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BackgroundThis study was performed to quantify intracranial aneurysm wall thickness (AWT) and enhancement using 7T MRI, and their relationship with aneurysm size and type.Methods27 patients with 29 intracranial aneurysms were included. Three-dimensional T1 weighted pre- and post-contrast fast spin echo with 0.4 mm isotropic resolution was used. AWT was defined as the full width at half maximum on profiles of signal intensity across the aneurysm wall on pre-contrast images. Enhancement ratio (ER) was defined as the signal intensity of the aneurysm wall over that of the brain parenchyma. The relationships between AWT, ER, and aneurysm size and type were investigated.Results7T MRI revealed large variations in AWT (range 0.11-1.24 mm). Large aneurysms (>7 mm) had thicker walls than small aneurysms (≤7 mm) (0.49±0.05 vs 0.41±0.05 mm, p1) than in non-enhanced segments (0.53±0.09 vs 0.38±0.07 mm, p

ObjectivesTo compare the visibility of intracranial aneurysm wall and thickness quantification between 7 and 3 T vessel wall imaging and evaluate the association between aneurysm size and wall thickness.MethodsTwenty-nine patients with 29 unruptured intracranial aneurysms were prospectively recruited for 3D T1-weighted vessel wall MRI at both 3 T and 7 T with 0.53 mm (3 T) and 0.4 mm (7 T) isotropic resolution, respectively. Two neuroradiologists independently evaluated wall visibility (0-5 Likert scale), quantified the apparent wall thickness (AWT) using a semi-automated full-width-half-maximum method, calculated wall sharpness, and measured the wall-to-lumen contrast ratio (CRwall/lumen).ResultsTwenty-four patients with 24 aneurysms were included in this study. 7 T achieved significantly better aneurysm wall visibility than 3 T (3.6 ± 1.1 vs 2.7 ± 0.8, p = 0.003). AWT measured on 3 T and 7 T had a good correlation (averaged r = 0.63 ± 0.19). However, AWT on 3 T was 15% thicker than that on 7 T (0.52 ± 0.07 mm vs 0.45 ± 0.05 mm, p

ObjectivesTo determine the intra-individual flow variation in serially acquired studies, and the influence of this variation on subsequent hemodynamic simulations using the inlet flow as a boundary condition. Author: Kindly check and confirm whether the corresponding authors are correctly identified.Confirmed.Materials and methodsThis prospective study included 51 patients (37 females and 14 males) with unruptured intracranial aneurysms who have received more than three times follow-up of 2D phase-contrast MR. The flow and velocity parameters were extracted to calculate the reproducibility and variation. Patient-specific computational fluid dynamics simulations were performed using the measured flows.ResultsIntraclass correlation coefficients for mean and maximum velocity and flow parameters ranged from 0.77 to 0.90. A 10% CV of mean flow was identified. Variations of 10% in inlet flow resulted in hemodynamic changes including 41.41% of peak systolic wall shear stress; 39.13% of end-diastolic wall shear stress; 2.79% of low shear area at peak systole; 2.12% of low shear area at end diastole: 47.57% of time-averaged wall shear stress; and 0.17% of oscillatory shear index.ConclusionThis study identified 10% of intra-individual mean flow variation on phase-contrast MR. Intra-individual flow variation resulted in a non-negligible variation in wall shear stress, but relatively small variation in low shear area in hemodynamic calculations.

BackgroundThe segmentation of cardiac medical images is a crucial step for calculating clinical indices such as wall thickness, ventricular volume, and ejection fraction.MethodsIn this study, we introduce a method named LsUnet that combines multi-channel, fully convolutional neural network, and annular shape level-set methods for efficiently segmenting cardiac cine magnetic resonance (MR) images. In this method, the multi-channel deep learning algorithm is applied to train the segmentation task to extract the left ventricle (LV) endocardial and epicardial contours. Next, the segmentation contours from the multi-channel deep learning method are incorporated into a level-set formulation, which is dedicated explicitly to detecting annular shapes to assure the segmentation's accuracy and robustness.ResultsThe proposed automatic approach was evaluated on 95 volumes (total 1,076 slices, ~80% as for training datasets, ~20% 2D as for testing datasets). This combined multi-channel deep learning and annular shape level-set segmentation method achieved high accuracy with average Dice values reaching 92.15% and 95.42% for LV endocardium and epicardium delineation, respectively, in comparison to the reference standard (the manual segmentation).ConclusionsA novel method for fully automatic segmentation of the LV endocardium and epicardium from different MRI datasets is presented. The proposed workflow is accurate and robust compared to the reference and other state-of-the-art methods.

ObjectivesThis study aims to evaluate the reproducibility of measures of plaque morphology in serially acquired black-blood MRI of untreated atherosclerotic femoral arteries.Materials and methodsMR studies was obtained from 42 timepoints, on 12 patients with known femoral artery atherosclerosis. Images with a 3D isotropic FLASH with DANTE-prepared black blood contrast (DASH) at a 3-T scanner were acquired at baseline, within 1 week, and at 1 month. Six of the patients were scanned additionally at 6 months. Inter-scan and inter-observer variations of arterial area/volume measurements were evaluated.ResultsMeasurement of vessel area, lumen area, wall area and wall volume showed inter-scan intraclass correlation coefficients (ICC) ranging from 0.92 to 0.97 for 3 scans, 0.91-0.97 for 4 scans, and inter-observer ICCs of 0.89-0.96. Among 3 scans, the coefficients of variance (CV) for the vessel area, lumen area, wall area and wall volume were 4.1%, 6.5%, 7.5%, and 4.4%. CVs among 4 scans ranged from 4.4% to 7.9%, and interobserver CVs ranged from 6.1% to 11.8% for the different area/volume measurements.ConclusionDASH MRI is useful for quantifying atherosclerotic vessel area and volume of femoral arteries with low variability among serial repeated scans and between observers.

BACKGROUND:Intraluminal thrombus (ILT) signal intensity on MRI has been studied as a potential marker of abdominal aortic aneurysm (AAA) progression. PURPOSE:1) To characterize the relationship between ILT signal intensity and AAA diameter; 2) to evaluate ILT change over time; and 3) to assess the relationship between ILT features and AAA growth. STUDY TYPE:Prospective. SUBJECTS:Eighty AAA patients were imaged, and a subset (n = 41) were followed with repeated MRI for 16 ± 9 months. FIELD STRENGTH/SEQUENCE:3D black-blood fast-spin-echo sequence at 3 T. ASSESSMENT:ILT was designated as "bright" if the signal was greater than 1.2 times that of adjacent psoas muscle. AAAs were divided into three groups based on ILT: Type 1: bright ILT; Type 2: isointense ILT; Type 3: no ILT. During follow-up, an active ILT change was defined as new ILT formation or an increase in ILT signal intensity to bright; stable ILT was defined as no change in ILT type or ILT became isointense from bright previously. STATISTICAL TESTS:Shapiro-Wilk test; Mann-Whitney U-test; Fisher's exact test; Kruskal-Wallis test; Spearman's r; intraclass correlation coefficient (ICC), Cohen's kappa. RESULTS:AAAs with Type 1 ILT were larger than those with Types 2 and 3 ILT (5.1 ± 1.1 cm, 4.4 ± 0.9 cm, 4.2 ± 0.8 cm, P = 0.008). The growth rate of AAAs with Type 1 ILT was significantly greater than that of AAAs with Types 2 and 3 ILT (2.6 ± 2.5, 0.6 ± 1.3, 1.5 ± 0.6 mm/year, P = 0.01). During follow-up, AAAs with active ILT changes had a 3-fold increased growth rate compared with AAAs with stable ILT (3.6 ± 3.0 mm/year vs. 1.2 ± 1.5 mm/year, P = 0.008). DATA CONCLUSION:AAAs with bright ILT are larger in diameter and grow faster. Active ILT change is associated with faster AAA growth. Black-blood MRI can characterize ILT features and monitor their change over time, which may provide new insights into AAA risk assessment. LEVEL OF EVIDENCE:2 Technical Efficacy Stage: 5 J. Magn. Reson. Imaging 2019;50:994-1001.

Traditional coal mine gas risk assessment relies on manual operations, leading to inefficiencies, incomplete information integration, and insufficient evaluation accuracy, ultimately affecting safety oversight. This paper proposes an intelligent gas risk assessment and report generation framework (IGRARG) based on fine-tuning a Generative Language Model (GLM) to address these challenges. The framework integrates multi-source sensor data with the reasoning capabilities of large language models (LLMs). It constructs a gas risk dataset for coal mine safety scenarios, fine-tuned with GLM. Incorporating industry regulations and a domain-specific knowledge base enhanced with a Retrieval-Augmented Generation (RAG) mechanism, the framework automates alarm judgment, suggestion generation, and report creation via a hierarchical graph structure. Real-time human feedback further refines decision making. Experimental results show an evaluation accuracy of 85–93%, with over 300 field tests achieving a 94.46% alarm judgment accuracy and reducing weekly report generation from 90 min to 2–3 min. This framework significantly enhances the intelligence and efficiency of gas risk assessment, providing robust decision support for coal mine safety management. [ABSTRACT FROM AUTHOR]

To address the issues of low efficiency in manual processing and lack of accuracy in judgment within traditional mine gas safety inspections, this paper designs and implements the Intelligent Mine Gas State Decision-Making System based on large language models (LLMs) and a multi-agent system. The system aims to enhance the accuracy of gas over-limit alarms and improve the efficiency of generating judgment reports. The system integrates the reasoning capabilities of LLMs and optimizes task allocation and execution efficiency of agents through the study of the hybrid multi-agent orchestration algorithm. Furthermore, the system establishes a comprehensive gas risk assessment knowledge base, encompassing historical alarm data, real-time monitoring data, alarm judgment criteria, treatment methods, and relevant policies and regulations. Additionally, the system incorporates several technologies, including retrieval-augmented generation based on human feedback mechanisms, tool management, prompt engineering, and asynchronous processing, which further enhance the application performance of the LLM in the gas status judgment system. Experimental results indicate that the system effectively improves the efficiency of gas alarm processing and the quality of judgment reports in coal mines, providing solid technical support for accident prevention and management in mining operations. [ABSTRACT FROM AUTHOR]

To comprehensively assess the effects of different Low Impact Development (LID) measures on urban rainfall and water quality control, this study focuses on the Liberation Monument Street in Yuzhong District, Chongqing City, and establishes a coupled model for water quantity and water quality. The model calibration and validation are carried out by targeting the uncertain parameters in the model, using watershed characteristic factors as the objective function. Different LID scheme with varying deployment areas were proposed to explore the control effects of water quantity and water quality indicators. The comprehensive evaluation of various schemes, including water quantity, water quality, construction and operational costs, was conducted using the life cycle cost, the analytic hierarchy process, and the principal component analysis. The results show that combination of green roof 10.62 hm2, permeable pavement 45.15 hm2, and biological retention pool 1.14 hm2 is the best, with a total construction cost of 181.53 million RMB. Under different return periods, the optimal scheme varies, providing a reference for selecting the best LID combi-nation deployment scheme under different return periods.

We present a novel design of gallium nitride (GaN) Schottky barrier diodes (SBDs) with a vertical micropillar structure. The micron-scale electrode design can effectively enhance the on-state current density and reduce the specific on-resistance (R on,Sp). In addition, it allows for a reduction in the turn-on voltage (V on) by minimizing the spacing between the electrodes. This phenomenon can be explained by the intensified electric field coupling between the electrodes, as confirmed from the technology computer aided design simulations. By controlling the distance between the micron pillars, the V on of the SBD varies from approximately 0.35 V to 0.46 V. These results showcase the potential of our SBD design with micropillar contacts in efficient high-power applications. [ABSTRACT FROM AUTHOR]