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Transparent and Accurate COVID-19 Diagnosis:Integrating Explainable AI with Advanced Deep Learning in CT Imaging
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作者 Mohammad Mehedi Hassan Salman A.AlQahtani +1 位作者 Mabrook S.AlRakhami Ahmed Zohier Elhendi 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第6期3101-3123,共23页
In the current landscape of the COVID-19 pandemic,the utilization of deep learning in medical imaging,especially in chest computed tomography(CT)scan analysis for virus detection,has become increasingly significant.De... In the current landscape of the COVID-19 pandemic,the utilization of deep learning in medical imaging,especially in chest computed tomography(CT)scan analysis for virus detection,has become increasingly significant.Despite its potential,deep learning’s“black box”nature has been a major impediment to its broader acceptance in clinical environments,where transparency in decision-making is imperative.To bridge this gap,our research integrates Explainable AI(XAI)techniques,specifically the Local Interpretable Model-Agnostic Explanations(LIME)method,with advanced deep learning models.This integration forms a sophisticated and transparent framework for COVID-19 identification,enhancing the capability of standard Convolutional Neural Network(CNN)models through transfer learning and data augmentation.Our approach leverages the refined DenseNet201 architecture for superior feature extraction and employs data augmentation strategies to foster robust model generalization.The pivotal element of our methodology is the use of LIME,which demystifies the AI decision-making process,providing clinicians with clear,interpretable insights into the AI’s reasoning.This unique combination of an optimized Deep Neural Network(DNN)with LIME not only elevates the precision in detecting COVID-19 cases but also equips healthcare professionals with a deeper understanding of the diagnostic process.Our method,validated on the SARS-COV-2 CT-Scan dataset,demonstrates exceptional diagnostic accuracy,with performance metrics that reinforce its potential for seamless integration into modern healthcare systems.This innovative approach marks a significant advancement in creating explainable and trustworthy AI tools for medical decisionmaking in the ongoing battle against COVID-19. 展开更多
关键词 Explainable AI COVID-19 ct images deep learning
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Cardiac CT Image Segmentation for Deep Learning-Based Coronary Calcium Detection Using K-Means Clustering and Grabcut Algorithm 被引量:1
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作者 Sungjin Lee Ahyoung Lee Min Hong 《Computer Systems Science & Engineering》 SCIE EI 2023年第8期2543-2554,共12页
Specific medical data has limitations in that there are not many numbers and it is not standardized.to solve these limitations,it is necessary to study how to efficiently process these limited amounts of data.In this ... Specific medical data has limitations in that there are not many numbers and it is not standardized.to solve these limitations,it is necessary to study how to efficiently process these limited amounts of data.In this paper,deep learning methods for automatically determining cardiovascular diseases are described,and an effective preprocessing method for CT images that can be applied to improve the performance of deep learning was conducted.The cardiac CT images include several parts of the body such as the heart,lungs,spine,and ribs.The preprocessing step proposed in this paper divided CT image data into regions of interest and other regions using K-means clustering and the Grabcut algorithm.We compared the deep learning performance results of original data,data using only K-means clustering,and data using both K-means clustering and the Grabcut algorithm.All data used in this paper were collected at Soonchunhyang University Cheonan Hospital in Korea and the experimental test proceeded with IRB approval.The training was conducted using Resnet 50,VGG,and Inception resnet V2 models,and Resnet 50 had the best accuracy in validation and testing.Through the preprocessing process proposed in this paper,the accuracy of deep learning models was significantly improved by at least 10%and up to 40%. 展开更多
关键词 Deep learning VGG resnet ct image processing
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Analysis of Imaging Characteristics and Dynamic Changes of 3 Cases of Severe Novel Coronavirus Pneumonia in Qinghai Province
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作者 Yingfang Yu Ruiyun Zhao +3 位作者 Changde Li Fuqiang Ma Lingyun Guo Yang Li 《Journal of Clinical and Nursing Research》 2024年第3期120-126,共7页
Objective:To analyze the characteristics,dynamic changes,and outcomes of the first imaging manifestations of 3 patients with severe COVID-19 in our hospital.Methods:Computed tomography(CT)findings of 3 patients with s... Objective:To analyze the characteristics,dynamic changes,and outcomes of the first imaging manifestations of 3 patients with severe COVID-19 in our hospital.Methods:Computed tomography(CT)findings of 3 patients with severe COVID-19 who tested positive by the nucleic acid test in our hospital were selected,mainly focusing on the morphology,distribution characteristics,and dynamic changes of the first CT findings.Results:3 patients with severe pneumonia were older,with one aged 80.The first chest CT examination for all 3 patients differed.Imaging showed a leafy distribution of consolidation,primarily affecting the lower lobes of both lungs and extending subpleurally.A grid-like pattern was observed,along with changes in the consolidation and air bronchogram.These changes had slower absorption,especially in patients with underlying diseases.Conclusion:CT manifestations of severe COVID-19 have specific characteristics and the analysis of their characteristics and dynamic changes provide valuable insights for clinical treatment. 展开更多
关键词 COVID-19 imagING ct findings Dynamic changes
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Analysis of The Value of Multi-Slice Spiral CT and Magnetic Resonance Imaging in The Diagnosis of Carpal Joint Injury
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作者 Rongfeng An Juntao Lu +1 位作者 Jingzhong Liu Fang Yan 《Journal of Clinical and Nursing Research》 2024年第5期145-149,共5页
Objective:To analyze the value of multi-slice spiral computed tomography(CT)and magnetic resonance imaging(MRI)in the diagnosis of carpal joint injury.Methods:A total of 130 patients with suspected wrist injuries admi... Objective:To analyze the value of multi-slice spiral computed tomography(CT)and magnetic resonance imaging(MRI)in the diagnosis of carpal joint injury.Methods:A total of 130 patients with suspected wrist injuries admitted to the Department of Orthopedics of our hospital from January 2023 to January 2024 were selected and randomly divided into a single group(n=65)and a joint group(n=65).The single group was diagnosed using multi-slice spiral CT,and the joint group was diagnosed using multi-slice spiral CT and magnetic resonance imaging,with pathological diagnosis as the gold standard.The diagnostic results of both groups were compared to the gold standard,and the diagnostic energy efficiency of both groups was compared.Results:The diagnostic results of the single group compared with the gold standard were significant(P<0.05).The diagnostic results of the joint group compared with the gold standard were not significant(P>0.05).The sensitivity and accuracy of diagnosis in the joint group were significantly higher than that in the single group(P<0.05).The specificity of diagnosis in the joint group was higher as compared to that in the single group(P>0.05).Conclusion:The combination of multi-slice spiral CT and MRI was highly accurate in diagnosing wrist injuries,and the misdiagnosis rate and leakage rate were relatively low.Hence,this diagnostic program is recommended to be popularized. 展开更多
关键词 Multi-slice ct Magnetic resonance imaging Carpal joint injury Joint diagnosis
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A new imaging mode based on X-ray CT as prior image and sparsely sampled projections for rapid clinical proton CT
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作者 Yu-Qing Yang Wen-Cheng Fang +4 位作者 Xiao-Xia Huang Qiang Du Ming Li Jian Zheng Zhen-Tang Zhao 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2023年第8期64-74,共11页
Proton computed tomography(CT)has a distinct practical significance in clinical applications.It eliminates 3–5%errors caused by the transformation of Hounsfield unit(HU)to relative stopping power(RSP)values when usin... Proton computed tomography(CT)has a distinct practical significance in clinical applications.It eliminates 3–5%errors caused by the transformation of Hounsfield unit(HU)to relative stopping power(RSP)values when using X-ray CT for positioning and treatment planning systems(TPSs).Following the development of FLASH proton therapy,there are increased requirements for accurate and rapid positioning in TPSs.Thus,a new rapid proton CT imaging mode is proposed based on sparsely sampled projections.The proton beam was boosted to 350 MeV by a compact proton linear accelerator(LINAC).In this study,the comparisons of the proton scattering with the energy of 350 MeV and 230 MeV are conducted based on GEANT4 simulations.As the sparsely sampled information associated with beam acquisitions at 12 angles is not enough for reconstruction,X-ray CT is used as a prior image.The RSP map generated by converting the X-ray CT was constructed based on Monte Carlo simulations.Considering the estimation of the most likely path(MLP),the prior image-constrained compressed sensing(PICCS)algorithm is used to reconstruct images from two different phantoms using sparse proton projections of 350 MeV parallel proton beam.The results show that it is feasible to realize the proton image reconstruction with the rapid proton CT imaging proposed in this paper.It can produce RSP maps with much higher accuracy for TPSs and fast positioning to achieve ultra-fast imaging for real-time image-guided radiotherapy(IGRT)in clinical proton therapy applications. 展开更多
关键词 Proton ct Real-time image guidance image reconstruction Proton therapy
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Stacked Gated Recurrent Unit Classifier with CT Images for Liver Cancer Classification
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作者 Mahmoud Ragab Jaber Alyami 《Computer Systems Science & Engineering》 SCIE EI 2023年第3期2309-2322,共14页
Liver cancer is one of the major diseases with increased mortality in recent years,across the globe.Manual detection of liver cancer is a tedious and laborious task due to which Computer Aided Diagnosis(CAD)models hav... Liver cancer is one of the major diseases with increased mortality in recent years,across the globe.Manual detection of liver cancer is a tedious and laborious task due to which Computer Aided Diagnosis(CAD)models have been developed to detect the presence of liver cancer accurately and classify its stages.Besides,liver cancer segmentation outcome,using medical images,is employed in the assessment of tumor volume,further treatment plans,and response moni-toring.Hence,there is a need exists to develop automated tools for liver cancer detection in a precise manner.With this motivation,the current study introduces an Intelligent Artificial Intelligence with Equilibrium Optimizer based Liver cancer Classification(IAIEO-LCC)model.The proposed IAIEO-LCC technique initially performs Median Filtering(MF)-based pre-processing and data augmentation process.Besides,Kapur’s entropy-based segmentation technique is used to identify the affected regions in liver.Moreover,VGG-19 based feature extractor and Equilibrium Optimizer(EO)-based hyperparameter tuning processes are also involved to derive the feature vectors.At last,Stacked Gated Recurrent Unit(SGRU)classifier is exploited to detect and classify the liver cancer effectively.In order to demonstrate the superiority of the proposed IAIEO-LCC technique in terms of performance,a wide range of simulations was conducted and the results were inspected under different measures.The comparison study results infer that the proposed IAIEO-LCC technique achieved an improved accuracy of 98.52%. 展开更多
关键词 Liver cancer image segmentation artificial intelligence deep learning ct images parameter tuning
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Variant Wasserstein Generative Adversarial Network Applied on Low Dose CT Image Denoising
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作者 Anoud A.Mahmoud Hanaa A.Sayed Sara S.Mohamed 《Computers, Materials & Continua》 SCIE EI 2023年第5期4535-4552,共18页
Computed Tomography(CT)images have been extensively employed in disease diagnosis and treatment,causing a huge concern over the dose of radiation to which patients are exposed.Increasing the radiation dose to get a be... Computed Tomography(CT)images have been extensively employed in disease diagnosis and treatment,causing a huge concern over the dose of radiation to which patients are exposed.Increasing the radiation dose to get a better image may lead to the development of genetic disorders and cancer in the patients;on the other hand,decreasing it by using a Low-Dose CT(LDCT)image may cause more noise and increased artifacts,which can compromise the diagnosis.So,image reconstruction from LDCT image data is necessary to improve radiologists’judgment and confidence.This study proposed three novel models for denoising LDCT images based on Wasserstein Generative Adversarial Network(WGAN).They were incorporated with different loss functions,including Visual Geometry Group(VGG),Structural Similarity Loss(SSIM),and Structurally Sensitive Loss(SSL),to reduce noise and preserve important information on LDCT images and investigate the effect of different types of loss functions.Furthermore,experiments have been conducted on the Graphical Processing Unit(GPU)and Central Processing Unit(CPU)to compare the performance of the proposed models.The results demonstrated that images from the proposed WGAN-SSIM,WGAN-VGG-SSIM,and WGAN-VGG-SSL were denoised better than those from state-of-the-art models(WGAN,WGAN-VGG,and SMGAN)and converged to a stable equilibrium compared with WGAN and WGAN-VGG.The proposed models are effective in reducing noise,suppressing artifacts,and maintaining informative structure and texture details,especially WGAN-VGG-SSL which achieved a high peak-signalto-noise ratio(PNSR)on both GPU(26.1336)and CPU(25.8270).The average accuracy of WGAN-VGG-SSL outperformed that of the state-ofthe-art methods by 1 percent.Experiments prove that theWGAN-VGG-SSL is more stable than the other models on both GPU and CPU. 展开更多
关键词 Machine learning deep learning image denoising low dose ct loss function
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Research on Automatic Elimination of Laptop Computer in Security CT Images Based on Projection Algorithm and YOLOv7-Seg
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作者 Fei Wang Baosheng Liu +1 位作者 Yijun Tang Lei Zhao 《Journal of Computer and Communications》 2023年第9期1-17,共17页
In civil aviation security screening, laptops, with their intricate structural composition, provide the potential for criminals to conceal dangerous items. Presently, the security process necessitates passengers to in... In civil aviation security screening, laptops, with their intricate structural composition, provide the potential for criminals to conceal dangerous items. Presently, the security process necessitates passengers to individually present their laptops for inspection. The paper introduced a method for laptop removal. By combining projection algorithms with the YOLOv7-Seg model, a laptop’s three views were generated through projection, and instance segmentation of these views was achieved using YOLOv7-Seg. The resulting 2D masks from instance segmentation at different angles were employed to reconstruct a 3D mask through angle restoration. Ultimately, the intersection of this 3D mask with the original 3D data enabled the successful extraction of the laptop’s 3D information. Experimental results demonstrated that the fusion of projection and instance segmentation facilitated the automatic removal of laptops from CT data. Moreover, higher instance segmentation model accuracy leads to more precise removal outcomes. By implementing the laptop removal functionality, the civil aviation security screening process becomes more efficient and convenient. Passengers will no longer be required to individually handle their laptops, effectively enhancing the efficiency and accuracy of security screening. 展开更多
关键词 Instance Segmentation PROJEctION ct image 3D Segmentation Real-Time Detection
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A Robust Automated Framework for Classification of CT Covid-19 Images Using MSI-ResNet
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作者 Aghila Rajagopal Sultan Ahmad +3 位作者 Sudan Jha Ramachandran Alagarsamy Abdullah Alharbi Bader Alouffi 《Computer Systems Science & Engineering》 SCIE EI 2023年第6期3215-3229,共15页
Nowadays,the COVID-19 virus disease is spreading rampantly.There are some testing tools and kits available for diagnosing the virus,but it is in a lim-ited count.To diagnose the presence of disease from radiological i... Nowadays,the COVID-19 virus disease is spreading rampantly.There are some testing tools and kits available for diagnosing the virus,but it is in a lim-ited count.To diagnose the presence of disease from radiological images,auto-mated COVID-19 diagnosis techniques are needed.The enhancement of AI(Artificial Intelligence)has been focused in previous research,which uses X-ray images for detecting COVID-19.The most common symptoms of COVID-19 are fever,dry cough and sore throat.These symptoms may lead to an increase in the rigorous type of pneumonia with a severe barrier.Since medical imaging is not suggested recently in Canada for critical COVID-19 diagnosis,computer-aided systems are implemented for the early identification of COVID-19,which aids in noticing the disease progression and thus decreases the death rate.Here,a deep learning-based automated method for the extraction of features and classi-fication is enhanced for the detection of COVID-19 from the images of computer tomography(CT).The suggested method functions on the basis of three main pro-cesses:data preprocessing,the extraction of features and classification.This approach integrates the union of deep features with the help of Inception 14 and VGG-16 models.At last,a classifier of Multi-scale Improved ResNet(MSI-ResNet)is developed to detect and classify the CT images into unique labels of class.With the support of available open-source COVID-CT datasets that consists of 760 CT pictures,the investigational validation of the suggested method is estimated.The experimental results reveal that the proposed approach offers greater performance with high specificity,accuracy and sensitivity. 展开更多
关键词 Covid-19 ct images multi-scale improved ResNet AI inception 14 and VGG-16 models
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Image J测量CT扫描图像脂肪面积的应用 被引量:11
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作者 何华 孙曾梅 +3 位作者 唐蜀西 周燚 蒋灵均 邬云红 《广东医学》 CAS 北大核心 2017年第2期255-258,共4页
目的探索一种可靠简便、不依赖CT图像工作站软件测量CT扫描图像腹部脂肪面积(VFA)的方法。方法对8名健康受试者行CT腹部平扫,定义采用西门子CT扫描仪自带软件测量VFA为方法 A,西门子CT扫描后的图像采用Image J软件来测量为方法 B。以方... 目的探索一种可靠简便、不依赖CT图像工作站软件测量CT扫描图像腹部脂肪面积(VFA)的方法。方法对8名健康受试者行CT腹部平扫,定义采用西门子CT扫描仪自带软件测量VFA为方法 A,西门子CT扫描后的图像采用Image J软件来测量为方法 B。以方法 A为金标准,评价方法 B测定结果的准确性及稳定性。结果 (1)准确性:方法 A与方法 B测定的内脏VFA和腹部脂肪总面积均高度相关(r=0.965 8,95%CI:0.817 7~0.994,P<0.001;r=0.997 7,95%CI:0.986 9~0.999 6,P<0.001);Bland-Altman检验证实方法 A与方法 B的一致性良好。方法 A与方法 B测量VFA的变异系数为4.86%。(2)稳定性:采用方法 B测量,观察者组内相关系数ICCs=1.0(95%CI:0.998~1.0),观察者间组内相关系数为ICCs=0.999 8(95%CI:0.999 4~1)。结论采用Image J软件分析腹部及腹内VFA结果准确稳定,不受专业软件的局限。 展开更多
关键词 ct图像 腹内脂肪面积 image J软件
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CT-MRI同体位图像融合在高级别脑胶质瘤放射治疗靶区勾画中应用 被引量:1
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作者 金龙 杨振 +2 位作者 张鑫 刘晓斌 缪星宇 《生物医学工程与临床》 CAS 2024年第1期35-41,共7页
目的探讨同体位MRI图像(MRIsim)与CT模拟定位图像融合在高级别脑胶质瘤放射治疗靶区勾画中的临床应用价值。方法选择20例脑胶质瘤术后放射治疗患者,其中男性13例,女性7例;年龄39~69岁,平均年龄45.5岁;全部为单发病变;均已行全切或不全... 目的探讨同体位MRI图像(MRIsim)与CT模拟定位图像融合在高级别脑胶质瘤放射治疗靶区勾画中的临床应用价值。方法选择20例脑胶质瘤术后放射治疗患者,其中男性13例,女性7例;年龄39~69岁,平均年龄45.5岁;全部为单发病变;均已行全切或不全切手术,全部经病理组织诊断证实;世界卫生组织(WHO)分级Ⅲ级6例,Ⅳ级14例。将每例患者的同体位MRIsim、常规MRI影像(MRIconv)分别与CT模拟定位图像融合。运用Dice相似指数(DSC)和豪斯多夫距离(HD)算法来评价配准的精确度。在CT与MRIsim融合图像(Fusion-CT MRIsim)、CT与MRIconv融合图像(Fusion-CT MRIconv)上分别勾画危及器官(OAR)及靶区[大体肿瘤靶区(GTV)、临床肿瘤靶区(CTV)]。评估两种融合图像(即Fusion-CT MRIsim组和Fusion-CT MRIconv组)OAR勾画体积、GTV、CTV及剂量学差异。结果融合精确度评估:除全脑外,Fusion-CT MRIsim组其余OAR DSC均高于Fusion-CT MRIconv组(P<0.05);Fusion-CT MRIsim组OAR HD小于Fusion-CT MRIconv组(P<0.05)。OAR勾画体积比较:Fusion-CT MRIsim组OAR勾画体积与Fusion-CT MRIconv比较,差异无统计学意义(P>0.05)。靶区:Fusion-CT MRIsim组GTV、CTV小于Fusion-CT MRIconv组[(118.2±8.0)cm^(3)vs(125.3±8.1)cm^(3)、(234.3±12.8)cm^(3)vs(256.0±13.4)cm^(3)],差异有显著统计学意义(均P=0.000)。剂量学比较:Fusion-CT MRIsim组D_(max)-PTV、D_(mean)-PTV与Fusion-CT MRIconv组[(6432.9±23.0)cGy vs(6430.4±25.2)cGy、(6159.0±13.7)cGy vs(6166.2±17.3)cGy]比较,差异无统计学意义(P>0.05)。结论CT-MRI同体位融合图像配准精确度高,可降低全脑平均剂量(D_(mean))及缩小GTV及CTV,是高级别脑胶质瘤术后精确放射治疗值得广泛应用的临床方法。 展开更多
关键词 ct-MRI同体位融合 高级别胶质瘤 放射治疗靶区 靶区勾画
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颅脑CT灌注成像及磁共振成像在脑梗死患者中的应用 被引量:3
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作者 荆梅 顾欣欣 《中国实用神经疾病杂志》 2024年第1期43-47,共5页
目的分析颅脑CT灌注成像及磁共振成像对脑梗死患者的诊断价值。方法选取2022-03—2023-05在江苏省中西医结合医院就诊的80例疑似脑梗死患者为研究对象,对比分析GE Revolution CT颅脑灌注成像、磁共振成像及联合检查的敏感性、特异性、... 目的分析颅脑CT灌注成像及磁共振成像对脑梗死患者的诊断价值。方法选取2022-03—2023-05在江苏省中西医结合医院就诊的80例疑似脑梗死患者为研究对象,对比分析GE Revolution CT颅脑灌注成像、磁共振成像及联合检查的敏感性、特异性、准确性,制作3种影像学检查的受试者工作特征(ROC)曲线。结果根据患者病情和临床综合诊断确诊,80例疑似脑梗死患者中脑梗死阳性69例(86.25%),脑梗死阴性11例(13.75%)。3种影像学检查方法的灵敏度、准确率比较,从高到低依次为联合检查、GE Revolution CT颅脑灌注成像检查、磁共振成像检查,差异有统计学意义(P<0.05)。GE Revolution CT颅脑灌注成像检查、磁共振成像检查、联合检查诊断脑梗死的ROC曲线下面积(AUC)分别为0.8109、0.7688、0.8682。结论GE Revolution CT颅脑灌注成像与磁共振成像检查的联合应用,有利于提高脑梗死患者诊断的灵敏度、准确率及AUC水平。 展开更多
关键词 脑梗死 GE Revolution ct 颅脑灌注成像 磁共振成像 灵敏度 准确率
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基于投影误差优化网络的碳/碳材料CT稀疏角度重建方法
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作者 金珂 陈博 +4 位作者 金虎 曾天辰 周星明 徐林 孙跃文 《同位素》 CAS 2024年第4期332-340,共9页
在采用^(60)Co作为射线源的碳/碳复合材料的计算机断层扫描(CT)中,降低采样角度数量可以显著缩短检测时间,提升检测效率。然而常规的解析重建算法,稀疏角度的重建图像中包含大量的噪声和伪影,干扰图像中缺陷的检出,影响检测系统在快速... 在采用^(60)Co作为射线源的碳/碳复合材料的计算机断层扫描(CT)中,降低采样角度数量可以显著缩短检测时间,提升检测效率。然而常规的解析重建算法,稀疏角度的重建图像中包含大量的噪声和伪影,干扰图像中缺陷的检出,影响检测系统在快速检测条件下对被检构件的质量评价。本研究提出了一种基于投影误差优化神经网络的稀疏角度CT图像重建方法,采用未训练的编码-解码卷积神经网络优化重建图像的投影误差,结合图像的总变分先验,采用自适应动量估计(ADAM)算法进行优化。与传统的深度学习重建算法相比,该方法无需训练样本集,具备更强的泛化能力和鲁棒性。CT检测实验结果表明,该方法相比于传统的解析和重建算法,重建图像质量大幅提升,在保留被检测构件细节信息的同时,显著抑制了重建图像中的伪影与噪声。 展开更多
关键词 碳/碳复合材料 ^(60)Co ct检测 深度学习 稀疏角度重建
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^(99)Tc^(m)-MIBI SPECT/CT显像对诊断甲状旁腺功能亢进症的增益价值
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作者 韩江琴 戴娜 +2 位作者 章斌 董佳佳 刘航 《标记免疫分析与临床》 CAS 2024年第7期1236-1241,共6页
目的拟探讨^(99)Tc^(m)-MIBI(甲氧基异丁基异腈)SPECT/CT(单光子发射计算机断层成像/计算机断层扫描)对甲状旁腺功能亢进症的诊断效能及其相关因素,以及MIBI断层融合显像的增益价值。方法入组标准:(1)回顾性分析2017年5月至2023年6月间... 目的拟探讨^(99)Tc^(m)-MIBI(甲氧基异丁基异腈)SPECT/CT(单光子发射计算机断层成像/计算机断层扫描)对甲状旁腺功能亢进症的诊断效能及其相关因素,以及MIBI断层融合显像的增益价值。方法入组标准:(1)回顾性分析2017年5月至2023年6月间临床怀疑甲状旁腺功能亢进症的患者;(2)在本院行甲状旁腺切除术;(3)在术前2周内行^(99)Tc^(m)-MIBI SPECT/CT甲状旁腺显像。由2位核医学科高年资主治医师共同阅片,观察MIBI平面及断层图像,分析记录病变特点及同机CT显示的骨质改变。结果符合筛选条件并最终入组的患者共100例,共切除165个病灶,包括甲旁亢病灶112枚。MIBI平面显像与断层融合显像诊断甲旁亢病灶的灵敏度、特异性、约登指数、阳性预测值和阴性预测值分别为66.07%、87.03%、53.10%、91.36%和55.29%和85.71%、87.03%、72.74%、93.20%和74.60%。MIBI平面显像阳性组患者的血清PTH(甲状旁腺激素)水平、血清ALP(碱性磷酸酶)水平明显高于阴性组。MIBI断层融合显像显示出96个真阳性病灶中,其中76个病灶在CT表现为密度均匀的类圆形或椭圆形结节,17个病灶伴囊性改变,3个病灶伴钙化。另外,MIBI断层融合显像发现了8枚异位甲状旁腺腺瘤。MIBI平面显像阳性组的甲旁亢病灶的长径、PPD(垂直直径的乘积)均大于显像阴性组;MIBI平面显像阳性组较阴性组更易出现颅骨皮质变薄及颅骨增厚。相关性分析结果显示血清ALP水平与颅骨CT值、颅骨皮质变薄、颅骨增厚及有无骨质吸收灶之间均存在相关性。结论MIBI显像对甲旁亢的诊断具有重要作用,SPECT/CT断层融合显像提高了传统平面显像的诊断准确率,同时有助于对甲旁亢病灶提供准确定位。同机CT显示的骨骼特点为甲旁亢的临床诊疗提供了更多的信息。 展开更多
关键词 MIBI显像 SPEct/ct断层融合显像 甲旁亢 增益价值
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Investigation of prior image constrained compressed sensing-based spectral X-ray CT image reconstruction
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作者 周正东 余子丽 +1 位作者 张雯雯 管绍林 《Journal of Southeast University(English Edition)》 EI CAS 2016年第4期420-425,共6页
To improve spectral X-ray CT reconstructed image quality, the energy-weighted reconstructed image xbins^W and the separable paraboloidal surrogates(SPS) algorithm are proposed for the prior image constrained compres... To improve spectral X-ray CT reconstructed image quality, the energy-weighted reconstructed image xbins^W and the separable paraboloidal surrogates(SPS) algorithm are proposed for the prior image constrained compressed sensing(PICCS)-based spectral X-ray CT image reconstruction. The PICCS-based image reconstruction takes advantage of the compressed sensing theory, a prior image and an optimization algorithm to improve the image quality of CT reconstructions.To evaluate the performance of the proposed method, three optimization algorithms and three prior images are employed and compared in terms of reconstruction accuracy and noise characteristics of the reconstructed images in each energy bin.The experimental simulation results show that the image xbins^W is the best as the prior image in general with respect to the three optimization algorithms; and the SPS algorithm offers the best performance for the simulated phantom with respect to the three prior images. Compared with filtered back-projection(FBP), the PICCS via the SPS algorithm and xbins^W as the prior image can offer the noise reduction in the reconstructed images up to 80. 46%, 82. 51%, 88. 08% in each energy bin,respectively. M eanwhile, the root-mean-squared error in each energy bin is decreased by 15. 02%, 18. 15%, 34. 11% and the correlation coefficient is increased by 9. 98%, 11. 38%,15. 94%, respectively. 展开更多
关键词 spectral X-ray ct prior image compressed sensing optimization algorithm image reconstruction
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双能量CT非线性融合和噪声优化的虚拟单能量图像技术在喉鳞状细胞癌中的应用
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作者 何长久 刘杰克 +4 位作者 青浩渺 郭玲 胡仕北 周鹏 何乐民 《重庆医科大学学报》 CAS CSCD 北大核心 2024年第2期198-202,共5页
目的:探讨双能量CT线性融合图像(linear blending imaging,LBI)、非线性融合图像(nonlinear blending image,NBI)和噪声优化的虚拟单能量图像(noise-optimized virtual monoenergetic image,VMI+)技术在喉鳞状细胞癌中的应用价值。方法... 目的:探讨双能量CT线性融合图像(linear blending imaging,LBI)、非线性融合图像(nonlinear blending image,NBI)和噪声优化的虚拟单能量图像(noise-optimized virtual monoenergetic image,VMI+)技术在喉鳞状细胞癌中的应用价值。方法:回顾性分析2019年6月至2022年3月61例经病理证实为喉鳞状细胞癌患者的双能量CT资料。双能量图像采用LBI[融合系数为1(80 kV)和0.6(M0.6)]、NBI和VMI+(40 keV、55 keV)技术重建。比较5组图像的客观图像质量[对比噪声比(contrast-tonoise ratio,CNR)、肿瘤CT值、噪声]和主观图像质量(肿瘤边界评分和整体图像质量评分)。结果:40 keV的CNR、肿瘤CT值和肿瘤边界评分均明显高于80 kV、M0.6、NBI和55 keV,差异均有统计学意义(均P<0.05)。NBI的整体图像质量评分明显高于80 kV、M0.6、40 keV和55 keV,差异均有统计学意义(均P<0.05)。NBI的噪声明显低于80 kV、40 keV和55 keV,差异均有统计学意义(均P<0.05)。结论:在喉鳞状细胞癌的双能量CT中,采用VMI+技术(40 keV)能提供更好的CNR、肿瘤CT值和肿瘤边界,采用NBI技术能提供更低的噪声和更好的整体图像质量。 展开更多
关键词 双能量ct 喉鳞状细胞癌 非线性融合图像 噪声优化的虚拟单能量图像
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CTA/CTP评估在缺血性脑血管病介入治疗中的应用
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作者 周新华 陈良义 张丹彤 《中国CT和MRI杂志》 2024年第4期20-22,共3页
目的探讨CT血管成像(CTA)/CT灌注成像(CTP)在缺血性脑血管病介入治疗中的应用价值。方法选取2021年1月至2023年9月我院收治的200例缺血性脑血管病患者,均在入院后接受CTA及CTP检查,分析其影像资料,探究CTA及CTP缺血性脑血管病介入治疗... 目的探讨CT血管成像(CTA)/CT灌注成像(CTP)在缺血性脑血管病介入治疗中的应用价值。方法选取2021年1月至2023年9月我院收治的200例缺血性脑血管病患者,均在入院后接受CTA及CTP检查,分析其影像资料,探究CTA及CTP缺血性脑血管病介入治疗中的应用价值。结果脑血容量(CBV)比较:缺血半暗带>健侧>梗死区(P<0.05);脑血流量(CBF)比较:健侧>缺血半暗带>梗死区(P<0.05);平均通过时间(MTT)、目标组织中浓度达峰时间(TTP)、目标组织中所有残余功能全部达峰时间(Tmax)比较:健侧<缺血半暗带<梗死区(P<0.05)。CTA检出左侧、右侧大脑中动脉(MCA)闭塞或狭窄分别59例(29.50%)、91例(45.50%),左侧、右侧颈内动脉(ICA)闭塞分别16例(8.00%)、12例(6.00%),双侧ICA狭窄为6例(3.00%);代偿分支血管显影基本满意129例(64.50%),显影不足71例(35.50),其余16例(8.00%)患者CTA影像资料显示无异常,敏感度为92.00%。预后不良组患侧CBV、CBF小于预后良好组,MTT、TTP、Tmax长于预后良好组,代偿血管建立比例低于预后良好组(P<0.05)。采用受试者工作特征曲线分析显示,CBV、CBF、MTT、TTP、Tmax对介入治疗预后均有一定的预测效能(P<0.05),其曲线下面积分别为0.839、0.815、0.673、0.713、0.710,其中CBV预测效能最高,敏感性为83.20%,特异性为73.33%。结论CTA/CTP可反映大脑、颈内动脉狭窄或闭塞、代偿分支建立情况,也可反映血流灌注情况,在介入治疗合理时机的判断方面可提供准确依据,提高患者预后。 展开更多
关键词 ct血管成像 ct灌注成像 缺血性脑血管病 介入治疗 指导 预后
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一种新的术中X线与术前CT图像配准方法
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作者 崔家礼 王杰 +2 位作者 郭曦 陈彧 舒丽霞 《北京生物医学工程》 2024年第2期151-157,186,共8页
目的本研究旨在配准胸主动脉血管内修复术(thoracic endovascular aortic repair,TEVAR)术中X线与术前CT图像,为TEVAR支架植入提供精确安全的导航。然而,现有配准算法存在无法有效弥合投影CT图像生成的数字重建影像(digitally reconstru... 目的本研究旨在配准胸主动脉血管内修复术(thoracic endovascular aortic repair,TEVAR)术中X线与术前CT图像,为TEVAR支架植入提供精确安全的导航。然而,现有配准算法存在无法有效弥合投影CT图像生成的数字重建影像(digitally reconstructed radiography,DRR)与X线图像之间的域间差异和难以获得图像分割标签的问题。因此,需要提出新的方法来改善这一问题。方法本文提出了一种新的配准框架,该框架结合了基于生成对抗网络(generative adversarial network,GAN)的域自适应网络和基于Transformer的配准网络。基于GAN的域自适应网络将X线图像的风格迁移到DRR图像上,使两者在图像风格上更接近。基于Transformer的配准网络采用CNN与跨模态变换器(cross-modality transformer,CMT)相结合的模式,直接配准X线与CT图像,无需进行图像分割。结果本文在208对标定的TEVAR术中X线与CT图像对上对新的配准方法进行了验证。与其他域适应方法相比,本文所采用的CycleGAN网络作为风格转换模块,有效减小了DRR图像与X线图像之间的域间差异。消融实验结果进一步证实,配准网络中的全局局部感知模块(global-local perception module,GLPM)对提高配准精度具有明显作用,而空间缩减(spatial reduction,SR)则有效缩短了配准时间。通过对比现有方法和本文方法在真实患者X线与CT图像对上的配准效果,本文的方法在配准精度和成功率方面均表现出最佳性能。结论本文提出的新的X线与CT图像配准方法有效克服了现有方法存在的域间差异以及难以获得分割标签的问题。 展开更多
关键词 X线图像 ct图像 配准 域自适应 跨模态变换器
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面向肺炎CT图像识别的DL-CTNet模型
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作者 王威 黄文迪 +1 位作者 王新 王珑润 《计算机辅助设计与图形学学报》 EI CSCD 北大核心 2024年第1期122-132,共11页
肺炎常缺乏明显呼吸系症状,症状多不典型,易发生漏诊、错诊.利用深度学习技术辅助医务人员安全、高效地检测感染者是一种有效途径.针对COVID-19感染者CT图像的磨玻璃影、铺路石征、血管扩张等特点,提出一种可有效地提取CT图像中的局部... 肺炎常缺乏明显呼吸系症状,症状多不典型,易发生漏诊、错诊.利用深度学习技术辅助医务人员安全、高效地检测感染者是一种有效途径.针对COVID-19感染者CT图像的磨玻璃影、铺路石征、血管扩张等特点,提出一种可有效地提取CT图像中的局部与全局特征的轻量级模型——DL-CTNet.输入预处理的CT图像后,首先采用空洞卷积和动态双路径多尺度特征融合(D-DMFF)模块的2个支路提取浅层特征;然后使用局部与全局特征拼接模块(LGFC)中的D-DMFF模块提取局部特征、Swin Transformer提取全局特征,并通过拼接获得深层特征;最后经过全连接层输出分类标签.实验结果表明,在2个CT图像数据集上,验证了LGFC模块以及DL-CTNet的低复杂度与有效性;DL-CTNet的分类准确率高达98.613%,与其他方法相比,其能更准确地识别肺炎的CT图像. 展开更多
关键词 肺炎 胸部ct图像 卷积神经网络 TRANSFORMER
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南海神狐海域天然气水合物微观赋存特征的超分辨率CT图像识别
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作者 李承峰 叶旺全 +7 位作者 陈亮 桂斌 郝锡荦 孙建业 张永超 刘乐乐 陈强 郑荣儿 《海洋地质与第四纪地质》 CAS CSCD 北大核心 2024年第3期149-159,共11页
南海神狐海域是我国天然气水合物资源勘探开发的主要目标区之一,2017和2020年先后两次现场试验性开采证实了水合物资源的利用前景。目前,对该地区含水合物储层的精细评价还有待进一步提升,水合物在沉积物孔隙空间中的微观赋存形态是其... 南海神狐海域是我国天然气水合物资源勘探开发的主要目标区之一,2017和2020年先后两次现场试验性开采证实了水合物资源的利用前景。目前,对该地区含水合物储层的精细评价还有待进一步提升,水合物在沉积物孔隙空间中的微观赋存形态是其中的重要影响因素。针对水合物微观赋存形态CT图像表征存在的分辨率不足的问题,建立了一种基于自监督学习的数字图像超分辨率重建算法,实现了CT扫描图像空间分辨率的2倍和4倍提升。在此基础上,对南海神狐海域含水合物沉积物孔隙结构演化规律和水合物微观赋存特征进行了形态表征。由于南海沉积物中存在大量有孔虫壳体,水合物主要占据有孔虫壳体内部空间并堵塞了空隙间的连通喉道,显著降低了沉积物的气、水渗透能力;然而,水合物未能全部占据整个孔隙空间,仍然会有少量的气体和水残留,气体则主要分布于水合物颗粒内部,而水则主要分布在水合物颗粒表面,上述实验结果对地震、测井等现场勘探数据解释具有一定的指导意义。 展开更多
关键词 天然气水合物储层 微观赋存特征 超分辨率重建 ct图像
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