Neurodegenerative disorders,including Alzheimer’s disease(AD),Parkinson’s disease(PD),and amyotrophic lateral sclerosis,impose a considerable social and economic burden on society and have dramatic consequences for ...Neurodegenerative disorders,including Alzheimer’s disease(AD),Parkinson’s disease(PD),and amyotrophic lateral sclerosis,impose a considerable social and economic burden on society and have dramatic consequences for individuals and their families.The majority of existing interventions have been found to be capable of only a slight modification of disease progression or to moderately delay significant functional decline in motor,cognitive,or mental domains.展开更多
Medical imaging is essential in modern health care,allowing accurate diagnosis and effective treatment planning.These images,however,often demonstrate low contrast,noise,and brightness distortion that reduce their dia...Medical imaging is essential in modern health care,allowing accurate diagnosis and effective treatment planning.These images,however,often demonstrate low contrast,noise,and brightness distortion that reduce their diagnostic reliability.This review presents a structured and comprehensive analysis of advanced histogram equalization(HE)-based techniques for medical image enhancement.Our review methodology encompasses:(1)classical HE approaches and related limitations in medical domains;(2)adaptive schemes like Adaptive Histogram Equalization(AHE)and Contrast Limited Adaptive Histogrma Equalization(CLAHE)and their advance variants;(3)brightnesspreserving schemes like BBHE and MMBEBHE and related algorithms;(4)dynamic and recursive histogram equalization methods incorporating DHE and RMSHE;(5)fuzzy logic-based enhancement methodologies addressing uncertainty and noise in medical images;and(6)hybrid optimization methodologies through the application of metaheuristic algorithms(World Cup Optimization,Particle Swarm Optimization,Genetic Algorithms,along with histogram-based methodologies.)There is also a comparative discussion given based on contrast improvement,image brightness preservation,noise management,and computational efficiency.Such advancements have better capabilities of improving image quality,which is more important for improved diagnosis and image analysis.展开更多
Microwave ablation(MWA)is a minimally invasive technique for treating hepatic tumors,necessitating precise monitoring to ensure treatment efficacy and minimize damage to surrounding tissues.This study explores the pot...Microwave ablation(MWA)is a minimally invasive technique for treating hepatic tumors,necessitating precise monitoring to ensure treatment efficacy and minimize damage to surrounding tissues.This study explores the potential of photoacoustic imaging(PAI)in monitoring MWA by examining ex vivo porcine liver tissues.In this study,a comprehensive analysis of photoacoustic signals was performed to compare the main lobe width(MLW)between ablated and normal regions in porcine liver tissue.Histological staining with succinate dehydrogenase(SDH)and shear wave elastography(SWE)were employed to validate the changes in tissue elasticity after ablation.The analysis demonstrated a notable reduction in the MLW of the average A-lines in ablated tissues compared to nonablated regions(p<0.01).This reduction,attributed to increased tissue density and enhanced elasticity,indicates accelerated sound propagation in thermally ablated areas,which then serves as a critical parameter for mapping tissue characteristics.The reconstruction of the MLW distribution successfully delineated the ablated regions,and was consistent with the results of SDH staining and SWE.In addition,MLW-based imaging exhibited higher spatial resolution compared to SWE.Incorporating MLW analysis into PAI may be a promising strategy to improve the accuracy and effectiveness of MWA monitoring in clinical settings.展开更多
This paper introduces a novel fractional-order model based on the Caputo-Fabrizio(CF)derivative for analyzing computer virus propagation in networked environments.The model partitions the computer population into four...This paper introduces a novel fractional-order model based on the Caputo-Fabrizio(CF)derivative for analyzing computer virus propagation in networked environments.The model partitions the computer population into four compartments:susceptible,latently infected,breaking-out,and antivirus-capable systems.By employing the CF derivative—which uses a nonsingular exponential kernel—the framework effectively captures memory-dependent and nonlocal characteristics intrinsic to cyber systems,aspects inadequately represented by traditional integer-order models.Under Lipschitz continuity and boundedness assumptions,the existence and uniqueness of solutions are rigorously established via fixed-point theory.We develop a tailored two-step Adams-Bashforth numerical scheme for the CF framework and prove its second-order accuracy.Extensive numerical simulations across various fractional orders reveal that memory effects significantly influence virus transmission and control dynamics;smaller fractional orders produce more pronounced memory effects,delaying both infection spread and antivirus activation.Further theoretical analysis,including Hyers-Ulam stability and sensitivity assessments,reinforces the model’s robustness and identifies key parameters governing virus dynamics.The study also extends the framework to incorporate stochastic effects through a stochastic CF formulation.These results underscore fractional-order modeling as a powerful analytical tool for developing robust and effective cybersecurity strategies.展开更多
Early correction of childhood malocclusion is timely managing morphological,structural,and functional abnormalities at different dentomaxillofacial developmental stages.The selection of appropriate imaging examination...Early correction of childhood malocclusion is timely managing morphological,structural,and functional abnormalities at different dentomaxillofacial developmental stages.The selection of appropriate imaging examination and comprehensive radiological diagnosis and analysis play an important role in early correction of childhood malocclusion.This expert consensus is a collaborative effort by multidisciplinary experts in dentistry across the nation based on the current clinical evidence,aiming to provide general guidance on appropriate imaging examination selection,comprehensive and accurate imaging assessment for early orthodontic treatment patients.展开更多
AIM:To evaluate the efficacy of computer-assisted color analysis of colorectal lesions using a novel auto-fluorescence imaging(AFI)system to distinguish neoplastic lesions from non-neoplastic lesions and to predict th...AIM:To evaluate the efficacy of computer-assisted color analysis of colorectal lesions using a novel auto-fluorescence imaging(AFI)system to distinguish neoplastic lesions from non-neoplastic lesions and to predict the depth of invasion.METHODS:From January 2013 to April 2013,consecutive patients with known polyps greater than 5 mm in size who were scheduled to undergo endoscopic treatment at The Jikei University Hospital were prospectively recruited for this study.All lesions were evaluated using a novel AFI system,and color-tone sampling was performed in a region of interest determined from narrow band imaging or from chromoendoscopy findings without magnification.The green/red(G/R)ratio for each lesion on the AFI images was calculated automatically using a computer-assisted color analysis system that permits real-time color analysis during endoscopic procedures.RESULTS:A total of 88 patients with 163 lesions were enrolled in this study.There were significant differences in the G/R ratios of hyperplastic polyps(non-neoplastic lesions),adenoma/intramucosal cancer/submucosal(SM)superficial cancer,and SM deep cancer(P<0.0001).The mean±SD G/R ratios were 0.984±0.118in hyperplastic polyps and 0.827±0.081 in neoplastic lesions.The G/R ratios of hyperplastic polyps were significantly higher than those of neoplastic lesions(P<0.001).When a G/R ratio cut-off value of>0.89 was applied to determine non-neoplastic lesions,the sensitivity,specificity,positive predictive value(PPV),negative predictive value(NPV),and accuracy were 83.9%,82.6%,53.1%,95.6%and 82.8%,respectively.For neoplastic lesions,the mean G/R ratio was 0.834±0.080 in adenoma/intramucosal cancer/SM superficial cancer and 0.746±0.045 in SM deep cancer.The G/R ratio of adenoma/intramucosal cancer/SM superficial cancer was significantly higher than that of SM deep cancer(P<0.01).When a G/R ratio cut-off value of<0.77 was applied to distinguish SM deep cancers,the sensitivity,specificity,PPV,NPV,and accuracy were80.0%,84.4%,29.6%,98.1%and 84.1%,respectively.CONCLUSION:The novel AFI system with color analysis was effective in distinguishing non-neoplastic lesions from neoplastic lesions and might allow determination of the depth of invasion.展开更多
BACKGROUND Pancreatic cancer remains one of the most lethal malignancies worldwide,with a poor prognosis often attributed to late diagnosis.Understanding the correlation between pathological type and imaging features ...BACKGROUND Pancreatic cancer remains one of the most lethal malignancies worldwide,with a poor prognosis often attributed to late diagnosis.Understanding the correlation between pathological type and imaging features is crucial for early detection and appropriate treatment planning.AIM To retrospectively analyze the relationship between different pathological types of pancreatic cancer and their corresponding imaging features.METHODS We retrospectively analyzed the data of 500 patients diagnosed with pancreatic cancer between January 2010 and December 2020 at our institution.Pathological types were determined by histopathological examination of the surgical spe-cimens or biopsy samples.The imaging features were assessed using computed tomography,magnetic resonance imaging,and endoscopic ultrasound.Statistical analyses were performed to identify significant associations between pathological types and specific imaging characteristics.RESULTS There were 320(64%)cases of pancreatic ductal adenocarcinoma,75(15%)of intraductal papillary mucinous neoplasms,50(10%)of neuroendocrine tumors,and 55(11%)of other rare types.Distinct imaging features were identified in each pathological type.Pancreatic ductal adenocarcinoma typically presents as a hypodense mass with poorly defined borders on computed tomography,whereas intraductal papillary mucinous neoplasms present as characteristic cystic lesions with mural nodules.Neuroendocrine tumors often appear as hypervascular lesions in contrast-enhanced imaging.Statistical analysis revealed significant correlations between specific imaging features and pathological types(P<0.001).CONCLUSION This study demonstrated a strong association between the pathological types of pancreatic cancer and imaging features.These findings can enhance the accuracy of noninvasive diagnosis and guide personalized treatment approaches.展开更多
Background:Congenital hepatic hemangioma(CHH)is a rare benign vascular tumor that occurs prenatally.However,only a few cases have been summarized and evaluated for the prenatal and postnatal imaging features of CHH,an...Background:Congenital hepatic hemangioma(CHH)is a rare benign vascular tumor that occurs prenatally.However,only a few cases have been summarized and evaluated for the prenatal and postnatal imaging features of CHH,and no studies have conducted long-term follow-up on it.This study aimed to explore the ultrasound and magnetic resonance features,growth patterns,and clinical outcomes of CHH.Methods:Thirty-six pregnancies with a prenatal fetal diagnosis and postnatal diagnosis of CHH were studied.CHHs were grouped into those with a diameter≥4 cm and those with a diameter<4 cm according to the largest diameter.Fisher's exact test was used to compare the imaging characteristics between the groups.The volume of CHHs was measured at each follow-up visit to plot the growth pattern of the tumors,and the volume of CHHs was compared before and after birth using a rank sum test analysis.Results:Thirty-three cases of CHHs were confirmed by postnatal imaging,and three were confirmed by a biopsy.Mixed echoes were more common in the diameter≥4 cm group than in the diameter<4 cm group(p=0.026).Complications were more likely to occur in the large-diameter group.Eighteen(54.5%)cases were classified as rapidly involuting congenital hemangioma,nine(27.3%)as partially involuting congenital hemangioma,and two(6.1%)as noninvoluting congenital hemangioma.A new type of CHH was identified in which four(12.1%)cases continued to proliferate after birth and spontaneously subsided in subsequent months.The CHH volume decreased with age and was significantly decreased at 9 months postnatal compared to birth(p=0.001).Conclusion:This study showed the imaging features of CHH were associated with the lesion size.Based on postnatal follow-up,a new type of CHH was identified.If there are no complications at birth in CHH cases,a good prognosis is indicated.展开更多
Video snapshot compressive imaging(Video SCI) modulates scenes using various encoding masks and captures compressed measurements with a low-speed camera during a single exposure. Subsequently, reconstruction algorithm...Video snapshot compressive imaging(Video SCI) modulates scenes using various encoding masks and captures compressed measurements with a low-speed camera during a single exposure. Subsequently, reconstruction algorithms restore image sequences of dynamic scenes, offering advantages such as reduced bandwidth and storage space requirements. The temporal correlation in video data is crucial for Video SCI, as it leverages the temporal relationships among frames to enhance the efficiency and quality of reconstruction algorithms, particularly for fast-moving objects.This paper discretizes video frames to create image datasets with the same data volume but differing temporal correlations. We utilized the state-of-the-art(SOTA) reconstruction framework, EfficientSCI++, to train various compressed reconstruction models with these differing temporal correlations. Evaluating the reconstruction results from these models, our simulation experiments confirm that a reduction in temporal correlation leads to decreased reconstruction accuracy. Additionally, we simulated the reconstruction outcomes of datasets devoid of temporal correlation, illustrating that models trained on non-temporal data affect the temporal feature extraction capabilities of transformers, resulting in negligible impacts on the evaluation of reconstruction results for non-temporal correlation test datasets.展开更多
Confocal laser endomicroscopy(CLE)has become an indispensable tool in the diagnosis and detection of gastrointestinal(GI)diseases due to its high-resolution and high-contrast imaging capabilities.However,the early-sta...Confocal laser endomicroscopy(CLE)has become an indispensable tool in the diagnosis and detection of gastrointestinal(GI)diseases due to its high-resolution and high-contrast imaging capabilities.However,the early-stage imaging changes of gastrointestinal disorders are often subtle,and traditional medical image analysis methods rely heavily on manual interpretation,which is time-consuming,subject to observer variability,and inefficient for accurate lesion identification across large-scale image datasets.With the introduction of artificial intelligence(AI)technologies,AI-driven CLE image analysis systems can automatically extract pathological features and have demonstrated significant clinical value in lesion recognition,classification diagnosis,and malignancy prediction of GI diseases.These systems greatly enhance diagnostic efficiency and early detection capabilities.This review summarizes the applications of AI-assisted CLE in GI diseases,analyzes the limitations of current technologies,and explores future research directions.It is expected that the deep integration of AI and confocal imaging technologies will provide strong support for precision diagnosis and personalized treatment in the field of gastrointestinal disorders.展开更多
Leesmidt et al present a comprehensive analysis of abdominal vascular flow in children using four-dimensional(4D)flow magnetic resonance imaging(MRI),aim to establish normal hemodynamic values for the abdominal viscer...Leesmidt et al present a comprehensive analysis of abdominal vascular flow in children using four-dimensional(4D)flow magnetic resonance imaging(MRI),aim to establish normal hemodynamic values for the abdominal visceral organs and to assess the feasibility of 4D flow MRI(4D-f-MRI)in this population.The researchers performed 4D-f-MRI on 9 pediatric patients with a history or suspi-cion of bowel pathology.Flow velocities were measured in the abdominal aorta and superior and inferior mesenteric arteries.The quality of the 4D-f-MRI images was evaluated,and the agreement between the measured flow velocities and those obtained from Duplex ultrasound was established.However,due to the specific limitations of this work,future studies should address the issues of small sample size and the specific age group design.展开更多
Objective To evaluate the cost-effectiveness of gadopentetate dimeglumine(Gd-DTPA)and gadobenate dimeglumine(Gd-BOPTA)magnetic resonance imaging(MRI)contrast agents for the early diagnosis of hepatocellular carcinoma(...Objective To evaluate the cost-effectiveness of gadopentetate dimeglumine(Gd-DTPA)and gadobenate dimeglumine(Gd-BOPTA)magnetic resonance imaging(MRI)contrast agents for the early diagnosis of hepatocellular carcinoma(HCC)from the perspective of China’s healthcare system.Methods A decision tree+partitioned survival model was constructed for early diagnosis of HCC based on literature data.Taking quality-adjusted life year(QALY)as the main health outcome measure for incremental cost-effectiveness ratio(ICER)analysis,the sensitivity analysis by Monte Carlo simulation was constructed to generate corresponding tornado diagram,incremental cost-effectiveness scatter plot,and cost-effectiveness acceptability curve.Results and Conclusion The basic analysis results showed that the ICER value of Gd-BOPTA diagnostic scheme compared with Gd-DTPA diagnostic scheme was 17302.46 yuan/QALY,which is less than 1 times of China’s gross domestic product(GDP)per capita.The sensitivity analysis results showed that the cost of delayed treatment and timely treatment had a significant impact on the results.When the willingness to pay(WTP)was 1 time of GDP per capita,the probability of cost-effectiveness advantage of Gd-BOPTA diagnostic scheme was 65.30%.When the WTP value is set at 1 times of GDP per capita,Gd-BOPTA MRI has cost-effectiveness advantages for the early diagnosis of HCC.展开更多
To identify coatings and analyze the anti-detection capabilities of camouflage patterns, material samples can be prepared using the super-pixel segmentation method. A spectral polarization imaging system is developed,...To identify coatings and analyze the anti-detection capabilities of camouflage patterns, material samples can be prepared using the super-pixel segmentation method. A spectral polarization imaging system is developed, based on the principle of bidirectional reflectance distribution function(BRDF), to obtain spectral reflection intensities of coatings at full spatial angles, and use polarization images to calculate the refractive index by the Fresnel equation. The index is then coupled into TorranceSparrow model to simulate the spectral scattering intensity to mutually verify the experimental results. The spectral scattering characteristics of standard camouflage patterns are then revealed and pinpoint the signature band and the angle of reflecting sensitivity.展开更多
This study aimed to analyze the early high-resolution CT(HRCT)manifestations and dynamic imaging changes of coronavirus disease 2019(COVID-19)in Qinghai Province.A total of 24 nucleic acid-positive COVID-19 patients a...This study aimed to analyze the early high-resolution CT(HRCT)manifestations and dynamic imaging changes of coronavirus disease 2019(COVID-19)in Qinghai Province.A total of 24 nucleic acid-positive COVID-19 patients admitted to our hospital between January 2020 and November 2021 were included.All patients underwent HRCT examinations,and lesion characteristics—including number,distribution,morphology,and surrounding involvement were analyzed.Among the 24 patients,systemic and respiratory circulatory symptoms were more common than other symptoms(P<0.05).There were no significant differences in the lung lobes,relative positions,quantity,size,and density of lesions across different stages of the disease course(P>0.05).Within the same disease stage,lesions were primarily located in the lower lobes of both lungs,the peripheral lung fields,and a combination of peripheral and central regions,with single and multiple lesions being the most common.Lesion morphology varied significantly across disease stages(P<0.05),including differences between patchy and striped lesions,striped and massive lesions(P<0.05),and patchy and massive lesions(P<0.05).The incidence of striped lesions was higher in the progressive and recovery stages than in the early stage,showing an upward trend.There were no significant differences in pleural thickening,pleural effusion,mediastinal lymph node enlargement,or pericardial effusion across different disease stages(P>0.05).Common HRCT signs observed at all stages included air bronchograms,paving stone patterns,halo signs,subpleural lines,and grid-like patterns.The main patterns of lesion progression were an increase in lesion size(16/24,66.67%),an increase in the number of lesions(17/24,70.83%),changes in lesion density(20/24,80.33%),and localized lesion increase and partial absorption(6/24,25.00%).In conclusion,the HRCT manifestations and evolution of lung lesions in COVID-19 patients are complex and varied,with a progressive increase in striped lesions potentially serving as a characteristic imaging feature of the disease.展开更多
Objective:To explore the research landscape and hotspots of Computerized Respiratory Sound Analysis(CORSA)and provide a reference for future in-depth studies.Methods:Literature related to CORSA published up to August ...Objective:To explore the research landscape and hotspots of Computerized Respiratory Sound Analysis(CORSA)and provide a reference for future in-depth studies.Methods:Literature related to CORSA published up to August 27,2020,was retrieved from the Web of Science Core Collection.CiteSpace 5.6.R3 was used to perform co-authorship analysis,institutional collaboration analysis,keyword co-occurrence analysis,and co-citation analysis.Results:A total of 1,897 publications were included.Co-authorship analysis identified several influential contributors,including Zahra Moussavi,Kenneth Sundaraj,and H.Pasterkamp.Major research institutions included the University of Manitoba,the University of Queensland,and Aristotle University of Thessaloniki.Keyword co-occurrence analysis indicated that“respiratory sound,”“lung sound,”“asthma,”“children,”and“classification”were major research themes.The most frequently co-cited articles were published by Arati Gurung(2011),Mohammed Bahoura(2009),and H.Pasterkamp(1997).Highly cited journals included Chest,the American Journal of Respiratory and Critical Care Medicine,and IEEE Transactions on Biomedical Engineering.Conclusion:CORSA research is primarily driven by European and North American scholars and institutions,with China still in an early stage of development.Current hotspots include respiratory sound acquisition and processing,feature extraction methods such as Mel-frequency cepstral coefficients(MFCCs),and classification techniques based on machine learning and deep learning.CORSA is suitable for diverse populations and is widely applied in respiratory diseases,especially bronchial asthma.Its non-invasive nature offers particular advantages for infants and pregnant women.Although CORSA demonstrates strong clinical potential,its clinical translation remains limited.Advancing clinical applications and bridging the gap between research and practice will be key directions for future development.The prominence of top-tier respiratory and engineering journals among citations suggests that CORSA is an emerging and influential research frontier.展开更多
The detection and characterization of non-metallic inclusions are essential for clean steel production.Recently,imaging analysis combined with high-dimensional data processing of metallic materials using artificial in...The detection and characterization of non-metallic inclusions are essential for clean steel production.Recently,imaging analysis combined with high-dimensional data processing of metallic materials using artificial intelligence(AI)-based machine learning(ML)has developed rapidly.This technique has achieved impressive results in the field of inclusion classification in process metallurgy.The present study surveys the ML modeling of inclusion prediction in advanced steels,including the detection,classification,and feature prediction of inclusions in different steel grades.Studies on clean steel with different features based on data and image analysis via ML are summarized.Regarding the data analysis,the inclusion prediction methodology based on ML establishes a connection between the experimental parameters and inclusion characteristics and analyzes the importance of the experimental parameters.Regarding the image analysis,the focus is placed on the classification of different types of inclusions via deep learning,in comparison with data analysis.Finally,further development of inclusion analyses using ML-based methods is recommended.This work paves the way for the application of AIbased methodologies for ultraclean-steel studies from a sustainable metallurgy perspective.展开更多
Integration of artificial intelligence increases in all aspects of human life,particularly in healthcare systems.Traumatic brain injury is a significant cause of mortality and long-term disability,with an important im...Integration of artificial intelligence increases in all aspects of human life,particularly in healthcare systems.Traumatic brain injury is a significant cause of mortality and long-term disability,with an important impact on the socioeconomic system of healthcare.The role of artificial intelligence in imaging and outcome prediction for traumatic brain injury patients is reviewed with a particular emphasis to the characteristics of machine and deep learning methods.Evidence of potential improvement in the clinical practice in discussed.展开更多
This study aims to analyze and discuss the current situation of quality control and quality management in medical imaging.Firstly,it defines the concepts of quality management and image quality,emphasizing the crucial...This study aims to analyze and discuss the current situation of quality control and quality management in medical imaging.Firstly,it defines the concepts of quality management and image quality,emphasizing the crucial role of the department's Quality and Safety Team in imaging quality management[1].Subsequently,it details the composition and responsibilities of the Quality and Safety Management Group,as well as the specific divisions of labor and tasks of the Quality Management Team.The effective implementation of comprehensive quality control in the imaging department is ensured through the"Seven-Star Sword"method,which encompasses total process control,organization-wide participation,and total quality control[2].Furthermore,the application of the PDCA(Plan-Do-Check-Act)cycle in quality management is explored,along with strategies to improve patient satisfaction by reducing the waiting time for imaging reports[3].Finally,the management of a key departmental medical quality control indicator—critical values—is analyzed,and improvement measures are proposed[4].This study underscores the importance of quality management in enhancing medical service quality and ensuring patient safety,and puts forward specific implementation strategies and improvement methods.展开更多
Underground hydrogen storage has gained interest in recent years due to the enormous demand for clean energy.Hydrogen is more diffusive than air,with a smaller density and lower viscosity.These unique properties intro...Underground hydrogen storage has gained interest in recent years due to the enormous demand for clean energy.Hydrogen is more diffusive than air,with a smaller density and lower viscosity.These unique properties introduce distinctive hydrodynamic phenomena in hydrogen storage,one of which is fingering.Fingering could induce the fluid trapped in small clusters of pores,leading to a dramatic decrease in hydrogen saturation and a lower recovery rate.In this study,numerical simulations are performed at the microscopic scale to understand the evolution of hydrogen saturation and the impacts of injection and withdrawal cycles.Two sets of micromodels with different porosity(0.362 and 0.426)and minimum sizes of pore throats(0.362 mm and 0.181 mm)are developed in the numerical model.A parameter analysis is then conducted to understand the influence of injection velocity(in the range of 10^(-2)m/s to 10^(-5)m/s)and porous structure on the fingering pattern,followed by an image analysis to capture the evolution of the fingering pattern.Viscous fingering,capillary fingering,and crossover fingering are observed and identified under different boundary conditions.The fractal dimension,specific area,mean angle,and entropy of fingers are proposed as geometric descriptors to characterize the shape of the fingering pattern.When porosity increases from 0.362 to 0.426,the saturation of hydrogen increases by 26.2%.Narrower pore throats elevate capillary resistance,which hinders fluid invasion.These results underscore the importance of pore structures and the interaction between viscous and capillary forces for hydrogen recovery efficiency.This work illuminates the influence of the pore structures and the fluid properties on the immiscible displacement of hydrogen and can be further extended to optimize the injection strategy of hydrogen in underground hydrogen storage.展开更多
Objective Atherosclerosis involves not only the narrowing of blood vessels and plaque accumulation but also changes in plaque composition and stability,all of which are critical for disease progression.Conventional im...Objective Atherosclerosis involves not only the narrowing of blood vessels and plaque accumulation but also changes in plaque composition and stability,all of which are critical for disease progression.Conventional imaging techniques such as magnetic resonance angiography(MRA)and digital subtraction angiography(DSA)primarily assess luminal narrowing and plaque size,but have limited capability in identifying plaque instability and inflammation within the vascular muscle wall.This study aimed to develop and evaluate a novel imaging approach using ligand-modified nanomagnetic contrast(lmNMC)nanoprobes in combination with molecular magnetic resonance imaging(mMRI)to visualize and quantify vascular inflammation and plaque characteristics in a rabbit model of atherosclerosis.Methods A rabbit model of atherosclerosis was established and underwent mMRI before and after administration of lmNMC nanoprobes.Radiomic features were extracted from segmented images using discrete wavelet transform(DWT)to assess spatial frequency changes and gray-level co-occurrence matrix(GLCM)analysis to evaluate textural properties.Further radiomic analysis was performed using neural network-based regression and clustering,including the application of self-organizing maps(SOMs)to validate the consistency of radiomic pattern between training and testing data.Results Radiomic analysis revealed significant changes in spatial frequency between pre-and post-contrast images in both the horizontal and vertical directions.GLCM analysis showed an increase in contrast from 0.08463 to 0.1021 and a slight decrease in homogeneity from 0.9593 to 0.9540.Energy values declined from 0.2256 to 0.2019,while correlation increased marginally from 0.9659 to 0.9708.Neural network regression demonstrated strong convergence between target and output coordinates.Additionally,SOM clustering revealed consistent weight locations and neighbor distances across datasets,supporting the reliability of the radiomic validation.Conclusion The integration of lmNMC nanoprobes with mMRI enables detailed visualization of atherosclerotic plaques and surrounding vascular inflammation in a preclinical model.This method shows promise for enhancing the characterization of unstable plaques and may facilitate early detection of high-risk atherosclerotic lesions,potentially improving diagnostic and therapeutic strategies.展开更多
文摘Neurodegenerative disorders,including Alzheimer’s disease(AD),Parkinson’s disease(PD),and amyotrophic lateral sclerosis,impose a considerable social and economic burden on society and have dramatic consequences for individuals and their families.The majority of existing interventions have been found to be capable of only a slight modification of disease progression or to moderately delay significant functional decline in motor,cognitive,or mental domains.
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,under grant No.(IFPDP-261-22).
文摘Medical imaging is essential in modern health care,allowing accurate diagnosis and effective treatment planning.These images,however,often demonstrate low contrast,noise,and brightness distortion that reduce their diagnostic reliability.This review presents a structured and comprehensive analysis of advanced histogram equalization(HE)-based techniques for medical image enhancement.Our review methodology encompasses:(1)classical HE approaches and related limitations in medical domains;(2)adaptive schemes like Adaptive Histogram Equalization(AHE)and Contrast Limited Adaptive Histogrma Equalization(CLAHE)and their advance variants;(3)brightnesspreserving schemes like BBHE and MMBEBHE and related algorithms;(4)dynamic and recursive histogram equalization methods incorporating DHE and RMSHE;(5)fuzzy logic-based enhancement methodologies addressing uncertainty and noise in medical images;and(6)hybrid optimization methodologies through the application of metaheuristic algorithms(World Cup Optimization,Particle Swarm Optimization,Genetic Algorithms,along with histogram-based methodologies.)There is also a comparative discussion given based on contrast improvement,image brightness preservation,noise management,and computational efficiency.Such advancements have better capabilities of improving image quality,which is more important for improved diagnosis and image analysis.
基金supported by the National Natural Science Foundation of China(82427808,61875085)the Jiangsu Provincial University Natural Science Foundation(25KJB413004)+1 种基金the Nanjing Health Science and Technology Development Foundation(ZKX24043)the Fundamental Research Funds for the Central Universities(NJ2024029).
文摘Microwave ablation(MWA)is a minimally invasive technique for treating hepatic tumors,necessitating precise monitoring to ensure treatment efficacy and minimize damage to surrounding tissues.This study explores the potential of photoacoustic imaging(PAI)in monitoring MWA by examining ex vivo porcine liver tissues.In this study,a comprehensive analysis of photoacoustic signals was performed to compare the main lobe width(MLW)between ablated and normal regions in porcine liver tissue.Histological staining with succinate dehydrogenase(SDH)and shear wave elastography(SWE)were employed to validate the changes in tissue elasticity after ablation.The analysis demonstrated a notable reduction in the MLW of the average A-lines in ablated tissues compared to nonablated regions(p<0.01).This reduction,attributed to increased tissue density and enhanced elasticity,indicates accelerated sound propagation in thermally ablated areas,which then serves as a critical parameter for mapping tissue characteristics.The reconstruction of the MLW distribution successfully delineated the ablated regions,and was consistent with the results of SDH staining and SWE.In addition,MLW-based imaging exhibited higher spatial resolution compared to SWE.Incorporating MLW analysis into PAI may be a promising strategy to improve the accuracy and effectiveness of MWA monitoring in clinical settings.
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2601).
文摘This paper introduces a novel fractional-order model based on the Caputo-Fabrizio(CF)derivative for analyzing computer virus propagation in networked environments.The model partitions the computer population into four compartments:susceptible,latently infected,breaking-out,and antivirus-capable systems.By employing the CF derivative—which uses a nonsingular exponential kernel—the framework effectively captures memory-dependent and nonlocal characteristics intrinsic to cyber systems,aspects inadequately represented by traditional integer-order models.Under Lipschitz continuity and boundedness assumptions,the existence and uniqueness of solutions are rigorously established via fixed-point theory.We develop a tailored two-step Adams-Bashforth numerical scheme for the CF framework and prove its second-order accuracy.Extensive numerical simulations across various fractional orders reveal that memory effects significantly influence virus transmission and control dynamics;smaller fractional orders produce more pronounced memory effects,delaying both infection spread and antivirus activation.Further theoretical analysis,including Hyers-Ulam stability and sensitivity assessments,reinforces the model’s robustness and identifies key parameters governing virus dynamics.The study also extends the framework to incorporate stochastic effects through a stochastic CF formulation.These results underscore fractional-order modeling as a powerful analytical tool for developing robust and effective cybersecurity strategies.
基金supports by the National Natural Science Foundation of China(Nos.82201135)"2015"Cultivation Program for Reserve Talents for Academic Leaders of Nanjing Stomatological School,Medical School of Nanjing University(No.0223A204).
文摘Early correction of childhood malocclusion is timely managing morphological,structural,and functional abnormalities at different dentomaxillofacial developmental stages.The selection of appropriate imaging examination and comprehensive radiological diagnosis and analysis play an important role in early correction of childhood malocclusion.This expert consensus is a collaborative effort by multidisciplinary experts in dentistry across the nation based on the current clinical evidence,aiming to provide general guidance on appropriate imaging examination selection,comprehensive and accurate imaging assessment for early orthodontic treatment patients.
文摘AIM:To evaluate the efficacy of computer-assisted color analysis of colorectal lesions using a novel auto-fluorescence imaging(AFI)system to distinguish neoplastic lesions from non-neoplastic lesions and to predict the depth of invasion.METHODS:From January 2013 to April 2013,consecutive patients with known polyps greater than 5 mm in size who were scheduled to undergo endoscopic treatment at The Jikei University Hospital were prospectively recruited for this study.All lesions were evaluated using a novel AFI system,and color-tone sampling was performed in a region of interest determined from narrow band imaging or from chromoendoscopy findings without magnification.The green/red(G/R)ratio for each lesion on the AFI images was calculated automatically using a computer-assisted color analysis system that permits real-time color analysis during endoscopic procedures.RESULTS:A total of 88 patients with 163 lesions were enrolled in this study.There were significant differences in the G/R ratios of hyperplastic polyps(non-neoplastic lesions),adenoma/intramucosal cancer/submucosal(SM)superficial cancer,and SM deep cancer(P<0.0001).The mean±SD G/R ratios were 0.984±0.118in hyperplastic polyps and 0.827±0.081 in neoplastic lesions.The G/R ratios of hyperplastic polyps were significantly higher than those of neoplastic lesions(P<0.001).When a G/R ratio cut-off value of>0.89 was applied to determine non-neoplastic lesions,the sensitivity,specificity,positive predictive value(PPV),negative predictive value(NPV),and accuracy were 83.9%,82.6%,53.1%,95.6%and 82.8%,respectively.For neoplastic lesions,the mean G/R ratio was 0.834±0.080 in adenoma/intramucosal cancer/SM superficial cancer and 0.746±0.045 in SM deep cancer.The G/R ratio of adenoma/intramucosal cancer/SM superficial cancer was significantly higher than that of SM deep cancer(P<0.01).When a G/R ratio cut-off value of<0.77 was applied to distinguish SM deep cancers,the sensitivity,specificity,PPV,NPV,and accuracy were80.0%,84.4%,29.6%,98.1%and 84.1%,respectively.CONCLUSION:The novel AFI system with color analysis was effective in distinguishing non-neoplastic lesions from neoplastic lesions and might allow determination of the depth of invasion.
文摘BACKGROUND Pancreatic cancer remains one of the most lethal malignancies worldwide,with a poor prognosis often attributed to late diagnosis.Understanding the correlation between pathological type and imaging features is crucial for early detection and appropriate treatment planning.AIM To retrospectively analyze the relationship between different pathological types of pancreatic cancer and their corresponding imaging features.METHODS We retrospectively analyzed the data of 500 patients diagnosed with pancreatic cancer between January 2010 and December 2020 at our institution.Pathological types were determined by histopathological examination of the surgical spe-cimens or biopsy samples.The imaging features were assessed using computed tomography,magnetic resonance imaging,and endoscopic ultrasound.Statistical analyses were performed to identify significant associations between pathological types and specific imaging characteristics.RESULTS There were 320(64%)cases of pancreatic ductal adenocarcinoma,75(15%)of intraductal papillary mucinous neoplasms,50(10%)of neuroendocrine tumors,and 55(11%)of other rare types.Distinct imaging features were identified in each pathological type.Pancreatic ductal adenocarcinoma typically presents as a hypodense mass with poorly defined borders on computed tomography,whereas intraductal papillary mucinous neoplasms present as characteristic cystic lesions with mural nodules.Neuroendocrine tumors often appear as hypervascular lesions in contrast-enhanced imaging.Statistical analysis revealed significant correlations between specific imaging features and pathological types(P<0.001).CONCLUSION This study demonstrated a strong association between the pathological types of pancreatic cancer and imaging features.These findings can enhance the accuracy of noninvasive diagnosis and guide personalized treatment approaches.
文摘Background:Congenital hepatic hemangioma(CHH)is a rare benign vascular tumor that occurs prenatally.However,only a few cases have been summarized and evaluated for the prenatal and postnatal imaging features of CHH,and no studies have conducted long-term follow-up on it.This study aimed to explore the ultrasound and magnetic resonance features,growth patterns,and clinical outcomes of CHH.Methods:Thirty-six pregnancies with a prenatal fetal diagnosis and postnatal diagnosis of CHH were studied.CHHs were grouped into those with a diameter≥4 cm and those with a diameter<4 cm according to the largest diameter.Fisher's exact test was used to compare the imaging characteristics between the groups.The volume of CHHs was measured at each follow-up visit to plot the growth pattern of the tumors,and the volume of CHHs was compared before and after birth using a rank sum test analysis.Results:Thirty-three cases of CHHs were confirmed by postnatal imaging,and three were confirmed by a biopsy.Mixed echoes were more common in the diameter≥4 cm group than in the diameter<4 cm group(p=0.026).Complications were more likely to occur in the large-diameter group.Eighteen(54.5%)cases were classified as rapidly involuting congenital hemangioma,nine(27.3%)as partially involuting congenital hemangioma,and two(6.1%)as noninvoluting congenital hemangioma.A new type of CHH was identified in which four(12.1%)cases continued to proliferate after birth and spontaneously subsided in subsequent months.The CHH volume decreased with age and was significantly decreased at 9 months postnatal compared to birth(p=0.001).Conclusion:This study showed the imaging features of CHH were associated with the lesion size.Based on postnatal follow-up,a new type of CHH was identified.If there are no complications at birth in CHH cases,a good prognosis is indicated.
基金supported in part by the National Natural Science Foundation of China (No. U23B2011)。
文摘Video snapshot compressive imaging(Video SCI) modulates scenes using various encoding masks and captures compressed measurements with a low-speed camera during a single exposure. Subsequently, reconstruction algorithms restore image sequences of dynamic scenes, offering advantages such as reduced bandwidth and storage space requirements. The temporal correlation in video data is crucial for Video SCI, as it leverages the temporal relationships among frames to enhance the efficiency and quality of reconstruction algorithms, particularly for fast-moving objects.This paper discretizes video frames to create image datasets with the same data volume but differing temporal correlations. We utilized the state-of-the-art(SOTA) reconstruction framework, EfficientSCI++, to train various compressed reconstruction models with these differing temporal correlations. Evaluating the reconstruction results from these models, our simulation experiments confirm that a reduction in temporal correlation leads to decreased reconstruction accuracy. Additionally, we simulated the reconstruction outcomes of datasets devoid of temporal correlation, illustrating that models trained on non-temporal data affect the temporal feature extraction capabilities of transformers, resulting in negligible impacts on the evaluation of reconstruction results for non-temporal correlation test datasets.
基金Supported by Interdisciplinary Program of Shanghai Jiao Tong University,No.YG2024 LC01National Natural Science Foundation of China,No.62406190.
文摘Confocal laser endomicroscopy(CLE)has become an indispensable tool in the diagnosis and detection of gastrointestinal(GI)diseases due to its high-resolution and high-contrast imaging capabilities.However,the early-stage imaging changes of gastrointestinal disorders are often subtle,and traditional medical image analysis methods rely heavily on manual interpretation,which is time-consuming,subject to observer variability,and inefficient for accurate lesion identification across large-scale image datasets.With the introduction of artificial intelligence(AI)technologies,AI-driven CLE image analysis systems can automatically extract pathological features and have demonstrated significant clinical value in lesion recognition,classification diagnosis,and malignancy prediction of GI diseases.These systems greatly enhance diagnostic efficiency and early detection capabilities.This review summarizes the applications of AI-assisted CLE in GI diseases,analyzes the limitations of current technologies,and explores future research directions.It is expected that the deep integration of AI and confocal imaging technologies will provide strong support for precision diagnosis and personalized treatment in the field of gastrointestinal disorders.
文摘Leesmidt et al present a comprehensive analysis of abdominal vascular flow in children using four-dimensional(4D)flow magnetic resonance imaging(MRI),aim to establish normal hemodynamic values for the abdominal visceral organs and to assess the feasibility of 4D flow MRI(4D-f-MRI)in this population.The researchers performed 4D-f-MRI on 9 pediatric patients with a history or suspi-cion of bowel pathology.Flow velocities were measured in the abdominal aorta and superior and inferior mesenteric arteries.The quality of the 4D-f-MRI images was evaluated,and the agreement between the measured flow velocities and those obtained from Duplex ultrasound was established.However,due to the specific limitations of this work,future studies should address the issues of small sample size and the specific age group design.
文摘Objective To evaluate the cost-effectiveness of gadopentetate dimeglumine(Gd-DTPA)and gadobenate dimeglumine(Gd-BOPTA)magnetic resonance imaging(MRI)contrast agents for the early diagnosis of hepatocellular carcinoma(HCC)from the perspective of China’s healthcare system.Methods A decision tree+partitioned survival model was constructed for early diagnosis of HCC based on literature data.Taking quality-adjusted life year(QALY)as the main health outcome measure for incremental cost-effectiveness ratio(ICER)analysis,the sensitivity analysis by Monte Carlo simulation was constructed to generate corresponding tornado diagram,incremental cost-effectiveness scatter plot,and cost-effectiveness acceptability curve.Results and Conclusion The basic analysis results showed that the ICER value of Gd-BOPTA diagnostic scheme compared with Gd-DTPA diagnostic scheme was 17302.46 yuan/QALY,which is less than 1 times of China’s gross domestic product(GDP)per capita.The sensitivity analysis results showed that the cost of delayed treatment and timely treatment had a significant impact on the results.When the willingness to pay(WTP)was 1 time of GDP per capita,the probability of cost-effectiveness advantage of Gd-BOPTA diagnostic scheme was 65.30%.When the WTP value is set at 1 times of GDP per capita,Gd-BOPTA MRI has cost-effectiveness advantages for the early diagnosis of HCC.
基金supported by the Jilin Province Science and Technology Development Plan Item (No.20240402068GH)。
文摘To identify coatings and analyze the anti-detection capabilities of camouflage patterns, material samples can be prepared using the super-pixel segmentation method. A spectral polarization imaging system is developed, based on the principle of bidirectional reflectance distribution function(BRDF), to obtain spectral reflection intensities of coatings at full spatial angles, and use polarization images to calculate the refractive index by the Fresnel equation. The index is then coupled into TorranceSparrow model to simulate the spectral scattering intensity to mutually verify the experimental results. The spectral scattering characteristics of standard camouflage patterns are then revealed and pinpoint the signature band and the angle of reflecting sensitivity.
基金Qinghai Provincial Health Commission Medical and Health Science and Technology(Project No.:2022-wjzdx-63)。
文摘This study aimed to analyze the early high-resolution CT(HRCT)manifestations and dynamic imaging changes of coronavirus disease 2019(COVID-19)in Qinghai Province.A total of 24 nucleic acid-positive COVID-19 patients admitted to our hospital between January 2020 and November 2021 were included.All patients underwent HRCT examinations,and lesion characteristics—including number,distribution,morphology,and surrounding involvement were analyzed.Among the 24 patients,systemic and respiratory circulatory symptoms were more common than other symptoms(P<0.05).There were no significant differences in the lung lobes,relative positions,quantity,size,and density of lesions across different stages of the disease course(P>0.05).Within the same disease stage,lesions were primarily located in the lower lobes of both lungs,the peripheral lung fields,and a combination of peripheral and central regions,with single and multiple lesions being the most common.Lesion morphology varied significantly across disease stages(P<0.05),including differences between patchy and striped lesions,striped and massive lesions(P<0.05),and patchy and massive lesions(P<0.05).The incidence of striped lesions was higher in the progressive and recovery stages than in the early stage,showing an upward trend.There were no significant differences in pleural thickening,pleural effusion,mediastinal lymph node enlargement,or pericardial effusion across different disease stages(P>0.05).Common HRCT signs observed at all stages included air bronchograms,paving stone patterns,halo signs,subpleural lines,and grid-like patterns.The main patterns of lesion progression were an increase in lesion size(16/24,66.67%),an increase in the number of lesions(17/24,70.83%),changes in lesion density(20/24,80.33%),and localized lesion increase and partial absorption(6/24,25.00%).In conclusion,the HRCT manifestations and evolution of lung lesions in COVID-19 patients are complex and varied,with a progressive increase in striped lesions potentially serving as a characteristic imaging feature of the disease.
基金Beijing Hospitals Authority Clinical Medicine Development of Special Funding Support,Code(Project No.:YGLX202514)。
文摘Objective:To explore the research landscape and hotspots of Computerized Respiratory Sound Analysis(CORSA)and provide a reference for future in-depth studies.Methods:Literature related to CORSA published up to August 27,2020,was retrieved from the Web of Science Core Collection.CiteSpace 5.6.R3 was used to perform co-authorship analysis,institutional collaboration analysis,keyword co-occurrence analysis,and co-citation analysis.Results:A total of 1,897 publications were included.Co-authorship analysis identified several influential contributors,including Zahra Moussavi,Kenneth Sundaraj,and H.Pasterkamp.Major research institutions included the University of Manitoba,the University of Queensland,and Aristotle University of Thessaloniki.Keyword co-occurrence analysis indicated that“respiratory sound,”“lung sound,”“asthma,”“children,”and“classification”were major research themes.The most frequently co-cited articles were published by Arati Gurung(2011),Mohammed Bahoura(2009),and H.Pasterkamp(1997).Highly cited journals included Chest,the American Journal of Respiratory and Critical Care Medicine,and IEEE Transactions on Biomedical Engineering.Conclusion:CORSA research is primarily driven by European and North American scholars and institutions,with China still in an early stage of development.Current hotspots include respiratory sound acquisition and processing,feature extraction methods such as Mel-frequency cepstral coefficients(MFCCs),and classification techniques based on machine learning and deep learning.CORSA is suitable for diverse populations and is widely applied in respiratory diseases,especially bronchial asthma.Its non-invasive nature offers particular advantages for infants and pregnant women.Although CORSA demonstrates strong clinical potential,its clinical translation remains limited.Advancing clinical applications and bridging the gap between research and practice will be key directions for future development.The prominence of top-tier respiratory and engineering journals among citations suggests that CORSA is an emerging and influential research frontier.
基金support from the National Key Research and Development Program of China(No.2024YFB3713705)is acknowledgedWangzhong Mu would like to acknowledge the Strategic Mobility,Sweden(SSF,No.SM22-0039)+1 种基金the Swedish Foundation for International Cooperation in Research and Higher Education(STINT,No.IB2022-9228)the Jernkontoret(Sweden)for supporting this clean steel research.Gonghao Lian would like to acknowledge China Scholarship Council(CSC,No.202306080032).
文摘The detection and characterization of non-metallic inclusions are essential for clean steel production.Recently,imaging analysis combined with high-dimensional data processing of metallic materials using artificial intelligence(AI)-based machine learning(ML)has developed rapidly.This technique has achieved impressive results in the field of inclusion classification in process metallurgy.The present study surveys the ML modeling of inclusion prediction in advanced steels,including the detection,classification,and feature prediction of inclusions in different steel grades.Studies on clean steel with different features based on data and image analysis via ML are summarized.Regarding the data analysis,the inclusion prediction methodology based on ML establishes a connection between the experimental parameters and inclusion characteristics and analyzes the importance of the experimental parameters.Regarding the image analysis,the focus is placed on the classification of different types of inclusions via deep learning,in comparison with data analysis.Finally,further development of inclusion analyses using ML-based methods is recommended.This work paves the way for the application of AIbased methodologies for ultraclean-steel studies from a sustainable metallurgy perspective.
文摘Integration of artificial intelligence increases in all aspects of human life,particularly in healthcare systems.Traumatic brain injury is a significant cause of mortality and long-term disability,with an important impact on the socioeconomic system of healthcare.The role of artificial intelligence in imaging and outcome prediction for traumatic brain injury patients is reviewed with a particular emphasis to the characteristics of machine and deep learning methods.Evidence of potential improvement in the clinical practice in discussed.
文摘This study aims to analyze and discuss the current situation of quality control and quality management in medical imaging.Firstly,it defines the concepts of quality management and image quality,emphasizing the crucial role of the department's Quality and Safety Team in imaging quality management[1].Subsequently,it details the composition and responsibilities of the Quality and Safety Management Group,as well as the specific divisions of labor and tasks of the Quality Management Team.The effective implementation of comprehensive quality control in the imaging department is ensured through the"Seven-Star Sword"method,which encompasses total process control,organization-wide participation,and total quality control[2].Furthermore,the application of the PDCA(Plan-Do-Check-Act)cycle in quality management is explored,along with strategies to improve patient satisfaction by reducing the waiting time for imaging reports[3].Finally,the management of a key departmental medical quality control indicator—critical values—is analyzed,and improvement measures are proposed[4].This study underscores the importance of quality management in enhancing medical service quality and ensuring patient safety,and puts forward specific implementation strategies and improvement methods.
基金supported by the National Key Research and Development Project(Grant No.2023YFE0110900)the National Natural Science Foundation of China(Grant Nos.42320104003,42477168).
文摘Underground hydrogen storage has gained interest in recent years due to the enormous demand for clean energy.Hydrogen is more diffusive than air,with a smaller density and lower viscosity.These unique properties introduce distinctive hydrodynamic phenomena in hydrogen storage,one of which is fingering.Fingering could induce the fluid trapped in small clusters of pores,leading to a dramatic decrease in hydrogen saturation and a lower recovery rate.In this study,numerical simulations are performed at the microscopic scale to understand the evolution of hydrogen saturation and the impacts of injection and withdrawal cycles.Two sets of micromodels with different porosity(0.362 and 0.426)and minimum sizes of pore throats(0.362 mm and 0.181 mm)are developed in the numerical model.A parameter analysis is then conducted to understand the influence of injection velocity(in the range of 10^(-2)m/s to 10^(-5)m/s)and porous structure on the fingering pattern,followed by an image analysis to capture the evolution of the fingering pattern.Viscous fingering,capillary fingering,and crossover fingering are observed and identified under different boundary conditions.The fractal dimension,specific area,mean angle,and entropy of fingers are proposed as geometric descriptors to characterize the shape of the fingering pattern.When porosity increases from 0.362 to 0.426,the saturation of hydrogen increases by 26.2%.Narrower pore throats elevate capillary resistance,which hinders fluid invasion.These results underscore the importance of pore structures and the interaction between viscous and capillary forces for hydrogen recovery efficiency.This work illuminates the influence of the pore structures and the fluid properties on the immiscible displacement of hydrogen and can be further extended to optimize the injection strategy of hydrogen in underground hydrogen storage.
基金supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF),funded by the Ministry of Education(Grant number:RS-2023-00248763).
文摘Objective Atherosclerosis involves not only the narrowing of blood vessels and plaque accumulation but also changes in plaque composition and stability,all of which are critical for disease progression.Conventional imaging techniques such as magnetic resonance angiography(MRA)and digital subtraction angiography(DSA)primarily assess luminal narrowing and plaque size,but have limited capability in identifying plaque instability and inflammation within the vascular muscle wall.This study aimed to develop and evaluate a novel imaging approach using ligand-modified nanomagnetic contrast(lmNMC)nanoprobes in combination with molecular magnetic resonance imaging(mMRI)to visualize and quantify vascular inflammation and plaque characteristics in a rabbit model of atherosclerosis.Methods A rabbit model of atherosclerosis was established and underwent mMRI before and after administration of lmNMC nanoprobes.Radiomic features were extracted from segmented images using discrete wavelet transform(DWT)to assess spatial frequency changes and gray-level co-occurrence matrix(GLCM)analysis to evaluate textural properties.Further radiomic analysis was performed using neural network-based regression and clustering,including the application of self-organizing maps(SOMs)to validate the consistency of radiomic pattern between training and testing data.Results Radiomic analysis revealed significant changes in spatial frequency between pre-and post-contrast images in both the horizontal and vertical directions.GLCM analysis showed an increase in contrast from 0.08463 to 0.1021 and a slight decrease in homogeneity from 0.9593 to 0.9540.Energy values declined from 0.2256 to 0.2019,while correlation increased marginally from 0.9659 to 0.9708.Neural network regression demonstrated strong convergence between target and output coordinates.Additionally,SOM clustering revealed consistent weight locations and neighbor distances across datasets,supporting the reliability of the radiomic validation.Conclusion The integration of lmNMC nanoprobes with mMRI enables detailed visualization of atherosclerotic plaques and surrounding vascular inflammation in a preclinical model.This method shows promise for enhancing the characterization of unstable plaques and may facilitate early detection of high-risk atherosclerotic lesions,potentially improving diagnostic and therapeutic strategies.
