目的初探纵向弛豫时间定量成像(T1 mapping)和酰胺质子转移加权(amide proton transfer weighted,APTw)成像鉴别慢性肾病(chronic kidney disease,CKD)患者与健康人群的价值。材料与方法回顾性分析2019年8月至2020年10月行3.0 T MRI检查...目的初探纵向弛豫时间定量成像(T1 mapping)和酰胺质子转移加权(amide proton transfer weighted,APTw)成像鉴别慢性肾病(chronic kidney disease,CKD)患者与健康人群的价值。材料与方法回顾性分析2019年8月至2020年10月行3.0 T MRI检查的CKD患者病例资料共21例(女6例,男15例),所有患者均经大连医科大学附属第一医院肾内科医师依据CKD临床实践指南确诊;同时收集24例健康志愿者临床资料作为对照组。将所有原始图像导入ISP工作站,生成伪彩图。由两名影像科诊断医师采用双盲法分别从肾的上极、中部、下极各选择一个层面并于皮质和髓质中分别放置感兴趣区(region of interest,ROI),面积约10~20 mm^(2),避开肾窦、大血管及肾周组织。测量所得皮髓质T1值与APT值应用SPSS 26.0软件进行统计学分析:应用组内相关系数(intra-class correlation coefficients,ICC)进行观察者间测量结果一致性检验;根据数据正态分布情况,采用独立样本t检验或Mann-Whitney U检验分析两组间参数值差异,P<0.05为差异具有统计学意义;采用受试者工作特征(receiver operating characteristic,ROC)曲线分析各参数诊断效能,根据最大约登指数得到相对应的阈值、敏感度和特异度,并计算曲线下面积(area under the curve,AUC)值。结果两位观察者间测量结果一致性良好(ICC>0.75)。CKD组双肾皮质T1值和皮质APT值显著高于健康对照组(P<0.05);左肾皮质T1值鉴别CKD的AUC值为0.887,敏感度66.7%,特异度100.0%;左肾皮质APT值鉴别CKD的AUC值为0.966,敏感度95.2%,特异度95.8%;右肾皮质T1值鉴别CKD的AUC值为0.960,敏感度76.2%,特异度100.0%;右肾皮质APT值鉴别CKD的AUC值为0.921,敏感度85.7%,特异度91.7%。结论T1 mapping与APTw成像可无创有效鉴别CKD,基于二者的定量参数在一定程度上反映了单侧肾脏各自结构与功能的改变,有望为临床疾病诊断提供一定的参考价值。展开更多
Upland crop-rice cropping systems(UCR)facilitate sustainable agricultural intensification.Accurate UCR cultivation mapping is needed to ensure food security,sustainable water management,and rural revitalization.Howeve...Upland crop-rice cropping systems(UCR)facilitate sustainable agricultural intensification.Accurate UCR cultivation mapping is needed to ensure food security,sustainable water management,and rural revitalization.However,datasets describing cropping systems are limited in spatial coverage and crop types.Mapping UCR is more challenging than crop identification and most existing approaches rely heavily on accurate phenology calendars and representative training samples,which limits its applications over large regions.We describe a novel algorithm(RRSS)for automatic mapping of upland crop-rice cropping systems using Sentinel-1 Synthetic Aperture Radar(SAR)and Sentinel-2 Multispectral Instrument(MSI)data.One indicator,the VV backscatter range,was proposed to discriminate UCR and another two indicators were designed by coupling greenness and pigment indices to further discriminate tobacco or oilseed UCR.The RRSS algorithm was applied to South China characterized by complex smallholder rice cropping systems and diverse topographic conditions.This study developed 10-m UCR maps of a major rice bowl in South China,the Xiang-Gan-Min(XGM)region.The performance of the RRSS algorithm was validated based on 5197 ground-truth reference sites,with an overall accuracy of 91.92%.There were7348 km^(2) areas of UCR,roughly one-half of them located in plains.The UCR was represented mainly by oilseed-UCR and tobacco-UCR,which contributed respectively 69%and 15%of UCR area.UCR patterns accounted for only one-tenth of rice production,which can be tripled by intensification from single rice cropping.Application to complex and fragmented subtropical regions suggested the spatiotemporal robustness of the RRSS algorithm,which could be further applied to generate 10-m UCR datasets for application at national or global scales.展开更多
文摘目的初探纵向弛豫时间定量成像(T1 mapping)和酰胺质子转移加权(amide proton transfer weighted,APTw)成像鉴别慢性肾病(chronic kidney disease,CKD)患者与健康人群的价值。材料与方法回顾性分析2019年8月至2020年10月行3.0 T MRI检查的CKD患者病例资料共21例(女6例,男15例),所有患者均经大连医科大学附属第一医院肾内科医师依据CKD临床实践指南确诊;同时收集24例健康志愿者临床资料作为对照组。将所有原始图像导入ISP工作站,生成伪彩图。由两名影像科诊断医师采用双盲法分别从肾的上极、中部、下极各选择一个层面并于皮质和髓质中分别放置感兴趣区(region of interest,ROI),面积约10~20 mm^(2),避开肾窦、大血管及肾周组织。测量所得皮髓质T1值与APT值应用SPSS 26.0软件进行统计学分析:应用组内相关系数(intra-class correlation coefficients,ICC)进行观察者间测量结果一致性检验;根据数据正态分布情况,采用独立样本t检验或Mann-Whitney U检验分析两组间参数值差异,P<0.05为差异具有统计学意义;采用受试者工作特征(receiver operating characteristic,ROC)曲线分析各参数诊断效能,根据最大约登指数得到相对应的阈值、敏感度和特异度,并计算曲线下面积(area under the curve,AUC)值。结果两位观察者间测量结果一致性良好(ICC>0.75)。CKD组双肾皮质T1值和皮质APT值显著高于健康对照组(P<0.05);左肾皮质T1值鉴别CKD的AUC值为0.887,敏感度66.7%,特异度100.0%;左肾皮质APT值鉴别CKD的AUC值为0.966,敏感度95.2%,特异度95.8%;右肾皮质T1值鉴别CKD的AUC值为0.960,敏感度76.2%,特异度100.0%;右肾皮质APT值鉴别CKD的AUC值为0.921,敏感度85.7%,特异度91.7%。结论T1 mapping与APTw成像可无创有效鉴别CKD,基于二者的定量参数在一定程度上反映了单侧肾脏各自结构与功能的改变,有望为临床疾病诊断提供一定的参考价值。
基金supported by the National Natural Science Foundation of China(42171325,41771468)the National Key Research and Development Program of China(2022YFD2001101)+1 种基金the Science Bureau of Fujian Province(2023Y0042)the Finance Department and the Digital Economy Alliance of Fujian Province。
文摘Upland crop-rice cropping systems(UCR)facilitate sustainable agricultural intensification.Accurate UCR cultivation mapping is needed to ensure food security,sustainable water management,and rural revitalization.However,datasets describing cropping systems are limited in spatial coverage and crop types.Mapping UCR is more challenging than crop identification and most existing approaches rely heavily on accurate phenology calendars and representative training samples,which limits its applications over large regions.We describe a novel algorithm(RRSS)for automatic mapping of upland crop-rice cropping systems using Sentinel-1 Synthetic Aperture Radar(SAR)and Sentinel-2 Multispectral Instrument(MSI)data.One indicator,the VV backscatter range,was proposed to discriminate UCR and another two indicators were designed by coupling greenness and pigment indices to further discriminate tobacco or oilseed UCR.The RRSS algorithm was applied to South China characterized by complex smallholder rice cropping systems and diverse topographic conditions.This study developed 10-m UCR maps of a major rice bowl in South China,the Xiang-Gan-Min(XGM)region.The performance of the RRSS algorithm was validated based on 5197 ground-truth reference sites,with an overall accuracy of 91.92%.There were7348 km^(2) areas of UCR,roughly one-half of them located in plains.The UCR was represented mainly by oilseed-UCR and tobacco-UCR,which contributed respectively 69%and 15%of UCR area.UCR patterns accounted for only one-tenth of rice production,which can be tripled by intensification from single rice cropping.Application to complex and fragmented subtropical regions suggested the spatiotemporal robustness of the RRSS algorithm,which could be further applied to generate 10-m UCR datasets for application at national or global scales.
