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Ultrasound (US) is an increasingly prevalent and effective diagnostic modality for neuromuscular imaging. Gray-scale B-mode imaging has been the dominant US approach to evaluating nerves qualitatively or making morphometric measurements of nerves, providing important insights into pathological changes for conditions such as carpal tunnel syndrome. Among more recent ultrasound strategies, high-frequency ultrasound (often defined as15 MHz for clinical applications), quantitative ultrasound and image textural analysis offer promising enhancements for improved and more objective approaches to nerve imaging. In this study, we evaluated the repeatability and reproducibility of backscatter coefficient (BSC) and imaging texture features extracted by gray-level co-occurrence matrices (GLCMs) in homogeneous tissue-mimicking reference phantoms and in median nerves in the wrists of healthy participants. We also investigated several practical sources of variability in the assessment of quantitative parameters, including influences of operators, and participant-to-participant variability. Overall, BSC- and GLCM-based outcomes are highly repeatable and reproducible after operator training, based on measurement of descriptive statistics, repeatability coefficient (RC) and reproducibility coefficient recommended by Quantitative Imaging Biomarker Alliance (QIBA RDC). GLCM parameters appear more reproducible and repeatable than BSC-based parameters in healthy participants in vivo. However, such variability noted here must be compared with the value ranges and variability of the results in pathological nerves, including median nerves afflicted by trauma, overuse syndromes such as carpal tunnel syndrome and after surgical repair.

BACKGROUND: MRI-derived proton density fat fraction (PDFF) is an accurate, reliable, and safe biologic marker for use in the noninvasive diagnosis of hepatic steatosis in patients with nonalcoholic fatty liver disease (NAFLD). Because of the cost and limited availability of MRI, it is necessary to develop an accurate method to diagnose NAFLD with potential point-of-care access. PURPOSE: To compare the diagnostic accuracy of the quantitative US (QUS) fat fraction (FF) estimator with that of the controlled attenuation parameter (CAP) in the diagnosis of NAFLD using contemporaneous MRI-derived PDFF as the reference standard. MATERIALS AND METHODS: Participants with or suspected of having NAFLD were prospectively recruited at the NAFLD Research Center between July 2015 and July 2019. All participants underwent MRI-derived PDFF measurement, transient elastography with CAP measurement, and QUS. QUS FF was derived using computed QUS parameters from the acquired radiofrequency US data using a calibrated reference phantom. The area under the receiver operating characteristic curve (AUC) was calculated to assess the accuracy of QUS FF and CAP in the diagnosis of hepatic steatosis (defined as MRI-derived PDFF ≥ 5%). AUCs were compared using the DeLong test. RESULTS: A total of 123 participants were included (mean age, 52 years ± 13 [SD]; 67 [54%] women). Of these participants, 100 (81%) had MRI-derived PDFF of 5% or more. QUS FF had a significantly higher AUC for diagnosis of NAFLD than did CAP (0.92 [95% CI: 0.87, 0.98] vs 0.79 [95% CI: 0.67, 0.90], P = .03). QUS FF had a sensitivity of 98% (98 of 100) and a specificity of 48% (11 of 23). CAP had a sensitivity of 87% (87 of 100) and a specificity of 57% (13 of 23). CONCLUSION: The quantitative US fat fraction estimator is more accurate than the controlled attenuation parameter in the diagnosis of hepatic steatosis in patients with or suspected of having nonalcoholic fatty liver disease. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Ito in this issue.

An innovative method that abides by all the standards of green chemistry for synthesizing high-purity lithium sulfide.

For some future clean-energy technologies (such as advanced batteries), the concept of green chemistry has not been exercised enough for their material synthesis. Herein, we report a waste-free method of synthesizing lithium sulfide (Li

To estimate the compressive strength of high-strength concrete (HSC), a hybrid model integrating the firefly algorithm (FFA) and fuzzy c-means (FCM) clustering method into the adaptive neuro fuzzy inference system (ANFIS) was developed in this paper. The FFA and FCM techniques were utilized to improve the forecasting accuracy of the proposed ANFIS. To establish the hybrid ANFIS-FFA model, five main constituents of HSC, cement, water, fine and coarse aggregates, and superplasticizer, are considered the input variables, and the compressive strength of HSC is used as the output variable. A comparison was conducted among four artificial intelligence models, including the proposed ANFIS-FFA model, the traditional ANFIS, the back propagation neural network (BPNN) and the extreme learning machine (ELM), in terms of four statistical indices. In addition, a detailed parametric study was conducted to investigate the influence of each input variable on the compressive strength of HSC. The results showed that the developed ANFIS-FFA model exhibits greater accuracy than the other three models, with a higher correlation coefficient (R) and lower root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) values, and it has great potential to accurately estimate the compressive strength of HSC.

OBJECTIVES—: To develop and evaluate deep learning models devised for liver fat assessment based on ultrasound (US) images acquired from four different liver views: transverse plane (hepatic veins at the confluence with the inferior vena cava, right portal vein, right posterior portal vein) and sagittal plane (liver/kidney). METHODS—: US images (four separate views) were acquired from 135 participants with known or suspected nonalcoholic fatty liver disease. Proton density fat fraction (PDFF) values derived from chemical shift-encoded magnetic resonance imaging served as ground truth. Transfer learning with a deep convolutional neural network (CNN) was applied to develop models for diagnosis of fatty liver (PDFF ≥ 5%), diagnosis of advanced steatosis (PDFF ≥ 10%), and PDFF quantification for each liver view separately. In addition, an ensemble model based on all four liver view models was investigated. Diagnostic performance was assessed using the area under the receiver operating characteristics curve (AUC), and quantification was assessed using the Spearman correlation coefficient (SCC). RESULTS—: The most accurate single view was the right posterior portal vein, with an SCC of 0.78 for quantifying PDFF and AUC values of 0.90 (PDFF ≥ 5%) and 0.79 (PDFF ≥ 10%). The ensemble of models achieved an SCC of 0.81 and AUCs of 0.91 (PDFF ≥ 5%) and 0.86 (PDFF ≥ 10%). CONCLUSION—: Deep learning-based analysis of US images from different liver views can help assess liver fat.

Hypothesis: Predicting the spontaneous preterm birth (sPTB) risk level is enhanced when using both historical clinical (HC) data and quantitative ultrasound (QUS) data compared to using only HC data. HC data defined herein includebirth history prior to that of the current pregnancy as well as, from the current pregnancy, a clinical cervical length assessment, and physical examination data. Study population included 248 full-term births (FTBs) and 26 sPTBs. Advanced statistical analyses were performed for supervised classification containing 53 scaled candidate features (48 QUS, 5 HC) using nested fivefold cross-validation of L1-penalized linear logistic regression with 1000 repetitions to identify potential predictors. Statistical models for HC data alone and HC + QUS data were compared with likelihood-ratio test, cross-validated receiver operating characteristic (ROC) area under the curve (AUC), sensitivity, and specificity. To assess performance, the ROC-AUC was estimated with 10-fold cross-validation logistic regression and 1000 repetitions. Averaged ROC curves plus AUCs were computed using threshold averaging. AUC confidence intervals and test statistics to test the two ROC curves’ differences were constructed via DeLong method. Combined HC and QUS data identified women at sPTB risk with better AUC (0.68; 95% CI, 0.57–0.78) than those of HC data alone (0.53; 95% CI, 0.40–0.66). [Work supported by NIHR01HD089935.]

Globally, ∼15 million babies are born preterm every year. In the United States, the preterm birth rate remains stubbornly high (10–15%) and refractory to interventions. Consequences of preterm birth (PTB) account for the second leading cause of infant mortality, with 1 million deaths annually. While the costs of PTB to society are more than for any other disease, the impact on families is devastating. Preterm babies suffer both immediate and lifelong physiological, cognitive, and developmental health problems. It is estimated that with the proper tools and technology, we could reduce the preterm birth and survival rates. Due to lack of technology, clinicians have had few interventions that have been rigorously studied to prevent preterm birth. Ineffective interventions have historically been based on opinion and patient symptoms rather than tissue based reliable and repeatable scientific studies. Our group has rigorously studied quantitative ultrasound’s (QUS’s) role for assessing PTB risk in animals and humans. QUS provided added value to currently available health and traditional ultrasound risk assessment methods. Basing clinical decision-making on tissue microstructure has the potential to reduce the PTB rate and provides a scientific basis for developing and objectively evaluating present and new treatments to prevent PTB. [Work supported by NIHR01HD089935.]

Having a history of a previous spontaneous preterm birth (sPTB) is the strongest risk factor for recurrent sPTB. It is unknown if there are differences in postpartum cervical remodeling between women who have delivered spontaneous preterm (sPT) and full-term. No studies have evaluated the role of postpartum remodeling between the two groups. Quantitative ultrasound (QUS) is a noninvasive ultrasound technology used to quantify tissue microstructure and function. QUS biomarkers were used to evaluate postpartum cervical microstructure in women who delivered sPT and full-term. Data were collected from 54 women:14 who delivered sPT and 40 who delivered full-term. Transvaginal QUS scans were performed at 6 weeks (±2 weeks) after delivery. Attenuation coefficient (AC), backscatter coefficient (BSC), and shear wave speed (SWS) QUS biomarkers were collected. BSC was significantly higher at six weeks postpartum in women who delivered sPT versus full-term (p = 0.01), while the AC approached statistical significance (p = 0.09). QUS biomarker BSC was able to identify cervical microstructure differences at six weeks postpartum between women who delivered sPT and full-term. QUS technology may improve our understanding of postpartum cervical remodeling and has the potential to noninvasively direct precision-health approaches for recurrent sPTB. [Work supported by NIH 5F31NR019716.]

Spontaneous preterm birth (sPTB) is one of the leading causes of infant morbidity. Medical interventions can prevent death caused by preterm birth if the risk is predicted at early stages. Quantitative ultrasound (QUS) is found valuable for predicting sPTB risk with a limitation of requiring a medically trained image analyst to manually draw a region of interest (ROI) on the cervix of a B-mode ultrasound image. An automated ROI placement algorithm was designed and trained to reduce the reliance on human annotations. The algorithm utilized a deep neural network with an optimized U-Net architecture to locate cervical tissues. A total of 8670 ultrasound images with sonographer-drawn ROIs were used for algorithm training and testing, followed by several postprocessing steps to yield the final ROIs. Quantitative comparison between algorithm-generated and sonographer-drawn ROIs yielded an average pixel accuracy of 96% and a dice coefficient of 88%. In addition, the QUS’s attenuation coefficient (AC) and backscatter coefficient (BSC) obtained from the algorithm-generated ROIs were highly correlated to those obtained from the sonographer-drawn ROIs with a Pearson correlation coefficient of 0.93 and 0.85, respectively. The results support the feasibility of automating QUS imaging for sPTB risk assessment. [Work supported by NIHR01HD089935.]

Accurately measuring the attenuation coefficient (AC) of reference phantoms is critical in clinical applications of quantitative ultrasound. Phantom AC measurement requires proper compensation of membrane transmission loss. Conventional methods require separate membrane samples to obtain membrane transmission loss. Unfortunately, separate membrane samples are often unavailable. A pulse-echo approach is proposed herein to compensate for membrane transmission loss without requiring separate membrane samples.The proposed method consists of the following steps. First, the insertion loss, caused by phantom attenuation and membrane transmission loss, is measured. Second, the membrane reflection coefficient is measured. Third, the unknown acoustic parameters of the membrane and phantom material are estimated by fitting theoretical reflection coefficient to the measured one. Finally, the fitted parameters are used to estimate membrane transmission loss and phantom AC. The proposed method was validated through k-Wave simulations and phantom experiments. Experimental AC measurements were repeated on 5 distinct phantoms by 2 operators to assess the repeatability and reproducibility of the proposed method. Five transducers were used to cover a broad bandwidth (0.7-16 MHz).The acquired AC in the simulations had a maximum error of 0.06 dB/cm-MHz for simulated phantom AC values ranging from 0.5 to 1 dB/cm-MHz. The acquired AC in the experiments had a maximum error of 0.045 dB/cm-MHz for phantom AC values ranging from 0.28 to 1.48 dB/cm-MHz. Good repeatability and cross-operator reproducibility were observed with a mean coefficient of variation below 0.054.The proposed method simplifies phantom AC measurement while providing satisfactory accuracy and precision.

Ultrasound localization microscopy (ULM) is an emerging vascular imaging technique that overcomes the resolution-penetration compromise of ultrasound imaging. Accurate and robust microbubble (MB) localization is essential for successful ULM. In this study, we present a deep learning (DL)- based localization technique that uses both Field-II simulation and in vivo chicken embryo chorioallantoic membrane (CAM) data for training. Both radiofrequency (RF) and in-phase quadrature (IQ) data were tested in this study. The simulation experiment shows that the proposed DL-based localization was able to reduce both missing MB localization rate and MB localization error. In general, RF data showed better performance than IQ. For the in vivo CAM study with high MB concentration, DL-based localization was able to reduce the vessel MB saturation time by more than 50% as compared to conventional localization. Additionally, we propose a DL-based framework for real-time visualization of the high-resolution microvasculature. The findings of the paper support the use of DL for more robust and faster MB localization, especially under high MB concentrations. The results indicate that further improvement could be achieved by incorporating temporal information of the MB data.

BACKGROUND: Advanced confounder-corrected chemical shift–encoded MRI-derived proton density fat fraction (PDFF) is a leading parameter for fat fraction quantification in nonalcoholic fatty liver disease (NAFLD). Because of the limited availability of this MRI technique, there is a need to develop and validate alternative parameters to assess liver fat. PURPOSE: To assess relationship of quantitative US parameters to MRI PDFF and to develop multivariable quantitative US models to detect hepatic steatosis and quantify hepatic fat. MATERIALS AND METHODS: Adults with known NAFLD or who were suspected of having NAFLD were prospectively recruited between August 2015 and February 2019. Participants underwent quantitative US and chemical shift–encoded MRI liver examinations. Liver biopsies were performed if clinically indicated. The correlation between seven quantitative US parameters and MRI PDFF was evaluated. By using leave-one-out cross validation, two quantitative US multivariable models were evaluated: a classifier to differentiate participants with NAFLD versus participants without NAFLD and a fat fraction estimator. Classifier performance was summarized by area under the receiver operating characteristic curve and area under the precision-recall curve. Fat fraction estimator performance was evaluated by correlation, linearity, and bias. RESULTS: Included were 102 participants (mean age, 52 years ± 13 [standard deviation]; 53 women), 78 with NAFLD (MRI PDFF ≥ 5%). A two-variable classifier yielded a cross-validated area under the receiver operating characteristic curve of 0.89 (95% confidence interval: 0.82, 0.96) and an area under the precision-recall curve of 0.96 (95% confidence interval: 0.93, 0.99). The cross-validated fat fraction predicted by a two-variable fat fraction estimator was correlated with MRI PDFF (Spearman ρ = 0.82 [P < .001]; Pearson r = 0.76 [P < .001]). The mean bias was 0.02% (P = .97), and 95% limits of agreement were ±12.0%. The predicted fat fraction was linear with MRI PDFF (R (2) = 0.63; slope, 0.69; intercept, 4.3%) for MRI PDFF of 34% or less. CONCLUSION: A multivariable quantitative US approach yielded excellent correlation with MRI proton density fat fraction for hepatic steatosis assessment in nonalcoholic fatty liver disease. © RSNA, 2020 Online supplemental material is available for this article.

Novel, hierarchical, flower-like Ag/Cu2O and Au/Cu2O nanostructures were successfully fabricated and applied as efficient electrocatalysts for the electrochemical reduction of CO2. Cu2O nanospheres with a uniform size of ∼180 nm were initially synthesized. Thereafter, Cu2O was used as a sacrificial template to prepare a series of Ag/Cu2O composites through galvanic replacement. By varying the Ag/Cu atomic ratio, Ag0.125/Cu2O, having a hierarchical, flower-like nanostructure with intersecting Ag nanoflakes encompassing an inner Cu2O sphere, was prepared. The as-prepared Agx/Cu2O samples presented higher Faradaic efficiencies (FE) for CO and relatively suppressed H2 evolution than the parent Cu2O nanospheres due to the combination of Ag with Cu2O in the former. Notably, the highest CO evolution rate was achieved with Ag0.125/Cu2O due to the larger electroactive surface area furnished by the hierarchical structure. The same hierarchical flower-like structure was also obtained for the Au0.6/Cu2O composite, where the FECO (10%) was even higher than that of Ag0.125/Cu2O. Importantly, the results reveal that Ag0.125/Cu2O and Au0.6/Cu2O both exhibit remarkably improved stability relative to Cu2O. This study presents a facile method of developing hierarchical metal-oxide composites as efficient and stable electrocatalysts for the electrochemical reduction of CO2.

While to date the element lithium (Li) has received eminent fame for the application of its compounds in lithium ion batteries (LIBs), its pure form of metallic Li is expected to achieve more fame in the future for its critical role in some new applications, for instance, Li-metal batteries. Such applications will require massive production of high-purity metallic Li, about a million tons at full scale development. However, the current industry for Li production faces two challenges: low production at the level of only two thousand tons per year and high cost due to tough manufacturing conditions. Very recently, several prestigious research groups published creative papers about Li production, which in the future would attract intensive efforts in this field. In order to provide the community with insightful views on the future production of Li, this review summarizes all the major methods of directly producing high-purity Li in the literature. After describing the production principles and key features, the article is concluded with our perspectives on future directions of producing metallic Li.

Phase aberration caused by skulls is a main challenge in transcranial ultrasound imaging for adults. Aberration could be corrected if the skull profile (i.e., thickness distribution) and speed of sound (SOS) are known. We previously designed a deep learning (DL) model to estimate the skull profile and SOS using pulse-echo ultrasound signals. This study’s objective is to develop strategies to improve the estimation and evaluate the effectiveness of aberration correction in transcranial ultrasound imaging. Acoustic simulations were performed using k-Wave in this numerical study. The following strategies were used to improve estimation: (1) A phased array was used instead of a single-element transducer; (2) Channel radiofrequency data were used instead of beamformed data as the DL model input; (3) A DL model was developed to incorporate physics into architecture design and model training. Compared with previously reported results, these strategies improved the correlation coefficient between the estimated and ground-truth values from 0.82 to 0.94 for SOS, and from 0.98 to 0.99 for thickness. Simulated transcranial images of point targets with phase correction using the estimated SOS and thickness values showed significantly reduced artifacts than those without correction. The results demonstrate feasibility of the proposed approach for transcranial ultrasound imaging.

This paper evaluated the repeatability and reproducibility (R&R) of quantitative ultrasound (QUS) biomarkers attenuation coefficient (AC) and backscatter coefficient (BSC) in transvaginal QUS reference phantoms for obstetric applications. Five phantoms were scanned by three sonographers according to the scanning protocol. Each sonographer scanned each phantom with four transvaginal transducers of the same model (MC9–4) and three probe cover types (latex cover, nonlatex cover, and no cover). The AC and BSC were estimated by using a reference phantom method. The R&R analysis was performed for the frequency-averaged AC and logBSC (=10log10BSC) (5.4–5.8 MHz) by using three-factor random effects Analysis of Variance with interaction. The total R&R variabilities for AC and logBSC are small (AC: 0.042 ∼ 0.065 dB/cm-MHz; logBSC: 0.50 ∼ 0.68 dB), indicating high measurement precision. These values are small compared to the ranges of AC (0.28 ∼ 0.99 dB/cm-MHz) and logBSC (−33.16 ∼ −20.35 dB) of the five phantoms. The AC and logBSC biomarkers measured on transvaginal QUS phantoms using the reference phantom method are repeatable, and reproducible between sonographers, transducers, and probe covers.

Untreated nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH) lead to irreversible liver damage. We hypothesized that a low-fat diet (LFD) or a high-fat diet (HFD) with soy protein isolate (SPI) would be an effective intervention to halt or reverse NAFLD progression. To test these hypotheses, we conducted 2 studies. In the first study, we fed an HFD to 7-week-old C57BL/6J mice to induce NAFLD compared to an LFD (control). Hepatic steatosis was monitored by quantitative ultrasound (QUS) scans (in vivo and ex vivo). Animals were euthanized after 0, 2, 4, and 6 weeks of feeding. In the second study, 7-week-old mice were randomized onto an LFD or HFD with SPI intervention after 4 weeks of feeding HFD. Animals from each group were scanned with QUS and euthanized after 4, 9, and 12 weeks of feeding. Animals fed the HFD developed NAFLD (100%) and NASH (80%) characterized by increased liver weight, lipid accumulation, and histological scores for inflammation by 4 weeks in the first study. In the second study, the LFD ameliorated this NAFLD phenotype after 5 weeks of feeding; however, the SPI intervention failed to significantly attenuate NAFLD. QUS parameters were significantly increased with the HFDs (P < .05) and steatosis grade (P < .05) and were positively correlated with hepatic lipid concentrations. In conclusion, dietary modification may be effective at reversing NAFLD and NASH at early stages. Furthermore, QUS may become a valuable tool to track hepatic steatosis. Additional studies are needed to further evaluate the effectiveness of these interventions.

ObjectivesTo assess inter-platform reproducibility of ultrasonic attenuation coefficient (AC) and backscatter coefficient (BSC) estimates in adults with known/suspected nonalcoholic fatty liver disease (NAFLD).MethodsThis HIPAA-compliant prospective study was approved by an institutional review board; informed consent was obtained. Participants with known/suspected NAFLD were recruited and underwent same-day liver examinations with clinical ultrasound scanner platforms from two manufacturers. Each participant was scanned by the same trained sonographer who performed multiple data acquisitions in the right liver lobe using a lateral intercostal approach. Each data acquisition recorded a B-mode image and the underlying radio frequency (RF) data. AC and BSC were calculated using the reference phantom method. Inter-platform reproducibility was evaluated for AC and log-transformed BSC (logBSC = 10log10BSC) by intraclass correlation coefficient (ICC), Pearson's correlation, Bland-Altman analysis with computation of limits of agreement (LOAs), and within-subject coefficient of variation (wCV; applicable to AC).ResultsSixty-four participants were enrolled. Mean AC values measured using the two platforms were 0.90 ± 0.13 and 0.94 ± 0.15dB/cm/MHz while mean logBSC values were - 30.6 ± 5.0 and - 27.9 ± 5.6dB, respectively. Inter-platform ICC was 0.77 for AC and 0.70 for log-transformed BSC in terms of absolute agreement. Pearson's correlation coefficient was 0.81 for AC and 0.80 for logBSC. Ninety-five percent LOAs were - 0.21 to 0.13dB/cm/MHz for AC, and - 9.48 to 3.98dB for logBSC. The wCV was 7% for AC.ConclusionsHepatic AC and BSC are reproducible across two different ultrasound platforms in adults with known or suspected NAFLD.Key points• Ultrasonic attenuation coefficient and backscatter coefficient are reproducible between two different ultrasound platforms in adults with NAFLD. • This inter-platform reproducibility may qualify quantitative ultrasound biomarkers for generalized clinical application in patients with suspected/known NAFLD.

OBJECTIVES To show that quantitative ultrasound biomarkers attenuation (AC) and backscatter (BSC) coefficients are effective tools to detect early changes in acute pancreatitis, using a cerulein-induced pancreatitis rat model. METHODS Sprague-Dawley rats (n = 68) were divided into 8 groups: uninjected cage controls, saline-injected controls, and cerulein-injected rats euthanized at 2, 4, 15, 24, 48, and 60 hours after injection. Pancreatic AC and BSC (25-55 MHz) were estimated in vivo (Vevo 2100, VisualSonics, Toronto, CA) and ex vivo (40-MHz transducer). The pancreas of each rat was evaluated histopathologically. RESULTS Changes in both in vivo and ex vivo AC and BSC relative to controls reflected temporal histomorphologic changes. Overall, there were decreased AC and BSC at early time points and then rebound toward control values over time. Maximal in vivo AC and BSC decreases occurred at 2 hours after cerulein injection. Attenuation coefficient changes corresponded well with early pancreatic edema and acinar cell vacuolation, with rebound as edema decreased, autophagy/cellular death occurred, and histiocytic infiltrates and fibrosis manifested. Backscatter coefficient decreased early but rebounded as autophagy and apoptosis increased, only to fall as acinar atrophy peaked, and fibrosis and histiocytic infiltration increased. CONCLUSIONS Cerulein-induced pancreatitis is an excellent model for studying ultrasonic AC and BSC biomarkers during the early stages of acute pancreatitits, reflecting microscopic structural changes. Edema followed by cell shrinkage and apoptosis, then histiocytic infiltration and fibrosis, has certain similarities with the morphologies of some forms of pancreatic carcinoma. This suggests that quantitative ultrasound may be very useful for early detection of disease onset or response to therapy for not only acute pancreatitis but also pancreatic cancer.

Quantitative ultrasound (QUS) techniques are diagnostically useful in assessing nonalcoholic fatty liver disease (NAFLD). From previous studies, the backscatter coefficient (BSC) correlates with liver fat fraction, but the mechanisms have not been determined. Understanding the ultrasound scattering mechanisms will lead to better acoustic scattering models and more accurate diagnostics. The purpose of this study is to test the following hypothesis: Fat droplet deposition in hepatocytes alters the locations of hepatocyte nuclei, changing the spatial distribution of the nuclei, which leads to the change in the structure function (SF), a factor of the BSC. The SF, determined by the distribution of the nuclei, is correlated with the fat fraction. To test this hypothesis, hematoxylin and eosin-stained liver histopathological slides from 48 participants were digitized (40×). One to 5 regions (453.6 µm × 453.6 µm) were selected from each participant's slide(s), yielding 218 images in total. For each image, hepatocyte nuclei and fat droplets each were automatically recognized. SF versus frequency was calculated from the nuclear distribution. Liver fat fraction was determined from the fractional surface area of fat droplets. SF was positively correlated with the liver fat fraction (Pearson's r ~ 0.4, p < 10−4) below 30 MHz (including clinically relevant frequencies 3 - 5 MHz). In conclusion, this study shows the fat droplets change the distribution of hepatocyte nuclei, which is a factor contributing to the correlation between fat fraction and BSC.

It is important to accurately calibrate the acoustic parameters of reference phantoms used in quantitative ultrasound (QUS) applications. However, laminate membranes used on the exterior of the phantoms complicate the attenuation coefficient (AC) measurements by introducing transmission loss. The existing through-transmission method addresses this issue by characterizing additional and separate membrane and phantom material specimens to estimate the membrane transmission loss. An alternative and simpler method is proposed, that uses a single phantom to simultaneously measure the membrane transmission loss and phantom AC. This is achieved using a single-element transducer in a broadband pulse-echo setup in three steps. Firstly, signal loss due to phantom insertion (insertion loss), composed of phantom attenuation and membrane transmission loss, is measured. Secondly, the membrane reflection coefficient is measured by comparing the echo from membrane surface to the echo from of a well-studied surface such as Plexiglas. Furthermore, the measured membrane reflection coefficient is compared with the theoretical membrane reflection coefficient to estimate the unknown acoustic parameters of the membrane and phantom material. Finally, these parameters are used in estimating the membrane transmission loss, which, in turn, is used to estimate the phantom AC from the insertion loss obtained in the first step. Two phantoms were used to validate the proposed method. AC of both phantoms were measured using the existing method, yielding a range of 0.5-1.2 dB/cm-MHz over 1.6-6.4 MHz. The AC estimates acquired using the proposed method showed good agreement with the existing method. Between the two methods, a root mean square AC difference of 0.006 dB/cm-MHz for the first phantom and 0.014 dB/cm-MHz for the second phantom was observed

BACKGROUND: Radiofrequency ultrasound data from the liver contain rich information about liver microstructure and composition. Deep learning might exploit such information to assess nonalcoholic fatty liver disease (NAFLD). PURPOSE: To develop and evaluate deep learning algorithms that use radiofrequency data for NAFLD assessment, with MRI-derived proton density fat fraction (PDFF) as the reference. MATERIALS AND METHODS: A HIPAA-compliant secondary analysis of a single-center prospective study was performed for adult participants with NAFLD and control participants without liver disease. Participants in the parent study were recruited between February 2012 and March 2014 and underwent same-day US and MRI of the liver. Participants were randomly divided into an equal number of training and test groups. The training group was used to develop two algorithms via cross-validation: a classifier to diagnose NAFLD (MRI PDFF ≥ 5%) and a fat fraction estimator to predict MRI PDFF. Both algorithms used one-dimensional convolutional neural networks. The test group was used to evaluate the classifier for sensitivity, specificity, positive predictive value, negative predictive value, and accuracy and to evaluate the estimator for correlation, bias, limits of agreements, and linearity between predicted fat fraction and MRI PDFF. RESULTS: A total of 204 participants were analyzed, 140 had NAFLD (mean age, 52 years ± 14 [standard deviation]; 82 women) and 64 were control participants (mean age, 46 years ± 21; 42 women). In the test group, the classifier provided 96% (95% confidence interval [CI]: 90%, 99%) (98 of 102) accuracy for NAFLD diagnosis (sensitivity, 97% [95% CI: 90%, 100%], 68 of 70; specificity, 94% [95% CI: 79%, 99%], 30 of 32; positive predictive value, 97% [95% CI: 90%, 99%], 68 of 70; negative predictive value, 94% [95% CI: 79%, 98%], 30 of 32). The estimator-predicted fat fraction correlated with MRI PDFF (Pearson r = 0.85). The mean bias was 0.8% (P = .08), and 95% limits of agreement were -7.6% to 9.1%. The predicted fat fraction was linear with an MRI PDFF of 18% or less (r = 0.89, slope = 1.1, intercept = 1.3) and nonlinear with an MRI PDFF greater than 18%. CONCLUSION: Deep learning algorithms using radiofrequency ultrasound data are accurate for diagnosis of nonalcoholic fatty liver disease and hepatic fat fraction quantification when other causes of steatosis are excluded. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Lockhart and Smith in this issue.

In recent years, defective TiO2-based composite nanomaterials have received much attention in the field of photocatalysis. In this work, TiB2 was used as a precursor to successfully prepare Ti3+ defective TiO2 (TiO2-B) with a truncated bipyramidal structure by a one-step method. Then, the SnS2 nanosheets were assembled onto the as-prepared TiO2-B through simple hydrothermal reaction. TiO2-B exhibits strong visible light absorption properties, but the recombination rate of the photo-generated electron-hole pair was high and does not exhibit ideal photocatalytic performance. Upon introducing SnS2, the heterojunction catalyst SnS2-Ti3+ defective TiO2 (SnS2/TiO2-B) not only possesses the strong light absorption from UV to visible light region, the lowest photo-generated charge recombination rate but also achieves a more negative conduction band potential than the reduction potential of CO2 to CO, and thereby, exhibits the significantly enhanced selectivity and yield of CO in photocatalytic CO2 reduction. Notably, SnS2/TiO2-B produces CO at a rate of 58 &micro, mol&middot, h&minus, 1&middot, g&minus, 1 with CO selectivity of 96.3% under visible light irradiation, which is 2 and 19 times greater than those of alone TiO2-B and SnS2, respectively. Finally, a plausible photocatalytic mechanism on SnS2/TiO2-B was proposed that the electron transfer between TiO2 and SnS2 follows the Z-scheme mode. Our results present an effective way to gain highly efficient TiO2 based photocatalysts for CO2 reduction by combining different modification methods of TiO2 and make full use of the synergistic effects.

Nonalcoholic fatty liver disease (NAFLD) affects ~25% of the world population. Confounder-corrected chemical-shift-encoded MRI-derived proton density fat fraction (MRI-PDFF) is an established quantitative noninvasive biomarker of hepatic steatosis but has limited availability. There is a clinical need for more practical and accessible methods to noninvasively assess hepatic steatosis. Previous work has shown that two quantitative ultrasound (QUS) biomarkers - attenuation coefficient (AC) and backscatter coefficient (BSC) - are correlated with hepatic steatosis. Examining a broad range of QUS biomarkers, this study aims to develop an improved, multi-parametric QUS-based approach to diagnose NAFLD and quantify hepatic fat, with MRI-PDFF as the reference standard. 102 participants recruited from the UCSD NAFLD Research Center underwent QUS exams on the right liver lobe with an Acuson S3000 ultrasound scanner and the 4C1 and 6C1HD transducers. Seven QUS biomarkers - AC, BSC, three Lizzi-Feleppa parameters (slope, intercept, midband), and two envelope parameters (k and μ) - were derived from ultrasound radiofrequency data. Two multivariable models were developed based on QUS biomarkers: a generalized linear regression model to predict hepatic PDFF using stepwise regression for biomarker selection and a regularized logistic regression model to classify NAFLD (MRI-PDFF>5%, N=78/102) versus no NAFLD (MRI-PDFF≤5%) using LASSO regularization for biomarker selection. Leave-one-out cross-validation was performed. The final regression model selected the midband and k-parameter. The cross-validated predicted PDFF values were correlated with the reference MRI-PDFF values (Spearman ρ = 0.82 and Pearson’s r = 0.76). In comparison, Pearson’s r was 0.59 between AC and MRI-PDFF and 0.58 between BSC and MRI-PDFF. The final classifier model selected the midband, k-parameter and μ-parameter, achieving an area under the receiver operating characteristic curve (AUROC) of 0.88. In comparison, AUROC was 0.83 using AC and 0.84 using BSC. The results suggest that multi-parametric QUS can improve the quantification of hepatic steatosis and diagnosis of NAFLD.

Author(s): Han, Aiguo; Andre, Michael P; Deiranieh, Lisa; Housman, Elise; Erdman, John W; Loomba, Rohit; Sirlin, Claude B; O'Brien, William D | Abstract: OBJECTIVES:To assess the repeatability and reproducibility of the ultrasonic attenuation coefficient (AC) and backscatter coefficient (BSC) measured in the livers of adults with known or suspected nonalcoholic fatty liver disease (NAFLD). METHODS:The Institutional Review Board approved this Health Insurance Portability and Accountability Act-compliant prospective study; informed consent was obtained. Forty-one research participants with known or suspected NAFLD were recruited and underwent same-day ultrasound examinations of the right liver lobe with a clinical scanner by a clinical sonographer. Each participant underwent 2 scanning trials, with participant repositioning between trials. Two transducers were used in each trial. For each transducer, machine settings were optimized by the sonographer but then kept constant while 3 data acquisitions were obtained from the liver without participant repositioning and then from an external calibrated phantom. Raw RF echo data were recorded. The AC and BSC were measured within 2.6 to 3.0 MHz from a user-defined hepatic field of interest from each acquisition. The repeatability and reproducibility were analyzed by random-effects models. RESULTS:The mean AC and log-transformed BSC (logBSC) were 0.94 dB/cm-MHz and -27.0 dB, respectively. Intraclass correlation coefficients were 0.88 to 0.94 for the AC and 0.87 to 0.95 for the logBSC acquired without participant repositioning. For between-trial repeated scans with participant repositioning, the intraclass correlation coefficients were 0.80 to 0.84 for the AC and 0.69 to 0.82 for the logBSC after averaging results from 3 within-trial images. The variability introduced by the transducer was less than the repeatability error. CONCLUSIONS:Hepatic AC and BSC measures using a reference phantom technique on a clinical scanner are repeatable and reproducible between transducers in adults with known or suspected NAFLD.

In this paper, a new machine learning based framework is developed for the energy policy and operation management of the smart grids, utilizing advanced support vector networks in the renewable smart grids (RSGs), considering storage unit, wind and tidal systems and dispatchable units. The proposed system first develops a support vector regression (SVR) for prediction of the tidal and wind units output power with high accuracy. In the second step, an energy policy system is devised which forces the system operator to support renewable sources by guaranteeing the full purchase of their generation. In the third step, the optimal energy management framework is launched which optimizes the operation costs when considering the practical constraints. In the proposed novel framework, a new optimization method based on fuzzy dragonfly algorithm (FDA) is developed to enhance the search performance by creating adjusting fuzzy version this algorithm. In order to handle the uncertainty effects, a reduced scenario based approach is developed which shows high accuracy of 95% confidence level but with trivial computational time. The system quality is assessed on a test RSG system. The results prove the contributing claims of the research, clearly.

This study uses in vivo radiofrequency ultrasound data acquired from human cervices to compare two commonly used spectral-based techniques for estimating the ultrasonic attenuation coefficient (AC): the spectral difference (SD) and the spectral log difference (SLD) techniques. The AC is a fundamental quantitative ultrasound (QUS) parameter useful for tissue characterization to improve diagnostics, e.g., cervix characterization to predict preterm birth. The selection of appropriate AC techniques is a critical step for QUS tissue characterization. The advantages and disadvantages of various AC estimation techniques have been studied using physical phantoms and computational simulations. However, the heterogeneous nature of real tissue cannot be fully simulated with phantoms and computations. In this study, the SD and SLD techniques were evaluated using human cervices from 214 pregnant women (enrollment still ongoing). Each participant underwent 1-2 cervical QUS exams during each visit. In each exam, 10 acquisitions of radiofrequency ultrasound and one acquisition of a physical phantom were made using a Siemens MC9-4 transvaginal ultrasound transducer (center frequency: 5.25 MHz) with a Siemens S2000 ultrasound system. Preliminary analysis yielded correlated AC estimates (Pearson’s r = 0.66

To assess inter-sonographer reproducibility of ultrasound attenuation coefficient (AC), backscatter coefficient (BSC) and shear wave speed (SWS) in adults with known/suspected non-alcoholic fatty liver disease (NAFLD). The institutional review board approved this HIPAA-compliant prospective study; informed consent was obtained. Participants with known/suspected NAFLD were recruited and underwent same-day liver examinations with a clinical scanner. Each participant was scanned by two of the six trained sonographers. Each sonographer performed multiple data acquisitions in the right liver lobe using a lateral intercostal approach. A data acquisition was a single operator button press that recorded a B-mode image, radio-frequency data, and the SWS value. AC and BSC were calculated from the radio-frequency data using the reference phantom method. SWS was calculated automatically using product software. Intraclass correlation coefficient (ICC) and within-subject coefficient of variation (wCV) were calculated for applicable metrics. Sixty-one participants were recruited. Inter-sonographer ICC was 0.86 (95% confidence interval: 0.77–0.92) for AC and 0.87 (0.78–0.92) for log-transformed BSC (logBSC = 10log10BSC) using one acquisition per sonographer. ICC was 0.88 (0.80–0.93) for both AC and logBSC averaging 5 acquisitions. ICC for SWS was 0.57 (0.29–0.74) using one acquisition per sonographer, and 0.84 (0.66–0.93) using 10 acquisitions. The wCV was ~7% for AC, and 19–43% for SWS, depending on number of acquisitions. Hepatic AC, BSC and SWS measures on a clinical scanner have good inter-sonographer reproducibility in adults with known or suspected NAFLD. Multiple acquisitions are required for SWS but not AC or BSC to achieve good inter-sonographer reproducibility. • AC, BSC and SWS measurements are reproducible in adults with NAFLD. • Inter-sonographer reproducibility of SWS measurement improves with more acquisitions being averaged. • Multiple acquisitions are required for SWS but not AC or BSC.

With the development of economy and science and technology in China, pile group foundation is widely used in various constructions to meet the complex engineering conditions. Pile foundation is often affected by inclined load (i.e. horizontal load and vertical load). The behavior of pile under inclined load is much more complex than that under single load. In engineering, the vertical component and the horizontal component of the pile top are often calculated separately, and then modified. This method is only suitable for small deformation, which is different from the actual situation. In this paper, taking the four pile foundation as an example, through the rotation of the cushion cap (the column does not rotate) and the column eccentricity, the force of the foundation pile can be relatively uniform. At the same time, the design calculation of the group pile foundation under the oblique load can be approximately equivalent to the design calculation of the group pile foundation under the single vertical load. When the force distribution on the pile top is relatively uniform under the same inclined load, the eccentricity of the column has a linear relationship with the depth of the cap. Through ABAQUS numerical simulation analysis, the correctness of the above understanding is verified.

Quantitative ultrasound (QUS) techniques are emerging to be useful for assessing nonalcoholic fatty liver disease (NAFLD), the accumulation of fat droplets in the liver without alcohol intake. Several QUS parameters, such as the backscatter coefficient (BSC), are known to be correlated with liver fat content. However, the underlining ultrasonic scattering mechanisms are not fully understood. Understanding these mechanisms may yield better QUS models for improved diagnostics. This study's purpose is to test the following hypothesized mechanism: hepatocyte nuclei are acoustic scattering sites and the accumulation of fat droplets changes the spatial distribution of hepatocyte nuclei, leading to the change of the structure function (SF), a component of the BSC. Hematoxylin and eosin-stained liver histopathological slides from 48 participants were digitized. Hepatocyte nuclei and fat droplets were automatically recognized. For each participant, the SF versus frequency was calculated from the nuclear distribution. The fat fraction (FF) was determined by the fractional surface area of fat droplets. The SF was positively correlated with the FF (Pearson's r ∼ 0.4, p < 10-4) between frequencies 1 and 30 MHz, supporting the hypothesized mechanism. Other potential mechanisms, such as the additional scattering caused by fat droplets, will also be discussed. [Work supported by R01DK106419 and R01CA226528-01A1.]