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在动态对比增强磁共振成像(DCE-MRI)中用于示踪剂动力学分析的团注到达时间和动脉输入函数估计的改进

Improved bolus arrival time and arterial input function estimation for tracer kinetic analysis in DCE-MRI.

作者信息

Singh Anup, Rathore Ram K Singh, Haris Mohammad, Verma Sanjay K, Husain Nuzhat, Gupta Rakesh K

机构信息

Department of Mathematics and Statistics, Indian Institute of Technology, Kanpur, India.

出版信息

J Magn Reson Imaging. 2009 Jan;29(1):166-76. doi: 10.1002/jmri.21624.

Abstract

PURPOSE

To develop a methodology for improved estimation of bolus arrival time (BAT) and arterial input function (AIF) which are prerequisites for tracer kinetic analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data and to verify the applicability of the same in the case of intracranial lesions (brain tumor and tuberculoma).

MATERIALS AND METHODS

A continuous piecewise linear (PL) model (with BAT as one of the free parameters) is proposed for concentration time curve C(t) in T(1)-weighted DCE-MRI. The resulting improved procedure suggested for automatic extraction of AIF is compared with earlier methods. The accuracy of BAT and other estimated parameters is tested over simulated as well as experimental data.

RESULTS

The proposed PL model provides a good approximation of C(t) trends of interest and fit parameters show their significance in a better understanding and classification of different tissues. BAT was correctly estimated. The automatic and robust estimation of AIF obtained using the proposed methodology also corrects for partial volume effects. The accuracy of tracer kinetic analysis is improved and the proposed methodology also reduces the time complexity of the computations.

CONCLUSION

The PL model parameters along with AIF measured by the proposed procedure can be used for an improved tracer kinetic analysis of DCE-MRI data.

摘要

目的

开发一种改进的方法来估计团注到达时间(BAT)和动脉输入函数(AIF),这是动态对比增强磁共振成像(DCE-MRI)数据示踪剂动力学分析的先决条件,并验证其在颅内病变(脑肿瘤和结核瘤)情况下的适用性。

材料与方法

针对T(1)加权DCE-MRI中的浓度-时间曲线C(t),提出了一种连续分段线性(PL)模型(将BAT作为自由参数之一)。将由此提出的用于自动提取AIF的改进程序与早期方法进行比较。在模拟数据和实验数据上测试BAT及其他估计参数的准确性。

结果

所提出的PL模型能很好地近似感兴趣的C(t)趋势,拟合参数在更好地理解和分类不同组织方面显示出其重要性。BAT得到了正确估计。使用所提出的方法获得的AIF的自动且稳健的估计也校正了部分容积效应。示踪剂动力学分析的准确性得到提高,并且所提出的方法还降低了计算的时间复杂度。

结论

所提出程序测量的PL模型参数以及AIF可用于改进DCE-MRI数据的示踪剂动力学分析。

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