基于k-means簇分析提取DSC成像的脑动脉输入函数

手动提取DSC-MRI脑灌注动脉输入函数的方法耗时长、对操作者依赖，同时准确性和可再现性较差.针对该问题，提出了一种基于k-means簇分析原理的半自动计算方法，对感兴趣区内像素分簇并计算各簇平均曲线，当某簇平均曲线的［峰值/(峰值到达时间×半高宽)］最大时，该簇像素的平均曲线则被视为动脉输入函数.选取20例健康被试的灌注数据进行测试，通过与传统手动方法进行比较，证实所提方法的临床可适用性.结果表明，基于k-means簇分析的半自动方法提取的动脉输入函数优于人工方法，提高了计算的准确性和可靠性，减少了分析时间和操作者依赖.

Selection of Cerebral Arterial Input Function with DSC Imaging Based on k-means Cluster Analysis

The manual method for the detection of arterial input function(AIF)in cerebral perfusion based on DSC-MRI technique was not only time-consuming but also user-dependent， meanwhile， the accuracy and reproducibility were not very satisfactory. To solve this problem， a semi-automatic AIF detection method based on k-means cluster analysis is suggested. The pixels in the region of interest(ROI)were divided into several clusters and the mean curve of each cluster was calculated. A measure，［peak value /(time to peak× full width at half maximum)］， was calculated for each mean curve， and the one with the maximum measured value was used to determine the AIF. Twenty subjects were taken part in the research. By comparing with the result derived from the traditional manual method， the clinical feasibility was validated. The result demonstrated that the AIF obtained from the semi-automatic method based on k-means cluster analysis was superior to that based on traditional manual method. In conclusion， the semi-automatic selection of AIF based on the k-means cluster analysis can not only reduce the analysis time and observer dependence， but also improve the calculation accuracy and reliability.