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荷瘤大鼠动态对比增强磁共振成像中基于模型的动脉输入函数比较

Comparison of model-based arterial input functions for dynamic contrast-enhanced MRI in tumor bearing rats.

作者信息

McGrath Deirdre M, Bradley Daniel P, Tessier Jean L, Lacey Tony, Taylor Chris J, Parker Geoffrey J M

机构信息

Imaging Science and Biomedical Engineering, School of Cancer and Imaging Sciences, University of Manchester, Manchester, United Kingdom.

出版信息

Magn Reson Med. 2009 May;61(5):1173-84. doi: 10.1002/mrm.21959.

Abstract

When using tracer kinetic modeling to analyze dynamic contrast-enhanced MRI (DCE-MRI) it is necessary to identify an appropriate arterial input function (AIF). The measured AIF is often poorly sampled in both clinical and preclinical MR systems due to the initial rapid increase in contrast agent concentration and the subsequent large-scale signal change that occurs in the arteries. However, little work has been carried out to quantify the sensitivity of tracer kinetic modeling parameters to the form of AIF. Using a preclinical experimental data set, we sought to measure the effect of varying model forms of AIF on the extended Kety compartmental model parameters (K(trans), v(e), and v(p)) through comparison with the results of experimentally acquired high temporal resolution AIFs. The AIF models examined have the potential to be parameterized on lower temporal resolution data to predict the form of the true, higher temporal resolution AIF. The models were also evaluated through application to the population average AIF. It was concluded that, in the instance of low temporal resolution or noisy data, it may be preferable to use a bi-exponential model applied to the raw data AIF, or when individual measurements are not available a bi-exponential model of the average AIF.

摘要

在使用示踪剂动力学模型分析动态对比增强磁共振成像(DCE-MRI)时,有必要确定合适的动脉输入函数(AIF)。由于造影剂浓度最初的快速增加以及随后动脉中发生的大规模信号变化,在临床和临床前磁共振系统中,所测量的AIF通常采样不佳。然而,在量化示踪剂动力学模型参数对AIF形式的敏感性方面,几乎没有开展相关工作。利用一个临床前实验数据集,我们试图通过与实验获取的高时间分辨率AIF的结果进行比较,来测量不同形式的AIF模型对扩展Kety房室模型参数(K(trans)、v(e)和v(p))的影响。所研究的AIF模型有潜力根据较低时间分辨率的数据进行参数化,以预测真实的、更高时间分辨率AIF的形式。这些模型还通过应用于总体平均AIF进行了评估。得出的结论是,在时间分辨率较低或数据有噪声的情况下,使用应用于原始数据AIF的双指数模型可能更可取,或者当无法获得个体测量值时,使用平均AIF的双指数模型。

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