential model, true diffusion (D) coefficient, pseudo-diffusion (D*) coefficient, and perfusion fraction (f) from a bi-exponential model, distributed diffusion coefficient (DDC) and intravoxel diffusion heterogeneity index (α) from a stretched-exponential model of primary tumors and LNs and the size characteristics of LNs, were measured and compared. Multivariate logistic regression analysis was used to establish models for predicting mediastinal LN metastasis. Receiver operating characteristic analysis was applied to evaluate diagnostic performances. The DWI parameters of primary tumors showed no statistical significance between LN metastasis-positive and LN metastasis-negative groups. Nonmetastatic LNs had significantly higher ADC, D, DDC, and α values compared with metastatic LNs (all P < 0.05). The short-dimension, long-dimension, and short-long dimension ratio of metastatic LNs was significantly larger than those of nonmetastatic ones (all P < 0.05). The D value showed the best diagnostic performance among all DWI-derived single parameters, and the short dimension of LNs performed the same among all the size variables. Furthermore, the combination of DWI parameters (ADC and D) and the short dimension of LNs can significantly improve diagnostic efficiency. The ADC, D, DDC, and α from the mono-exponential, bi-exponential, and stretched-exponential models were demonstrated efficient in differentiating benign from metastatic LNs, and the combination of ADC, D, and short dimension of LNs may have a better diagnostic performance than DWI or size-derived parameters either in combination or individually.

The true fraction of repeating fast radio bursts revealed through CHIME source count evolution

ABSTRACT Fast Radio Bursts (FRBs) are classified into repeaters and non-repeaters, with only a few per cent of the observed FRB population from the Canadian Hydrogen Intensity Mapping Experiment (CHIME) confirmed as repeaters. However, this figure represents only a lower limit due to the observational biases, and the true fraction of repeaters remains unknown. Correcting for these biases uncovers a notable decline in apparently non-repeating FRB detection rate as the CHIME operational time increases. This finding suggests that a significant portion of apparently non-repeating FRBs could in fact exhibit repetition when observed over more extended periods. A simple population model infers that the true repeater fraction likely exceeds 50 per cent with 99 per cent confidence, a figure substantially larger than the observed face value, even consistent with 100 per cent. This greater prevalence of repeaters had previously gone unnoticed due to their very low repetition rates (∼10−3.5 h−1 on average). Hence, theoretical FRB models must incorporate these low-rate repeaters. Furthermore, our results indicate a significantly higher repeater volume number density, potentially exceeding observed values by up to 104 times, which in turn impacts comparisons with potential FRB progenitors.

Whole-joint histogram analysis of different models of diffusion weighted imaging in evaluating disease activity of axial spondyloarthritis.

To assess whole-joint histogram analysis of mono-exponential and bi-exponential diffusion weighted and diffusion kurtosis imaging in evaluating disease activity of axial spondyloarthritis (axSpA). A total of 82 patients with axSpA who underwent both DKI and multi b diffusion weighted imaging of the sacroiliac joints were divided into active and inactive disease groups based on clinical activity indices. Another 17 patients with nonspecific low back pain were included as a control group. The SPARCC scoring system was used to assess the level of sacroiliac joint bone marrow edema. Histogram parameters of apparent diffusion coefficient (ADC), true diffusion coefficient (D), perfusion fraction (f), pseudodiffusion coefficient (D*), mean kurtosis (MK), and mean diffusivity (MD) were calculated. Regions of interest were placed covering the entire sacroiliac joint. Receiver operating characteristic (ROC) analysis was performed to assess the diagnostic performance of imaging parameters in distinguishing different groups. Regression analysis was applied to determine the correlation between imaging parameters and clinical activity indices. All of the histogram parameters distinguished the active from inactive groups with a low area under the curve (AUC) (max AUCs≤0.71), while the SPARCC score failed to differentiate the two groups (p = 0.08). MD, MK, D, D*, and ADC showed good performance in differentiating active and inactive from control groups (max AUC = 0. 81 ~ 0.98). f50 differentiated the active from control groups with an AUC of 0.72, significantly lower than the maximum AUC for MD, MK, D, ADC, and SPARCC score (all p < 0.05). The max AUC of MD in differentiating inactive from control groups was significantly higher than that of D* and the SPARCC score. MD, D, D*, f, and ADC were positively correlated with BASDAI, while MK was negatively correlated with BASDAI. Only MD was positively correlated with hsCRP. Whole-joint histogram analysis of mono-exponential, bi-exponential diffusion weighted, and diffusion kurtosis imaging showed good diagnostic performance in differentiating active and inactive axSpA from patients with non-specific back pain. All the imaging parameters were correlated with BASDAI except for SPARCC score. Only DKI-derived MD was correlated with an increase in hsCRP, suggesting its potential use as an imaging biomarker for disease activity in axSpA. 1. No significant difference was found between the three models of diffusion weighted imaging in evaluating disease activity of axial spondyloarthritis.2. Only DKI-derived MD was correlated with an increase in hsCRP, suggesting its potential use as an imaging biomarker for disease activity in axSpA.

Preoperative prediction of microvascular invasion in hepatocellular carcinoma using diffusion-weighted imaging-based habitat imaging.

Habitat imaging allows for the quantification and visualization of various subregions within the tumor. We aim to develop an approach using diffusion-weighted imaging (DWI)-based habitat imaging for preoperatively predicting the microvascular invasion (MVI) of hepatocellular carcinoma (HCC). Sixty-five patients were prospectively included and underwent multi-b DWI examinations. Based on the true diffusion coefficient (Dt), perfusion fraction (f), and mean kurtosis coefficient (MK), which respectively characterize cellular density, perfusion, and heterogeneity, the HCCs were divided into four habitats. The volume fraction of each habitat was quantified. The logistic regression was used to explore the risk factors from habitat fraction and clinical variables. Clinical, habitat, and nomogram models were constructed using the identified risk factors from clinical characteristics, habitat fraction, and their combination, respectively. The diagnostic accuracy was evaluated using the area under the receiver operating characteristic curves (AUCs). MVI-positive HCC exhibited a significantly higher fraction of habitat 4 (f4) and a significantly lower fraction of habitat 2 (f2) (p < 0.001), which were selected as risk factors. Additionally, tumor size and elevated alpha-fetoprotein (AFP) were also included as risk factors for MVI. The nomogram model demonstrated the highest diagnostic performance (AUC = 0.807), followed by the habitat model (AUC = 0.777) and the clinical model (AUC = 0.708). Decision curve analysis indicated that the nomogram model offered more net benefit in identifying MVI compared to the clinical model. DWI-based habitat imaging shows clinical potential for noninvasively and preoperatively determining the MVI of HCC with high accuracy. The proposed strategy, diffusion-weighted imaging-based habitat imaging, can be applied for preoperatively and noninvasively identifying microvascular invasion in hepatocellular carcinoma, which offers potential benefits in terms of prognostic prediction and clinical management. • This study proposed a strategy of DWI-based habitat imaging for hepatocellular carcinoma. • The habitat imaging-derived metrics can serve as diagnostic markers for identifying the microvascular invasion. • Integrating the habitat-based metric and clinical variable, a predictive nomogram was constructed and displayed high accuracy for predicting microvascular invasion.

Differentiating tumour progression from pseudoprogression in glioblastoma patients: a monoexponential, biexponential, and stretched-exponential model-based DWI study

BackgroundTo investigate the diagnostic performance of parameters derived from monoexponential, biexponential, and stretched-exponential diffusion-weighted imaging models in differentiating tumour progression from pseudoprogression in glioblastoma patients.MethodsForty patients with pathologically confirmed glioblastoma exhibiting enhancing lesions after completion of chemoradiation therapy were enrolled in the study, which were then classified as tumour progression and pseudoprogression. All patients underwent conventional and multi-b diffusion-weighted MRI. The apparent diffusion coefficient (ADC) from a monoexponential model, the true diffusion coefficient (D), pseudodiffusion coefficient (D*) and perfusion fraction (f) from a biexponential model, and the distributed diffusion coefficient (DDC) and intravoxel heterogeneity index (α) from a stretched-exponential model were compared between tumour progression and pseudoprogression groups. Receiver operating characteristic curves (ROC) analysis was used to investigate the diagnostic performance of different DWI parameters. Interclass correlation coefficient (ICC) was used to evaluate the consistency of measurements.ResultsThe values of ADC, D, DDC, and α values were lower in tumour progression patients than that in pseudoprogression patients (p < 0.05). The values of D* and f were higher in tumour progression patients than that in pseudoprogression patients (p < 0.05). Diagnostic accuracy for differentiating tumour progression from pseudoprogression was highest for α(AUC = 0.94) than that for ADC (AUC = 0.91), D (AUC = 0.92), D* (AUC = 0.81), f (AUC = 0.75), and DDC (AUC = 0.88).ConclusionsMulti-b DWI is a promising method for differentiating tumour progression from pseudoprogression with high diagnostic accuracy. In addition, the α derived from stretched-exponential model is the most promising DWI parameter for the prediction of tumour progression in glioblastoma patients.

Microstructure evolution mechanisms and a physically-based constitutive model for an Al–Zn–Mg–Cu–Zr aluminum alloy during hot deformation

High-temperature flow features of the Al−Zn−Mg−Cu−Zr aluminum alloy was revealed by hot compression tests. The evolution mechanisms of dislocation clusters, subgrain, and dynamic recrystallization (DRX) grains, are thoroughly explored by EBSD and TEM analysis. Experimental results suggest that the high strain rate can exacerbate dislocation clusters formation, as well as subgrain nucleation/accumulation, inducing the increasing of flow stress. Nevertheless, the noticeable annihilation of substructures, as well as the growth of DRX grains, emerge at the higher temperature, causing the descending of flow stress. Three types of DRX nucleating mechanisms, i.e., discontinuous DRX (DDRX), geometric DRX (GDRX) and continuous DRX (CDRX) are activated in the Al−Zn−Mg−Cu−Zr aluminum alloy during hot compression. Simultaneously, the GDRX often appears at a high compressed temperature or a low strain rate. A physically-based (PB) model is proposed to collaboratively reconstruct true stresses and microstructure evolution features. The estimated values of true stress, DRX fractions and average grain size preferably fit the experimental data, indicating the proposed PB model can precisely catch the thermal compression behaviors and microstructure evolution characteristics of the Al−Zn−Mg−Cu−Zr aluminum alloy.