基于k-means簇分析提取DSC成像的脑动脉输入函数

doi:10.12068/j.issn.1005-3026.2014.12.002

东北大学学报:自然科学版 ›› 2014, Vol. 35 ›› Issue (12): 1677-1681.DOI: 10.12068/j.issn.1005-3026.2014.12.002

基于k-means簇分析提取DSC成像的脑动脉输入函数

尹建东¹，孙洪赞²，杨嘉文³，郭启勇²

(1. 东北大学中荷生物医学与信息工程学院，辽宁沈阳110819； 2. 中国医科大学附属盛京医院放射科，辽宁沈阳110004； 3. 中国医科大学附属盛京医院医疗设备科，辽宁沈阳110004)

收稿日期:2013-08-19 修回日期:2013-08-19 出版日期:2014-12-15 发布日期:2014-09-12
通讯作者: 尹建东
作者简介:尹建东(1980-)，男，山东德州人，东北大学博士研究生; 郭启勇(1958-)，男，辽宁沈阳人，中国医科大学教授，博士生导师.
基金资助:
辽宁省工程技术研究中心资助项目(2011040059-301).

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

YIN Jian-dong¹， SUN Hong-zan²， YANG Jia-wen³， GUO Qi-yong²

1. Sino-Dutch Biomedical and Information Engineering School， Northeastern University， Shenyang 110819， China; 2. Department of Radiology， Shengjing Hospital of China Medical University， Shenyang 110004， China; 3. Department of Medical Equipment， Shengjing Hospital of China Medical University， Shenyang 110004， China.

Received:2013-08-19 Revised:2013-08-19 Online:2014-12-15 Published:2014-09-12
Contact: GUO Qi-yong
About author:-
Supported by:
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摘要/Abstract

摘要： 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.

关键词: 脑灌注, 动态敏感对比, 动脉输入函数, 血液动力学参数, 簇分析

Abstract: 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.

Key words: cerebral perfusion, dynamic susceptibility contrast, arterial input function, hemodynamic parameters, cluster analysis

中图分类号:

TP274

尹建东，孙洪赞，杨嘉文，郭启勇. 基于k-means簇分析提取DSC成像的脑动脉输入函数[J]. 东北大学学报:自然科学版, 2014, 35(12): 1677-1681.

YIN Jian-dong， SUN Hong-zan， YANG Jia-wen， GUO Qi-yong. Selection of Cerebral Arterial Input Function with DSC Imaging Based on k-means Cluster Analysis[J]. Journal of Northeastern University Natural Science, 2014, 35(12): 1677-1681.

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基于k-means簇分析提取DSC成像的脑动脉输入函数

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

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