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基于样条模型的小动物动态对比增强磁共振成像中团注到达时间估计。

Bolus arrival time estimation in dynamic contrast-enhanced magnetic resonance imaging of small animals based on spline models.

机构信息

Department of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany. Faculty of Biosciences, University of Heidelberg, Heidelberg, Germany. Author to whom any correspondence should be addressed.

出版信息

Phys Med Biol. 2019 Feb 5;64(4):045003. doi: 10.1088/1361-6560/aafce7.

Abstract

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is used to quantify perfusion and vascular permeability. In most cases a bolus arrival time (BAT) delay exists between the arterial input function (AIF) and the contrast agent arrival in the tissue of interest which needs to be estimated. Existing methods for BAT estimation are tailored to tissue concentration curves, which have a fast upslope to the peak as frequently observed in patient data. However, they may give poor results for curves that do not have this characteristic shape such as tissue concentration curves of small animals. In this paper, we propose a method for BAT estimation of signals that do not have a fast upslope to their peak. The model is based on splines which are able to adapt to a large variety of concentration curves. Furthermore, the method estimates BATs on a continuous time scale. All relevant model parameters are automatically determined by generalized cross validation. We use simulated concentration curves of small animal and patient settings to assess the accuracy and robustness of our approach. The proposed method outperforms a state-of-the-art method for small animal data and it gives competitive results for patient data. Finally, it is tested on in vivo acquired rat data where accuracy of BAT estimation was also improved upon the state-of-the-art method. The results indicate that the proposed method is suitable for accurate BAT estimation of DCE-MRI data, especially for small animals.

摘要

动态对比增强磁共振成像(DCE-MRI)用于量化灌注和血管通透性。在大多数情况下,动脉输入函数(AIF)和感兴趣组织中对比剂到达之间存在团注到达时间(BAT)延迟,需要进行估计。现有的 BAT 估计方法针对组织浓度曲线进行了定制,这些曲线在患者数据中经常观察到快速上升到峰值。然而,对于没有这种特征形状的曲线,例如小动物的组织浓度曲线,它们可能会给出较差的结果。在本文中,我们提出了一种用于估计没有快速上升到峰值的信号的 BAT 的方法。该模型基于能够适应各种浓度曲线的样条。此外,该方法在连续时间尺度上估计 BAT。所有相关的模型参数都通过广义交叉验证自动确定。我们使用小动物和患者设置的模拟浓度曲线来评估我们方法的准确性和稳健性。所提出的方法在小动物数据方面优于最先进的方法,在患者数据方面也给出了有竞争力的结果。最后,它在体内采集的大鼠数据上进行了测试,其中 BAT 估计的准确性也优于最先进的方法。结果表明,该方法适用于 DCE-MRI 数据的准确 BAT 估计,特别是对于小动物。

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