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Background: Former studies showed that magnetic resonance cholangiopancreatography (MRCP) is useful in diagnosing the presence of santorinicele; however, few studies have evaluated MRCP in diagnosing wirsungocele and the association between pancreatitis and santorinicele or wirsungocele. The purpose of the study was to explore the performance of MRCP in diagnosing santorinicele and wirsungocele and investigate the potential association among pancreatitis, pancreas divisum, and santorinicele or wirsungocele. Method: Sixty-five patients (mean age, 55.68 years; range, 11-82 years) with santorinicele or wirsungocele were included and sorted into two groups: the santorinicele group (n = 48) and the wirsungocele group (n = 17). All patients underwent MRCP. The images were evaluated for the appearance and size of santorinicele or wirsungocele. The diagnostic sensitivity of MRCP was assessed. Additionally, whether two groups are correlated with pancreas divisum or pancreatitis were investigated. Result: The sensitivity of MRCP in detecting santorinicele and wirsungocele showed no difference (68.8% and 76.5%, respectively). The proportion of patients who developed pancreatitis in santorinicele and wirsungocele groups were 60.4% and 11.8%, respectively (p < 0.05). Pancreas divisum accounted for 77.1% and 11.8% of the patients in the santorinicele and wirsungocele groups, respectively (p < 0.05). Patients with santorinicele and pancreas divisum tended to be older when they acquired pancreatitis. Conclusion: MRCP could be an alternative imaging method to detect cystic dilation of the pancreatic duct. Pancreatitis is more common in patients with santorinicele than in those with wirsungocele. Moreover, santorinicele is more closely associated with pancreatitis than with pancreas divisum.

With the great improvement of computer performance, multi-physics and high-fidelity reactor numerical simulation has attracted widespread attention. The development of a three-dimensional fine-mesh coupled neutronics and thermal-hydraulics procedure mainly using the free open source C++ library OpenFOAM is presented in this research. The coupling codes are allowed to solve the coupled neutronic and thermal-hydraulic problem within the same one procedure for both steady-state and transient conditions, avoiding the data transmission between different programs. The coupling strategy among two physical fields is implemented in the same fully three-dimensional and fine mesh system, which eliminates numerical errors caused by the mapping of the grid. For the thermal-hydraulics, the built-in solid-fluid coupling conjugate heat transfer solver is applied for the calculation of the fluid mass, momentum, and energy equations, together with the solid energy equation to obtain the temperature distribution. The neutron diffusion equation is solved iteratively via the developed three-dimensional multi-group multi-region neutron diffusion solver to get the power distribution. The macroscopic cross-sections are pre-generated by the Monte Carlo code OpenMC and fitted as functions of temperature added in the neutron diffusion solver. The temperature distribution obtained by thermal-hydraulics calculation will change the macroscopic cross-sections and then impact the neutron diffusion calculation, while the power distribution gained from the neutronic calculation is transferred to the thermal-hydraulics calculation and is used as the heat source term. This coupling methodology are tested on a 3 × 3 PWR fuel pins model. The results show that all physical fields conform correct distribution regularity, illustrating the feasibility of the coupling methodology.

The Bayesian Maximum Entropy (BME) algorithm that can model larger-scale spatial heterogeneity and integrate multiple types of data is a better spatial estimation algorithm in avoiding the circular...

Objectives We aim to evaluate a deep learning (DL) model and radiomic model for preoperative differentiation of nodular cryptococcosis from solitary lung cancer in patients with malignant features on CT images. Materials and methods We retrospectively recruited 319 patients with solitary pulmonary nodules and suspicious signs of malignancy from three hospitals. All lung nodules were resected, and one by one radiologic-pathologic correlation was performed. A three-dimensional DL model was used for tumor segmentation and extraction of three-dimensional radiomic features. We used the Max-Relevance and Min-Redundancy algorithm and the eXtreme Gradient Boosting algorithm to select the nodular radiomics features. We proposed a DL local-global model, a DL local model and radiomic model to preoperatively differentiate nodular cryptococcosis from solitary lung cancer. The DL local-global model includes information of both nodules and the whole lung, while the DL local model only includes information of solitary lung nodules. Five-fold cross-validation was used to select and validate these models. The prediction performance of the model was evaluated using receiver operating characteristic curve (ROC) and calibration curve. A new loss function was applied in our deep learning framework to optimize the area under the ROC curve (AUC) directly. Results 295 patients were enrolled and they were non-symptomatic, with negative tumor markers and fungus markers in blood tests. These patients have not been diagnosed by the combination of CT imaging, laboratory results and clinical data. The lung volume was slightly larger in patients with lung cancers than that in patients with cryptococcosis (3552.8 ± 1184.6 ml vs 3491.9 ± 1017.8 ml). The DL local-global model achieved the best performance in differentiating between nodular cryptococcosis and lung cancer (area under the curve [AUC] = 0.88), which was higher than that of the DL local model (AUC = 0.84) and radiomic (AUC = 0.79) model. Conclusion The DL local-global model is a non-invasive diagnostic tool to differentiate between nodular cryptococcosis and lung cancer nodules which are hard to be diagnosed by the combination of CT imaging, laboratory results and clinical data, and overtreatment may be avoided.

To investigate the incidence, CT appearance, and implication for prognosis of the hollow adrenal gland sign (HAGS). A total of 194 patients with septic shock and 24 patients with hemorrhagic shock (as control group) were retrospectively included in this study and the patients with septic shock were further divided into four subgroups (digestive tract diseases, DTD, n = 49; biliary and pancreatic diseases, BPD, n = 41; postsurgical infection, PI, n = 64; and other diseases, OD, n = 40). All patients underwent a dual-phase contrast-enhanced CT within 1 week after diagnosis. CT findings and clinical records were reviewed. If in the arterial phase the central zone of adrenal gland showed temporally much lower attenuation than the peripheral zone, it was defined as HAGS positive. The incidence of the HAGS in patients with septic shock and hemorrhagic shock, the demographic features, and mortality between HAGS-positive and HAGS-negative patients in each group were respectively compared. The incidence of the HAGS in the septic shock group was nearly 30%, while it was 0 in the hemorrhagic shock group. There was no significant difference in age or gender between HAGS-positive and HAGS-negative patients in all groups, while the mortality of HAGS-positive patients was significantly higher than that of HAGS-negative patients in each group (p

Our study aimed to evaluate myocardial strain and tissue characteristics by multiparametric cardiovascular magnetic resonance (CMR) imaging in end-stage renal disease (ESRD) patients on peritoneal dialysis with preserved left ventricular ejection fraction (LVEF). ESRD patients on peritoneal dialysis with echocardiographic LVEF > 50% and age- and sex-matched healthy volunteers underwent multiparametric CMR at 3 T. LV function, LV myocardial native T1 and T2, and biventricular strain were measured and compared between the patients and controls. Associations of LV myocardial mass index (LVMI) with tissue characterization and strain were evaluated by multiple linear regression. A total of 65 subjects (42 healthy volunteers and 23 ESRD patients) were enrolled. ESRD group demonstrated larger LVMI, higher native T1 and T2 (1301.9 ± 30.6 ms, 44.6 ± 2.6 ms) than those of the control group (1255.8 ± 45.2 ms, 40.5 ± 1.6 ms; both p < 0.001). Decreased LV strain and increased right ventricular circumferential strain were observed in the ESRD group. In ESRD patients with normal diastolic function on echocardiography, native T1 and T2 values were higher than those of the control group (p = 0.006, p = 0.001). Increased LVMI was associated with increased native T1 (p = 0.001) and T2 value (p < 0.001) after adjusting for age and sex. Increased myocardial native T1 value was associated with reduced LV strain after adjusting age, sex, and LVMI. ESRD patients on peritoneal dialysis with preserved LVEF demonstrated higher myocardial mass, higher native T1 and T2 values, decreased LV strain, and increased RVGCS compared with healthy controls. Increased myocardial T1 and T2 were found in ESRD even when no systolic or diastolic dysfunction was detected by routine echocardiography. • Even with preserved LVEF and no known cardiovascular diseases, ESRD patients on peritoneal dialysis demonstrated elevated myocardial T1 and T2 values and decreased left ventricular strain. • Subclinical changes in myocardial tissue composition may exist in ESRD patients on peritoneal dialysis even when no systolic or diastolic dysfunction was detected by routine echocardiography based on ejection fraction, left atrium size, and tissue Doppler. • Right ventricular free wall strain could be enhanced in response to subclinical LV systolic dysfunction in ESRD patients on peritoneal dialysis with preserved LVEF at an early stage.

BACKGROUND: Subjective cognitive decline plus could be an extremely early phase of Alzheimer’s disease; however, changes of N-acetylaspartate, myoinositol, and N-acetylaspartate/myoinositol is still unknown at this stage. This study aimed to explore brain neurometabolic alterations in patients with subjective cognitive decline plus using quantitative single-voxel and multi-voxel (1)H-magnetic resonance spectroscopy. METHODS: A total of 91 participants were enrolled and underwent a GE 3.0-T magnetic resonance imaging, including 33 elderly controls, 27 patients with subjective cognitive decline plus, and 31 patients with amnestic mild cognitive impairment (MCI). Single-voxel and multi-voxel (1)H-magnetic resonance spectroscopy were used to investigate the differences in neurometabolite levels among the three groups. RESULTS: Compared with elderly controls, patients with subjective cognitive decline plus showed significant decline in N-acetylaspartate and N-acetylaspartate/myoinositol values in multiple regions, and amnestic MCI participants demonstrated more significant decreased N-acetylaspartate and N-acetylaspartate/myoinositol levels in multiple regions. The combined concentrations of N-acetylaspartate with myoinositol showed an excellent discrimination between those with subjective cognitive decline plus and elderly controls as compared to that obtained using N-acetylaspartate/myoinositol ratios with the area under the receiver operating characteristic curve of 0.895 and 0.860, respectively. Likewise, the combined area under the curve for differentiating patients with subjective cognitive decline plus from amnestic MCI was obtained using the combined levels of N-acetylaspartate with myoinositol was 0.892. This was also higher than the combined area under the curve of 0.836 obtained using N-acetylaspartate/myoinositol ratios. Moreover, N-acetylaspartate levels in the left hippocampus and left posterior cingulate cortex (PCC) was positively related to the Auditory Verbal Learning Test delayed recall scores in patients with subjective cognitive decline plus, whereas only the N-acetylaspartate/myoinositol ratio was positively related to this scale scores in the left hippocampus. CONCLUSIONS: Quantitative single-voxel and multi-voxel (1)H-magnetic resonance spectroscopy can provide valuable information to detect alterative brain neurometabolites characteristics in patients with subjective cognitive decline plus. N-acetylaspartate concentrations may be used as one of the earliest neuroimaging markers at this stage, while N-acetylaspartate/myoinositol ratio could be more suitable for monitoring Alzheimer’s disease progression.

The ASCAT (Advanced SCATterometer) soil moisture product with 10-km spatial resolution was retrieved based on the soil water index (SWI) algorithm from the data acquired by the scatterometer on board the Meteorological OPerational (MetOP) satellites (MetOP-A, MetOP-B). In this study, the ASCAT product was downscaled from 10-km to 1-km spatial resolution based on the Apparent Thermal Inertia (ATI) estimated from MODIS Land Surface Temperature (LST) and Albedo retrievals in 54 grids (1 degree ∗1 degree) around 54 FLUXNET stations. First, the ATI was estimated at 1-km spatial resolution by using MODIS LST and Albedo data at the same spatial resolution and then resampled to 10-km. Second, the relationship between ASCAT soil moisture and ATI at 10-km spatial resolution was established. Finally, the spatiotemporally continuous soil moisture at 1-km spatial resolution was retrieved using the obtained relationship between ATI and ASCAT at 10-km spatial resolution, and the ATI data at 1-km spatial resolution. However, there were many missing values in the MODIS LST maps leading to spatiotemporal discontinuity in LST and calculated ATI data. To obtain spatiotemporal continuous ATI data, this study first reconstructed the MODIS LST data by finding similar points that had the same land cover type and similar NDVI (the Normalized Difference Vegetation Index) value. In this study, we found that the LST data of similar points in a pair of temporal adjacent LST images had a linear relationship. The LST data of these similar points in a pair of temporal adjacent LST images were used to establish a linear relationship and then used to reconstruct the pair of temporally adjacent LST images. The reconstructed LST data were used to obtain the spatiotemporal continuous ATI data at 1-km and 10-km spatial resolutions. In this study, downscaled 1-km spatial resolution soil moisture product within the 54 grids around the FLUXNET sites were obtained in 2013. Results indicated that the spatial distribution of the downscaled soil moisture using the reconstructed MODIS LST data is better than that using original MODIS LST data. Additionally, the downscaled soil moisture was evaluated against in-situ soil moisture measurements at 54 FLUXNET stations. The average of RMSE (the Root Mean Square Error) was 0.098 m3m−3 and the average of MAE (the Mean Absolute Error) was 0.08 m3m−3.

Objective:Unsuspected pulmonary embolism (UPE) has been increasingly diagnosed as an incidental finding on CT scans for routine staging in cancer patients. Previous studies suggest that obesity is an independent risk factor for venous thromboembolism in patients with malignant tumors. In this study, we aimed to investigate the association between abdominal adipose tissue, especially visceral adipose tissue (VAT) and the occurrence of UPE in hospitalized patients with gastrointestinal cancer.Methods:Routine contrast-enhanced chest and abdominal CT scans of 1974 patients were retrospectively assessed for the presence of UPE, of which 58 patients were identified with UPE and 108 non-UPE patients were selected as the non-UPE control group based on several matching criteria. Abdominal adipose tissue was measured by volumes of VAT and subcutaneous adipose tissue (SAT) at the navel level.Results:VAT, SAT, indwelling venous catheters, surgery, chemotherapy, and bed rest or immobilization were associated with the occurrence of UPE. Higher VAT volumes were associated with increased risk of UPE (odds ratio: 1.96; 95% confidence interval: 1.25, 3.06; p = 0.003) adjusting body mass index (BMI), bed rest or immobilization, surgery, chemotherapy and smoking, while SAT was not associated with UPE adjusting the same confounders (p = 0.117). No statistical association was found between BMI and UPE (p = 0.102).Conclusion:Higher VAT rather than SAT is associated with an increased risk of unsuspected pulmonary embolism on routine CT scans in hospitalized gastrointestinal cancer patients.Advances in knowledge:Our findings indicate that VAT is a stronger risk factor for unsuspected pulmonary embolism than BMI and SAT in hospitalized patients with gastrointestinal cancer.

Background: AAD refers to the blood flow into the middle membrane through the intimal rupture of the aorta. HPS is a common complication of Stanford-A AAD. The risk factors of HPS are remaining unclear Methods: In this study, we have probed the potential risk factors of hepatopulmonary syndrome (HPS) patients with acute Stanford A aortic dissection. 18 HPS patients with acute Stanford A aortic dissection were selected as case group. 36 Normal people were considerate as control group. Demographic data, treatment methods, AD related disease history, clinical symptoms and Charlson comorbidity index (CCI) values of each patient were collected. Meanwhile, the values of maximum diameter of ascending aorta (mm), aortic dissection range, and main branch of aorta, pleural effusion/blood, and pericardial effusion/blood were measured by two experienced cardiovascular radiological physicians. Univariate and multivariate conditional logistic regression analysis were used in this study. Results: CCI value and the branches of the brachiocephalus in case group were significant higher than that in control group (p

The AMSR-E (Advanced Microwave Scanning Radiometer - EOS) sensor onboard NASA’s Aqua satellite is a passive microwave radiometer and is widely used in land surface Soil Moisture (SM) retrieval. The NASA National Snow and Ice Data Center Distributed Active Archive Center (NSIDC DAAC) archives and distributes a daily SM product (AMSR-E/NASA) from 2002 to 2011 at 25km resolution retrieved by a simplified soil moisture retrieval algorithm derived from a simplified radiative transfer model. However, the SM dynamic range of this product is very narrow, which basically cannot reflect intra- and inter-annual variations. This paper analyzed the AMSR-E/NASA SM product and compared it with the AMSR-E/JAXA SM. Furthermore, the reason of the narrow intra- and inter-annual variation of AMSR-E/NASA SM was analyzed in depth. Finally, the AMSR-E/NASA SM product was improved using in-situ data collected in the Tibetan Plateau. Results indicated that the improved monthly SM AMSR-E product was better than AMSR-E/NASA and AMSR-E/JAXA in Naqu study area from January to September of 2011, as documented by a lower RMSE (Root Mean Square Error) against in-situ SM data, i.e. 0.065 cm3 cm-3.

Graph Convolutional Networks (GCNs) are a novel and powerful method for dealing with non-Euclidean data, while Convolutional Neural Networks (CNNs) can learn features from Euclidean data such as images. In this work, we propose a novel method to combine CNNs with GCNs (CNN-GCN), that can consider both Euclidean and non-Euclidean features and can be trained end-to-end. We applied this method to separate the pulmonary vascular trees into arteries and veins (A/V). Chest CT scans were pre-processed by vessel segmentation and skeletonization, from which a graph was constructed: voxels on the skeletons resulting in a vertex set and their connections in an adjacency matrix. 3D patches centered around each vertex were extracted from the CT scans, oriented perpendicularly to the vessel. The proposed CNN-GCN classifier was trained and applied on the constructed vessel graphs, where each node is then labeled as artery or vein. The proposed method was trained and validated on data from one hospital (11 patient, 22 lungs), and tested on independent data from a different hospital (10 patients, 10 lungs). A baseline CNN method and human observer performance were used for comparison. The CNN-GCN method obtained a median accuracy of 0.773 (0.738) in the validation (test) set, compared to a median accuracy of 0.817 by the observers, and 0.727 (0.693) by the CNN. In conclusion, the proposed CNN-GCN method combines local image information with graph connectivity information, improving pulmonary A/V separation over a baseline CNN method, approaching the performance of human observers.

Soil moisture is the basic condition required for crop growth and development. Gaofen-3 (GF-3) is the first C-band synthetic-aperture radar (SAR) satellite of China, offering broad land and ocean imaging applications, including soil moisture monitoring. This study developed an approach to estimate soil moisture in agricultural areas from GF-3 data. An inversion technique based on an artificial neural network (ANN) is introduced. The neural network was trained and tested on a training sample dataset generated from the Advanced Integral Equation Model. Incidence angle and HH or VV polarization data were used as input variables of the ANN, with soil moisture content (SMC) and surface roughness as the output variables. The backscattering contribution from the vegetation was eliminated using the water cloud model (WCM). The acquired soil backscattering coefficients of GF-3 and in situ measurement data were used to validate the SMC estimation algorithm, which achieved satisfactory results (R2 = 0.736; RMSE = 0.042). These results highlight the contribution of the combined use of the GF-3 synthetic-aperture radar and Landsat-8 images based on an ANN method for improving SMC estimates and supporting hydrological studies.

Soil moisture (SM) plays important roles in surface energy conversion, crop growth, environmental protection, and drought monitoring. As crops grow, the associated vegetation seriously affects the ability of satellites to retrieve SM data. Here, we collected such data at different growth stages of maize using Bragg and X-Bragg scattering models based on the Freeman–Durden polarization decomposition method. We used the H/A/Alpha polarization decomposition approach to extract accurate threshold values of decomposed scattering components. The results showed that the H and Alpha values of bare soil areas were lower and those of vegetated areas were higher. The threshold values of the three scattering components were 0.2–0.4 H and 7–24° Alpha for the surface scattering component, 0.6–0.9 H and 22–50° Alpha for the volume scattering component, and other values for the dihedral scattering component. The SM data retrieved (using the X-Bragg model) on June 27, 2014, were better than those retrieved at other maize growth stages and were thus associated with the minimum root-mean-square error value (0.028). The satellite-evaluated SM contents were in broad agreement with data measured in situ. Our algorithm thus improves the accuracy of SM data retrieval from synthetic-aperture radar (SAR) images.

DC bus protection is a key technological problem to be solved in MMC-based HVDC grids. This article mainly deals with fast DC bus protection based on transient characteristic analysis of faults at different location. By building the equivalent grid model, transient fault characteristics of internal and external pole-to-pole faults can be obtained, the difference of which mainly shows in voltage derivative and current change. Then the protection criterions can be set using these two parameters. Finally, simulations in PSCAD/EMTDC verify the effectiveness and sensitivity of proposed protection method.

Soil moisture (SM) estimates are important to research, but are not accurately predictable in areas with tall vegetation. Full-polarization Radarsat-2 C-band data were used to retrieve SM contents using typical polarization decomposition (Freeman–Durden, Yamaguchi and VanZly) at different growth stages of maize. Applicability analyses were conducted, including proportion, regression and surface scattering model analyses. Furthermore, the Bragg, the extended Bragg scattering model (X-Bragg) and improved surface scattering models (ISSM) were used to retrieve SM content. The results indicated that the VanZly decomposition method was the best. The proportion of surface scattering in the proportion analysis was highest (>52%), followed by that in the Yamaguchi method (>41%). The R2 (>0.6144) between surface scattering and SM was significantly higher (R2 < 0.4484) between dihedral scattering and SM in the regression analysis. The ISSM was better at different maize growth stages than the Bragg and X-Bragg models with a higher R2 (>0.6599) and lower absolute error (AE) (

Smartphone camera is a very powerful sensor for many intelligent applications. But it is difficult to obtain a stable video quality in an unstable motion environment. In this case, robust and fast video stabilization algorithm is necessary for some intelligent applications on smartphone. A novel rapid and efficient video stabilization algorithm is proposed in this paper. The proposed algorithm not only obtains good visual effect, but can also be implemented in real-time on mobile platforms with limited computational resource. Our algorithm can process about 2.9ms per frame with QVGA video format on the mobile platform. The experimental results show the proposed method has similar video stabilization effect with the Optical Image Stabilization (OIS) hardware. The proposed algorithm can support many camera applications for smartphone.

Soil moisture (SM) retrieval from synthetic aperture radar data in maize fields is a challenging process, as the proportion of surface scattering from underlying soil declines with maize growth. The goal of this study was to develop an SM retrieval algorithm from multi-source fusion data, through the sowing (bare soil), jointing, heading and flowering stages. At the sowing stage, the relationship between backscattering simulations based on an integration equation model and impact factors showed that the influence of surface roughness could be reduced by using the co-polarized difference (CPD). Furthermore, the Dubois model was improved by developing a new CPD model. Comparison of measured and estimated SM contents showed that the improved Dubois model (IDubois) was better than the Dubois model, based on root mean square errors (RMSEIDubois = 0.0371, RMSEDubois = 0.0654). At other growth stages, a variety of vegetation indices were simulated by the PROSAIL model for correlation analysis with the equivalent water thickness of maize leaves. The normalized difference water index was found to be the best vegetation index, and the ideal canopy water (CW) retrieval model could be obtained. The best CW retrieval and IDubois models were subsequently used in the water cloud model (WCM) to retrieve SM content in the maize field. The retrieved SM content agreed well with the measured data (RMSEHH = 0.0278, 0.1226, 0.0719, RMSEVV = 0.0346, 0.1809, 0.0723). Overall, these results indicated that the WCM was effective for SM retrieval at some maize growth stages. It was most suitable for estimating SM content at the maize jointing stage.