Supercritical carbon dioxide applications for energy conversion systems

In the present paper, the possibility of increasing the thermodynamic efficiency of an electric energy production plant, by using an advanced energy conversion system based on supercritical carbon dioxide (S-CO2) as working fluid, has been analyzed. Since the supercritical carbon dioxide cycles are being considered as a favorable candidate for the next generation of nuclear power plant energy conversion systems, a lead cooled fast reactor has been selected as reference in the present analyses. The main aim of the present study is to compare two different S-CO2 thermal cycles applied on the conversion system of a nuclear power plant. The reference Lead cooled Fast Reactor (LFR) used for the present analyses is the ALFRED reactor, which has a thermal power of 300 MW and it is considered the scaled down prototype of the industrial European Lead Fast Reactor (ELFR). Thermodynamic cycles selected for the present study are a Recompression Cycle and a Brayton Cycle with Regeneration. Each of them has been analyzed under several design conditions regarding the maximum pressure and the regeneration coefficient. Among different design conditions, the solution allowing the maximization of the overall efficiency has been identified. Thermodynamic analyses have been carried out with GateCycle™ v. 6.1.1, which is a General Electric software able to predict design and off-design performance of power plants

An anisotropic numerical model for thermal hydraulic analyses: application to liquid metal flow in fuel assemblies

A CFD analysis has been carried out to study the thermal–hydraulic behavior of liquid metal coolant in a fuel assembly of triangular lattice. In order to obtain fast and accurate results, the isotropic two-equation RANS approach is often used in nuclear engineering applications. A different approach is provided by Non-Linear Eddy Viscosity Models (NLEVM), which try to take into account anisotropic effects by a nonlinear formulation of the Reynolds stress tensor. This approach is very promising, as it results in a very good numerical behavior and in a potentially better fluid flow description than classical isotropic models. An Anisotropic Shear Stress Transport (ASST) model, implemented into a commercial software, has been applied in previous studies, showing very trustful results for a large variety of flows and applications. In the paper, the ASST model has been used to perform an analysis of the fluid flow inside the fuel assembly of the ALFRED lead cooled fast reactor. Then, a comparison between the results of wall-resolved conjugated heat transfer computations and the results of a decoupled analysis using a suitable thermal wall-function previously implemented into the solver has been performed and presented

Severe chest allodynia as an unusual first presentation of hydatid disease. A case report

Background: Cystic echinococcosis (CE) is a worldwide zoonosis and the liver is the most commonly affected organ. Clinical manifestations range from completely asymptomatic cysts to a potential lethal cyst rupture and anaphylaxis. Case presentation: Severe chest allodynia was an unusual clinical presentation of hepatic cyst rupture in the retroperitoneal space, without any other specific symptoms. CE diagnosis was confirmed by computed tomography scan and magnetic resonance. The patient underwent hepatectomy with complete resolution of the neuropathic pain. Conclusions: Retroperitoneal hydatid cyst rupture is a rare event and its clinical manifestation may mimic other chest neuropathies

Thermal-hydraulic analysis of an innovative decay heat removal system for lead-cooled fast reactors

Improvement of safety requirements in GEN IV reactors needs more reliable safety systems, among which the decay heat removal system (DHR) is one of the most important. Complying with the diversification criteria and based on pure passive and very reliable components, an additional DHR for the ALFRED reactor (Advanced Lead Fast Reactor European Demonstrator) has been proposed and its thermal-hydraulic performances are analyzed. It consists in a coupling of two innovative subsystems: the radiative-based direct heat exchanger (DHX), and the pool heat exchanger (PHX). Preliminary thermal-hydraulic analyses, by using RELAP5 and RELAP5-3D© computer programs, have been carried out showing that the whole system can safely operate, in natural circulation, for a long term. Sensitivity analyses for: the emissivity of the DHX surfaces, the PHX water heat transfer coefficient (HTC) and the lead HTC have been carried out. In addition, the effects of the density variation uncertainty on the results has been analyzed and compared. It allowed to assess the feasibility of the system and to evaluate the acceptable range of the studied parameters. A comparison of the results obtained with RELAP5 and RELAP5-3D© has been carried out and the analysis of the differences of the two codes for lead is presented. The features of the innovative DHR allow to match the decay heat removal performance with the trend of the reactor decay heat power after shutdown, minimizing at the same time the risk of lead freezing. This system, proposed for the diversification of the DHR in the LFRs, could be applicable in the other pool-type liquid metal fast reactors

Lean body weight-tailored Iodinated contrast Injection in obese patient. boer versus James Formula

Purpose. To prospectively compare the performance of James and Boer formula in contrast media (CM) administration, in terms of image quality and parenchymal enhancement in obese patients undergoing CT of the abdomen. Materials and Methods. Fifty-five patients with a body mass index (BMI) greater than 35 kg/m2were prospectively included in the study. All patients underwent 64-row CT examination and were randomly divided in two groups: 26 patients in Group A and 29 patients in Group B. The amount of injected CM was computed according to the patient's lean body weight (LBW), estimated using either Boer formula (Group A) or James formula (Group B). Patient's characteristics, CM volume, contrast-to-noise ratio (CNR) of liver, aorta and portal vein, and liver contrast enhancement index (CEI) were compared between the two groups. For subjective image analysis readers were asked to rate the enhancement of liver, kidneys, and pancreas based on a 5-point Likert scale. Results. Liver CNR, aortic CNR, and portal vein CNR showed no significant difference between Group A and Group B (all P ≥ 0.177). Group A provided significantly higher CEI compared to Group B (P = 0.007). Group A and Group B returned comparable overall subjective enhancement values (3.54 and vs 3.20, all P ≥ 0.199). Conclusions. Boer formula should be the method of choice for LBW estimation in obese patients, leading to an accurate CM amount calculation and an optimal liver contrast enhancement in CT

Magnetic resonance imaging radiomics in prostate cancer radiology: what is currently known?

Diagnostic and treatment approaches in prostate cancer rely on a combination of magnetic resonance imaging and histological data. This study aimed to introduce the basics of the current diagnostic approach in prostate cancer with a focus on texture analysis. Texture analysis evaluates the relationships between image pixels using mathematical methods, which provide additional information. First-order texture analysis of features can have greater clinical reproducibility than higher-order texture features. Textural features that are extracted from diffusion coefficient maps have shown the greatest clinical relevance. Future research should focus on integrating machine learning methods to facilitate the use of texture analysis in clinical practice. The development of automated segmentation methods is required to reduce the likelihood of including normal tissue in the area of interest. Texture analysis allows the noninvasive separation of patients into groups in terms of possible treatment options. Currently, few clinical studies reported on the differential diagnosis of clinically significant prostate cancer, including the Gleason and International Society of Urological Pathology grading. Large prospective studies are required to verify the diagnostic potential of textural features

Chest CT Features of COVID-19 in Rome, Italy

Background The standard for diagnosis of SARS-CoV-2 virus is reverse transcription polymerase chain reaction (RT-PCR) test, but chest CT may play a complimentary role in the early detection of COVID-19 pneumonia. Purpose To investigate CT features of patients with COVID-19 in Rome, Italy, and to compare the accuracy of CT with RT-PCR. Methods In this prospective study from March 4, 2020, until March 19, 2020, consecutive patients with suspected COVID-19 infection and respiratory symptoms were enrolled. Exclusion criteria were: chest CT with contrast medium performed for vascular indications, patients who refused chest CT or hospitalization, and severe CT motion artifact. All patients underwent RT-PCR and chest CT. Diagnostic performance of CT was calculated using RT-PCR as reference. Chest CT features were calculated in a subgroup of RT-PCR-positive and CT-positive patients. CT features of hospitalized patients and patient in home isolation were compared by using Pearson chi squared test. Results Our study population comprised 158 consecutive study participants (83 male and 75 female, mean age 57 y ±17). Fever was observed in 97/158 (61%), cough in 88/158 (56%), dyspnea in 52/158 (33%), lymphocytopenia in 95/158 (60%), increased C-reactive protein level in 139/158 (88%), and elevated lactate dehydrogenase in 128/158 (81%) study participants. Sensitivity, specificity, and accuracy of CT were 97% (60/62)[95% IC, 88-99%], 56% (54/96)[95% IC,45-66%] and 72% (114/158)[95% IC 64-78%], respectively. In the subgroup of RT-PCR-positive and CT-positive patients, ground-glass opacities (GGO) were present in 58/58 (100%), multilobe and posterior involvement were both present in 54/58 (93%), bilateral pneumonia in 53/58 (91%), and subsegmental vessel enlargement (> 3 mm) in 52/58 (89%) of study participants. Conclusion The typical pattern of COVID-19 pneumonia in Rome, Italy, was peripherally ground-glass opacities with multilobe and posterior involvement, bilateral distribution, and subsegmental vessel enlargement (> 3 mm). Chest CT sensitivity was high (97%) but with lower specificity (56%)

Automated segmentation of colorectal tumor in 3D MRI Using 3D multiscale densely connected convolutional neural network

The main goal of this work is to automatically segment colorectal tumors in 3D T2-weighted (T2w) MRI with reasonable accuracy. For such a purpose, a novel deep learning-based algorithm suited for volumetric colorectal tumor segmentation is proposed. The proposed CNN architecture, based on densely connected neural network, contains multiscale dense interconnectivity between layers of fine and coarse scales, thus leveraging multiscale contextual information in the network to get better flow of information throughout the network. Additionally, the 3D level-set algorithm was incorporated as a postprocessing task to refine contours of the network predicted segmentation. The method was assessed on T2-weighted 3D MRI of 43 patients diagnosed with locally advanced colorectal tumor (cT3/T4). Cross validation was performed in 100 rounds by partitioning the dataset into 30 volumes for training and 13 for testing. Three performance metrics were computed to assess the similarity between predicted segmentation and the ground truth (i.e., manual segmentation by an expert radiologist/oncologist), including Dice similarity coefficient (DSC), recall rate (RR), and average surface distance (ASD). The above performance metrics were computed in terms of mean and standard deviation (mean ± standard deviation). The DSC, RR, and ASD were 0.8406 ± 0.0191, 0.8513 ± 0.0201, and 2.6407 ± 2.7975 before postprocessing, and these performance metrics became 0.8585 ± 0.0184, 0.8719 ± 0.0195, and 2.5401 ± 2.402 after postprocessing, respectively. We compared our proposed method to other existing volumetric medical image segmentation baseline methods (particularly 3D U-net and DenseVoxNet) in our segmentation tasks. The experimental results reveal that the proposed method has achieved better performance in colorectal tumor segmentation in volumetric MRI than the other baseline techniques

Development and validation of artificial-intelligence-based radiomics model using computed tomography features for preoperative risk stratification of gastrointestinal stromal tumors

Background: preoperative risk assessment of gastrointestinal stromal tumors (GISTS) is required for optimal and personalized treatment planning. Radiomics features are promising tools to predict risk assessment. The purpose of this study is to develop and validate an artificial intelligence classification algorithm, based on CT features, to define GIST's prognosis as determined by the Miettinen classification. Methods: patients with histological diagnosis of GIST and CT studies were retrospectively enrolled. Eight morphologic and 30 texture CT features were extracted from each tumor and combined to obtain three models (morphologic, texture and combined). Data were analyzed using a machine learning classification (WEKA). For each classification process, sensitivity, specificity, accuracy and area under the curve were evaluated. Inter- and intra-reader agreement were also calculated. Results: 52 patients were evaluated. In the validation population, highest performances were obtained by the combined model (SE 85.7%, SP 90.9%, ACC 88.8%, and AUC 0.954) followed by the morphologic (SE 66.6%, SP 81.8%, ACC 76.4%, and AUC 0.742) and texture (SE 50%, SP 72.7%, ACC 64.7%, and AUC 0.613) models. Reproducibility was high of all manual evaluations. Conclusions: the AI-based radiomics model using a CT feature demonstrates good predictive performance for preoperative risk stratification of GISTs

Updates on Quantitative MRI of Diffuse Liver Disease. A Narrative Review

Diffuse liver diseases are highly prevalent conditions around the world, including pathological liver changes that occur when hepatocytes are damaged and liver function declines, often leading to a chronic condition. In the last years, Magnetic Resonance Imaging (MRI) is reaching an important role in the study of diffuse liver diseases moving from qualitative to quantitative assessment of liver parenchyma. In fact, this can allow noninvasive accurate and standardized assessment of diffuse liver diseases and can represent a concrete alternative to biopsy which represents the current reference standard. MRI approach already tested for other pathologies include diffusion-weighted imaging (DWI) and radiomics, able to quantify different aspects of diffuse liver disease. New emerging MRI quantitative methods include MR elastography (MRE) for the quantification of the hepatic stiffness in cirrhotic patients, dedicated gradient multiecho sequences for the assessment of hepatic fat storage, and iron overload. Thus, the aim of this review is to give an overview of the technical principles and clinical application of new quantitative MRI techniques for the evaluation of diffuse liver disease