Chen Jeremy, Yao Jianhua, Thomasson David
Diagnostic Radiology Department, Clinical Center, National Institutes of Health, Bethesda, MD 20892, USA.
Med Image Comput Comput Assist Interv. 2008;11(Pt 1):594-601. doi: 10.1007/978-3-540-85988-8_71.
Dynamic Contrast Enhanced MRI (DCE-MRI) is today one of the most popular methods for tumor assessment. Several pharmacokinetic models have been proposed to analyze DCE-MRI. Most of them depend on an accurate arterial input function (AIF). We propose an automatic and versatile method to determine the AIF. The method has two stages, detection and segmentation, incorporating knowledge about artery structure, fluid kinetics, and the dynamic temporal property of DCE-MRI. We have applied our method in DCE-MRIs of four different body parts: breast, brain, liver and prostate. The results show that we achieve average 89.5% success rate for 40 cases. The pharmacokinetic parameters computed from the automatic AIF are highly agreeable with those from a manually derived AIF (R2 = 0.89, P (T <=t) = 0.19) and a semiautomatic AIF (R2 = 0.98, P(T <=t) = 0.01).
动态对比增强磁共振成像(DCE-MRI)如今是肿瘤评估中最常用的方法之一。已经提出了几种药代动力学模型来分析DCE-MRI。其中大多数依赖于准确的动脉输入函数(AIF)。我们提出了一种自动且通用的方法来确定AIF。该方法有检测和分割两个阶段,融合了关于动脉结构、流体动力学以及DCE-MRI动态时间特性的知识。我们已将我们的方法应用于四个不同身体部位(乳房、大脑、肝脏和前列腺)的DCE-MRI中。结果表明,对于40个病例,我们实现了平均89.5%的成功率。从自动AIF计算得到的药代动力学参数与手动推导的AIF(R2 = 0.89,P(T <=t) = 0.19)和半自动AIF(R2 = 0.98,P(T <=t) = 0.01)计算得到的参数高度一致。