高级检索
    张冉, 葛海涛, 朱红, 巩萍. 基于静息态功能磁共振异常脑区体素值的帕金森病分类研究[J]. 徐州医科大学学报, 2018, 38(1): 21-25.
    引用本文: 张冉, 葛海涛, 朱红, 巩萍. 基于静息态功能磁共振异常脑区体素值的帕金森病分类研究[J]. 徐州医科大学学报, 2018, 38(1): 21-25.
    ZHANG Ran, GE Haitao, ZHU Hong, GONG Ping. Classification of Parkinson′s disease based on abnormal brain region voxels with rest-stating functional magnetic resonance imaging[J]. Journal of Xuzhou Medical University, 2018, 38(1): 21-25.
    Citation: ZHANG Ran, GE Haitao, ZHU Hong, GONG Ping. Classification of Parkinson′s disease based on abnormal brain region voxels with rest-stating functional magnetic resonance imaging[J]. Journal of Xuzhou Medical University, 2018, 38(1): 21-25.

    基于静息态功能磁共振异常脑区体素值的帕金森病分类研究

    Classification of Parkinson′s disease based on abnormal brain region voxels with rest-stating functional magnetic resonance imaging

    • 摘要: 目的通过提取正常对照组与帕金森病(PD)组静息态功能磁共振图像特征实现PD的分类。方法对32例正常人(正常对照组)与32例PD患者(PD组)采用3.0T磁共振扫描仪进行静息态功能磁共振检查;接着使用DPARSF软件对图像进行预处理,得到全脑的低频振幅(ALFF)、比率低频振幅(fALFF)和局部一致性(ReHo)参数图;然后对参数图利用双样本T检验提取具有显著差异的脑区体素值作为图像特征值,利用支持向量机分类器对特征值进行模型训练;最后采用分类精度、受试者操作特性(receiver operating characteristics,ROC)曲线及曲线下面积(AUC)值对分类结果进行评价。结果在ALFF、fALFF和ReHo参数图上分别提取了1 167、1 678、 2 780个异常脑区体素值,利用这些脑区体素值取得了较理想的分类结果,其中基于ReHo参数图的分类结果最好,其分类精度为96.97%,AUC值为0.99。结论通过提取正常对照组与PD组静息态功能磁共振图像中差异脑区体素值,可以对PD实现较高准确率的分类,为临床诊断提供辅助依据。

       

      Abstract: Objective To investigate the classification of Parkinson’s disease (PD) through analyzing the profile of resting-state functional magnetic resonance of PD patients and healthy subjects. MethodsA total of 32 healthy subjects and 32 PD patients underwent resting-state functional magnetic resonance by 3.0T MRI. The resultant images were pre-treated by DPARSF software, yielding the amplitude of low-frequency fluctuation (ALFF), fractional amplitude of low-frequency fluctuation (fALFF) and regional homogeneity (ReHo) parameter maps. Then, the two-sample test was used to extract voxel values of significantly different brain regions which were viewed as the imaging features and the support vector machine classifier was used to train the model with the features. Finally, the classification accuracy, receiver operating characteristics (ROC) curve and the area under the curve (AUC) were evaluated. ResultsA total of 1167, 1678 and 2780 voxel values in abnormal brain regions were extracted from ALFF, fALFF and ReHo parameter maps. The highest classification accuracy was 96.97%, while the AUC value was 0.99. ConclusionsPD classification can be higher accurate through extracting the voxel values of abnormal brain regions from resting-state functional magnetic resonance images, providing an auxiliary basis for clinical diagnosis.

       

    /

    返回文章
    返回