Operando monitoring of internal and local electrochemical processes within lithium-ion batteries(LIBs)is crucial,necessitating a range of non-invasive,real-time imaging characterization techniques including nuclear ma...Operando monitoring of internal and local electrochemical processes within lithium-ion batteries(LIBs)is crucial,necessitating a range of non-invasive,real-time imaging characterization techniques including nuclear magnetic resonance(NMR)techniques.This review provides a comprehensive overview of the recent applications and advancements of non-invasive magnetic resonance imaging(MRI)techniques in LIBs.It initially introduces the principles and hardware of MRI,followed by a detailed summary and comparison of MRI techniques used for characterizing liquid/solid electrolytes,electrodes and commercial batteries.This encompasses the determination of electrolytes'transport properties,acquisition of ion distribution profile,and diagnosis of battery defects.By focusing on experimental parameters and optimization strategies,our goal is to explore MRI methods suitable to a variety of research subjects,aiming to enhance imaging quality across diverse scenarios and offer critical physical/chemical insights into the ongoing operation processes of LIBs.展开更多
BACKGROUND Due to frequent and high-risk sports activities,the elbow joint is susceptible to injury,especially to cartilage tissue,which can cause pain,limited movement and even loss of joint function.AIM To evaluate ...BACKGROUND Due to frequent and high-risk sports activities,the elbow joint is susceptible to injury,especially to cartilage tissue,which can cause pain,limited movement and even loss of joint function.AIM To evaluate magnetic resonance imaging(MRI)multisequence imaging for improving the diagnostic accuracy of adult elbow cartilage injury.METHODS A total of 60 patients diagnosed with elbow cartilage injury in our hospital from January 2020 to December 2021 were enrolled in this retrospective study.We analyzed the accuracy of conventional MRI sequences(T1-weighted imaging,T2-weighted imaging,proton density weighted imaging,and T2 star weighted image)and Three-Dimensional Coronary Imaging by Spiral Scanning(3D-CISS)in the diagnosis of elbow cartilage injury.Arthroscopy was used as the gold standard to evaluate the diagnostic effect of single and combination sequences in different injury degrees and the consistency with arthroscopy.RESULTS The diagnostic accuracy of 3D-CISS sequence was 89.34%±4.98%,the sensitivity was 90%,and the specificity was 88.33%,which showed the best performance among all sequences(P<0.05).The combined application of the whole sequence had the highest accuracy in all sequence combinations,the accuracy of mild injury was 91.30%,the accuracy of moderate injury was 96.15%,and the accuracy of severe injury was 93.33%(P<0.05).Compared with arthroscopy,the combination of all MRI sequences had the highest consistency of 91.67%,and the kappa value reached 0.890(P<0.001).CONCLUSION Combination of 3D-CISS and each sequence had significant advantages in improving MRI diagnostic accuracy of elbow cartilage injuries in adults.Multisequence MRI is recommended to ensure the best diagnosis and treatment.展开更多
This study addresses challenges in fetal magnetic resonance imaging (MRI) related to motion artifacts, maternal respiration, and hardware limitations. To enhance MRI quality, we employ deep learning techniques, specif...This study addresses challenges in fetal magnetic resonance imaging (MRI) related to motion artifacts, maternal respiration, and hardware limitations. To enhance MRI quality, we employ deep learning techniques, specifically utilizing Cycle GAN. Synthetic pairs of images, simulating artifacts in fetal MRI, are generated to train the model. Our primary contribution is the use of Cycle GAN for fetal MRI restoration, augmented by artificially corrupted data. We compare three approaches (supervised Cycle GAN, Pix2Pix, and Mobile Unet) for artifact removal. Experimental results demonstrate that the proposed supervised Cycle GAN effectively removes artifacts while preserving image details, as validated through Structural Similarity Index Measure (SSIM) and normalized Mean Absolute Error (MAE). The method proves comparable to alternatives but avoids the generation of spurious regions, which is crucial for medical accuracy.展开更多
In the medical profession,recent technological advancements play an essential role in the early detection and categorization of many diseases that cause mortality.The technique rising on daily basis for detecting illn...In the medical profession,recent technological advancements play an essential role in the early detection and categorization of many diseases that cause mortality.The technique rising on daily basis for detecting illness in magnetic resonance through pictures is the inspection of humans.Automatic(computerized)illness detection in medical imaging has found you the emergent region in several medical diagnostic applications.Various diseases that cause death need to be identified through such techniques and technologies to overcome the mortality ratio.The brain tumor is one of the most common causes of death.Researchers have already proposed various models for the classification and detection of tumors,each with its strengths and weaknesses,but there is still a need to improve the classification process with improved efficiency.However,in this study,we give an in-depth analysis of six distinct machine learning(ML)algorithms,including Random Forest(RF),Naïve Bayes(NB),Neural Networks(NN),CN2 Rule Induction(CN2),Support Vector Machine(SVM),and Decision Tree(Tree),to address this gap in improving accuracy.On the Kaggle dataset,these strategies are tested using classification accuracy,the area under the Receiver Operating Characteristic(ROC)curve,precision,recall,and F1 Score(F1).The training and testing process is strengthened by using a 10-fold cross-validation technique.The results show that SVM outperforms other algorithms,with 95.3%accuracy.展开更多
目的分析基于多模态磁共振成像(Magnetic Resonance Imaging,MRI)影像组学鉴别胶质瘤及单发脑转移瘤的研究进展,得出提升鉴别准确性的要素。方法通过检索PubMed、Web of Science及FMRS外文医学信息资源检索平台3个数据库,根据纳入排除标...目的分析基于多模态磁共振成像(Magnetic Resonance Imaging,MRI)影像组学鉴别胶质瘤及单发脑转移瘤的研究进展,得出提升鉴别准确性的要素。方法通过检索PubMed、Web of Science及FMRS外文医学信息资源检索平台3个数据库,根据纳入排除标准,对纳入的文章提取数据来源、患者数量、MRI设备、MRI序列、肿瘤分割软件、分割方式、分割范围、分割类型、特征提取方法、筛选方法、机器学习分类器、最优的机器学习分类器等数据进行综合分析。结果最终纳入12篇文献进行分析,大多数研究选择MRI传统结构序列,特征筛选方法选择最多的是最小绝对收缩和选择算子,使用最多且表现最佳的机器学习分类器为随机森林。结论MRI影像组学方法在鉴别胶质瘤及单发脑转移瘤方面展现出了较高的准确性,为临床决策提高了较大帮助。展开更多
Objective To measure the intraobserver concordance of an experienced genitourinary radiologist reporting of multiparametric magnetic resonance imaging of the prostate(mpMRIp)scans over time.Methods An experienced geni...Objective To measure the intraobserver concordance of an experienced genitourinary radiologist reporting of multiparametric magnetic resonance imaging of the prostate(mpMRIp)scans over time.Methods An experienced genitourinary radiologist re-reported his original 100 consecutive mpMRIp scans using Prostate Imaging-Reporting and Data System version 2(PI-RADS v2)after 5 years of further experience comprising>1000 scans.Intraobserver agreement was measured using Cohen's kappa.Sensitivity,specificity,negative predictive value(NPV),positive predictive value(PPV),and accuracy were calculated,and comparison of sensitivity was performed using McNemar's test.Results Ninety-six mpMRIp scans were included in our final analysis.Of the 96 patients,53(55.2%)patients underwent subsequent biopsy(n=43)or prostatectomy(n=15),with 73 lesions targeted.Moderate agreement(Cohen's kappa 0.55)was seen in the number of lesions identified at initial reporting and on re-reading(81 vs.39 total lesions;and 71 vs.37 number of PI-RADS≥3 lesions).For clinically significant prostate cancer,re-reading demonstrated an increase in specificity(from 43%to 89%)and PPV(from 62%to 87%),but a decrease in sensitivity(from 94%to 72%,p=0.01)and NPV(from 89%to 77%).Conclusion The intraobserver agreement for a novice to experienced radiologist reporting mpMRIp using PI-RADS v2 is moderate.Reduced sensitivity is off-set by improved specificity and PPV,which validate mpMRIp as a gold standard for prebiopsy screening.展开更多
基金supported by the National Key R&D Program of China,Grant No.2021YFB2401800。
文摘Operando monitoring of internal and local electrochemical processes within lithium-ion batteries(LIBs)is crucial,necessitating a range of non-invasive,real-time imaging characterization techniques including nuclear magnetic resonance(NMR)techniques.This review provides a comprehensive overview of the recent applications and advancements of non-invasive magnetic resonance imaging(MRI)techniques in LIBs.It initially introduces the principles and hardware of MRI,followed by a detailed summary and comparison of MRI techniques used for characterizing liquid/solid electrolytes,electrodes and commercial batteries.This encompasses the determination of electrolytes'transport properties,acquisition of ion distribution profile,and diagnosis of battery defects.By focusing on experimental parameters and optimization strategies,our goal is to explore MRI methods suitable to a variety of research subjects,aiming to enhance imaging quality across diverse scenarios and offer critical physical/chemical insights into the ongoing operation processes of LIBs.
文摘BACKGROUND Due to frequent and high-risk sports activities,the elbow joint is susceptible to injury,especially to cartilage tissue,which can cause pain,limited movement and even loss of joint function.AIM To evaluate magnetic resonance imaging(MRI)multisequence imaging for improving the diagnostic accuracy of adult elbow cartilage injury.METHODS A total of 60 patients diagnosed with elbow cartilage injury in our hospital from January 2020 to December 2021 were enrolled in this retrospective study.We analyzed the accuracy of conventional MRI sequences(T1-weighted imaging,T2-weighted imaging,proton density weighted imaging,and T2 star weighted image)and Three-Dimensional Coronary Imaging by Spiral Scanning(3D-CISS)in the diagnosis of elbow cartilage injury.Arthroscopy was used as the gold standard to evaluate the diagnostic effect of single and combination sequences in different injury degrees and the consistency with arthroscopy.RESULTS The diagnostic accuracy of 3D-CISS sequence was 89.34%±4.98%,the sensitivity was 90%,and the specificity was 88.33%,which showed the best performance among all sequences(P<0.05).The combined application of the whole sequence had the highest accuracy in all sequence combinations,the accuracy of mild injury was 91.30%,the accuracy of moderate injury was 96.15%,and the accuracy of severe injury was 93.33%(P<0.05).Compared with arthroscopy,the combination of all MRI sequences had the highest consistency of 91.67%,and the kappa value reached 0.890(P<0.001).CONCLUSION Combination of 3D-CISS and each sequence had significant advantages in improving MRI diagnostic accuracy of elbow cartilage injuries in adults.Multisequence MRI is recommended to ensure the best diagnosis and treatment.
文摘This study addresses challenges in fetal magnetic resonance imaging (MRI) related to motion artifacts, maternal respiration, and hardware limitations. To enhance MRI quality, we employ deep learning techniques, specifically utilizing Cycle GAN. Synthetic pairs of images, simulating artifacts in fetal MRI, are generated to train the model. Our primary contribution is the use of Cycle GAN for fetal MRI restoration, augmented by artificially corrupted data. We compare three approaches (supervised Cycle GAN, Pix2Pix, and Mobile Unet) for artifact removal. Experimental results demonstrate that the proposed supervised Cycle GAN effectively removes artifacts while preserving image details, as validated through Structural Similarity Index Measure (SSIM) and normalized Mean Absolute Error (MAE). The method proves comparable to alternatives but avoids the generation of spurious regions, which is crucial for medical accuracy.
基金support of the Deputy for Research and Innovation-Ministry of Education,Kingdom of Saudi Arabia for this research through a grant(NU/IFC/ENT/01/014)under the institutional Funding Committee at Najran University,Kingdom of Saudi Arabia.
文摘In the medical profession,recent technological advancements play an essential role in the early detection and categorization of many diseases that cause mortality.The technique rising on daily basis for detecting illness in magnetic resonance through pictures is the inspection of humans.Automatic(computerized)illness detection in medical imaging has found you the emergent region in several medical diagnostic applications.Various diseases that cause death need to be identified through such techniques and technologies to overcome the mortality ratio.The brain tumor is one of the most common causes of death.Researchers have already proposed various models for the classification and detection of tumors,each with its strengths and weaknesses,but there is still a need to improve the classification process with improved efficiency.However,in this study,we give an in-depth analysis of six distinct machine learning(ML)algorithms,including Random Forest(RF),Naïve Bayes(NB),Neural Networks(NN),CN2 Rule Induction(CN2),Support Vector Machine(SVM),and Decision Tree(Tree),to address this gap in improving accuracy.On the Kaggle dataset,these strategies are tested using classification accuracy,the area under the Receiver Operating Characteristic(ROC)curve,precision,recall,and F1 Score(F1).The training and testing process is strengthened by using a 10-fold cross-validation technique.The results show that SVM outperforms other algorithms,with 95.3%accuracy.
文摘目的分析基于多模态磁共振成像(Magnetic Resonance Imaging,MRI)影像组学鉴别胶质瘤及单发脑转移瘤的研究进展,得出提升鉴别准确性的要素。方法通过检索PubMed、Web of Science及FMRS外文医学信息资源检索平台3个数据库,根据纳入排除标准,对纳入的文章提取数据来源、患者数量、MRI设备、MRI序列、肿瘤分割软件、分割方式、分割范围、分割类型、特征提取方法、筛选方法、机器学习分类器、最优的机器学习分类器等数据进行综合分析。结果最终纳入12篇文献进行分析,大多数研究选择MRI传统结构序列,特征筛选方法选择最多的是最小绝对收缩和选择算子,使用最多且表现最佳的机器学习分类器为随机森林。结论MRI影像组学方法在鉴别胶质瘤及单发脑转移瘤方面展现出了较高的准确性,为临床决策提高了较大帮助。
基金This research has been kindly supported by a grant from the St Vincent's Research Endowment Fund(approval number 55.2014).
文摘Objective To measure the intraobserver concordance of an experienced genitourinary radiologist reporting of multiparametric magnetic resonance imaging of the prostate(mpMRIp)scans over time.Methods An experienced genitourinary radiologist re-reported his original 100 consecutive mpMRIp scans using Prostate Imaging-Reporting and Data System version 2(PI-RADS v2)after 5 years of further experience comprising>1000 scans.Intraobserver agreement was measured using Cohen's kappa.Sensitivity,specificity,negative predictive value(NPV),positive predictive value(PPV),and accuracy were calculated,and comparison of sensitivity was performed using McNemar's test.Results Ninety-six mpMRIp scans were included in our final analysis.Of the 96 patients,53(55.2%)patients underwent subsequent biopsy(n=43)or prostatectomy(n=15),with 73 lesions targeted.Moderate agreement(Cohen's kappa 0.55)was seen in the number of lesions identified at initial reporting and on re-reading(81 vs.39 total lesions;and 71 vs.37 number of PI-RADS≥3 lesions).For clinically significant prostate cancer,re-reading demonstrated an increase in specificity(from 43%to 89%)and PPV(from 62%to 87%),but a decrease in sensitivity(from 94%to 72%,p=0.01)and NPV(from 89%to 77%).Conclusion The intraobserver agreement for a novice to experienced radiologist reporting mpMRIp using PI-RADS v2 is moderate.Reduced sensitivity is off-set by improved specificity and PPV,which validate mpMRIp as a gold standard for prebiopsy screening.
