Stepwise heterogeneity analysis of breast tumors in perfusion DCE-MRI datasets
Stepwise heterogeneity analysis of breast tumors in perfusion DCE-MRI datasets
复制标题
灌注 DCE-MRI 数据集中乳腺肿瘤的逐步异质性分析
DOI：
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发表时间：
2012
期刊：
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通讯作者：
K. Englmeier
中科院分区：
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作者：
Mojgan Mohajer;Volker J Schmid;Nina Engels;P. Noel;E. Rummeny;K. Englmeier
乳腺癌组织灌注动态增强MRI（DCE-MRI）信号曲线揭示了肿瘤血管生成的重要信息。病理学研究表明，乳腺肿瘤由不同的亚区组成，特别是在其生长过程中具有更均匀的性质。如果这些子区域的特征与灌注和血管生成相关，则DCEMRI信号曲线中的差异应该是可识别的。我们介绍了一个逐步聚类方法，在第一步使用一个新的相似性度量。新的相似性度量（PM）比较两条曲线的平行洗脱阶段。为了区分洗脱期的起始点，将线性回归方法部分拟合至曲线。在下一步中，将冲刷阶段的最小信号值归一化为零。最后，根据在洗脱阶段期间逐点差异之间的最大变化来计算PM。在聚类的第二步中，使用欧几里德距离对具有平行洗脱的信号曲线组进行聚类。在15个不同类型乳腺肿瘤的DCE-MRI乳腺数据集上对所介绍的方法进行了评估。我们的新异质性分析的使用是可行的，在单个患者的检查，提高乳腺MR诊断。
The signal curves in perfusion dynamic contrast enhanced MRI (DCE-MRI) of cancerous breast tissue reveal valuable information about tumor angiogenesis. Pathological studies have illustrated that breast tumors consist of different subregions, especially with more homogeneous properties during their growth. Differences should be identifiable in DCEMRI signal curves if the characteristics of these sub-regions are related to the perfusion and angiogenesis. We introduce a stepwise clustering method which in a first step uses a new similarity measure. The new similarity measure (PM) compares how parallel washout phases of two curves are. To distinguish the starting point of the washout phase, a linear regression method is partially fitted to the curves. In the next step, the minimum signal value of the washout phase is normalized to zero. Finally, PM is calculated according to maximal variation among the point wise differences during washout phases. In the second step of clustering the groups of signal curves with parallel washout are clustered using Euclidean distance. The introduced method is evaluated on 15 DCE-MRI breast datasets with different types of breast tumors. The use of our new heterogeneity analysis is feasible in single patient examination and improves breast MR diagnostics.