阿尔茨海默病患者脑扩散张量成像自动纤维定量分析  

Analysis on cerebral diffusion tensor imaging automatic fiber quantification of patients with Alzheimer's disease

作　　者：陈飞 青钊 戴真煜[1] 姚立正[1] 董从松[1] 穆天池 李卫萍[2] 王澍[1] 张冰[2] Chen Fei;Qing Zhao;Dai Zhenyu;Yao Lizheng;Dong Congsong;Mu Tianchi;Li Weiping;Wang Shu;Zhang Bing(Department of Radiology,Yancheng Third People's Hospital,the Yancheng School of Clinical Medicine of Nanjing Medical University,Yancheng 224001,China;Department of Radiology,the Affiliated Drum Tower Hospital of Nanjing University Medical School,Nanjing 210008,China)

机构地区：[1]盐城市第三人民医院(南京医科大学盐城临床医学院)医学影像科,224001 [2]南京大学医学院附属鼓楼医院医学影像科,210008

出　　处：《中华行为医学与脑科学杂志》2020年第11期983-988,共6页Chinese Journal of Behavioral Medicine and Brain Science

基　　金：国家自然科学基金项目(81720108022);江苏省卫计委"科教强卫"工程医学重点人才项目(ZDRCA2016064);江苏省"六大人才高峰项目"高层次人才项目(WSN-313)。

摘　　要：目的探讨磁共振(magnetic resonance,MR)扩散张量成像(diffusion tensor imaging,DTI)自动纤维定量(automatic fiber quantification,AFQ)在阿尔茨海默病(Alzheimer’s disease,AD)诊断预测中的应用价值。方法搜集21例AD患者(AD组)和33例正常对照(NC组)的临床和MR资料。采用AFQ软件分析DTI数据,追踪脑内20根白质纤维束,比较各纤维束各项异性指数(fractional anisotropy,FA)、平均扩散系数(mean diffusivity,MD)值的组间差异。沿每根纤维束走行方向分成100等份,将各等份的FA或MD值作为一个特征。筛选具有组间差异的特征,通过支持向量机(support vector machine,SVM)留一法交叉验证对AD和NC进行分类。采用受试者工作特征(receiver operating characteristic,ROC)曲线评价分类效能。结果20根纤维束中的11根[左/右丘脑前束(anterior thalamic radiation,ATR)、左/右皮质脊髓束(corticospinal tract,CST)、胼胝体膝部束(corpus callosum,CC Genu)、右下纵束(longitudinal fasciculus,ILF)、右上纵束(superior longitudinal fasciculus,SLF)、左/右钩状束(uncinated fasciculus,UF)和左/右弓形束(arcuate fasciculus,AF)]在所有受检者中被成功追踪。相对NC组而言,AD组2根纤维束(左/右UF)的FA值明显减低(t=-2.532,-2.391,均P<0.05),7根纤维束(左ATR、左/右CST、右ILF、左/右UF、左AF)的MD值明显升高(t=2.569,2.411,2.108,2.357,3.773,3.796,3.492,均P<0.05)。在11根纤维束的2200个特征中筛选出412个具有组间差异的分类特征,其中78个FA特征分布于7根纤维束(左ATR、左/右CST、CC Genu、右ILF、左/右UF),334个MD特征分布于9根纤维束(左/右ATR、左/右CST、CC Genu、右ILF、左/右UF、左AF)。SVM分类准确度为85.19%,敏感性为80.95%,特异性为87.88%,ROC曲线下面积为0.8947。结论基于DTI的AFQ分析在AD诊断预测中具有较高的应用价值。Objective:To investigate the application value of magnetic resonance(MR)diffusion tensor imaging(DTI)automatic fiber quantification(AFQ)in the diagnosis and prediction of Alzheimer's disease(AD).Methods:Clinical and MR data of 21 patients with AD(AD group)and 33 normal controls(NC group)were collected.AFQ software was used to analyze DTI data,track 20 white matter fiber bundles in the brain,and compare the differences of fractional anisotropy(FA)and mean diffusivity(MD)value of each bundle between groups.Each fiber bundle was divided into 100 equal parts along the direction of travel,and the FA or MD value of each part was taken as a characteristic.Screening the characteristics with statistic differences between groups for classification of AD and NC by support vector machine(SVM)with leave one method for cross validation.Classification effectiveness was evaluated using the receiver operating characteristic(ROC)curve.Results:Eleven(left/right anterior thalamic radiation(ATR),left/right corticospinal tract(CST),genu of corpus callosum(CC Genu),right inferior longitudinal fasciculus(ILF),right superior longitudinal fasciculus(SLF),left/right uncinated fasciculus(UF),and left/right arcuate fasciculus(AF))of the 20 fiber bundles were successfully tracked in all subjects.Compared with NC group,the FA values of 2 fiber bundles(left/right UF)in AD group were significantly decreased(t=-2.532,-2.391,both P<0.05),and the MD values of 7 fiber bundles(left ATR,left/right CST,right ILF,left/right UF,and left AF)were significantly increased(t=2.569,2.411,2.108,2.357,3.773,3.796,3.492,all P<0.05).Among the 2200 characteristics of 11 fiber bundles,412 classification characteristics with inter-group differences were selected.Among which,78 FA characteristics were distributed in 7 fiber bundles(left ATR,left/right CST,CC Genu,right ILF,left/right UF),and 334 MD characteristics were distributed in 9 fiber tracts(left/right ATR,left/right CST,CC Genu,right ILF,left/right UF,and left AF).The accuracy of SVM classification was 85.19%,

关 键 词：扩散张量成像 自动纤维定量 阿尔茨海默病 分类 

分 类 号：R749.16[医药卫生—神经病学与精神病学]