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首页> 外文期刊>Physics in medicine and biology. >Bolus arrival time estimation in dynamic contrast-enhanced magnetic resonance imaging of small animals based on spline models
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Bolus arrival time estimation in dynamic contrast-enhanced magnetic resonance imaging of small animals based on spline models

机译:基于样条模型的小动物动态对比度增强磁共振成像的推注抵达时间估计

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摘要

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.
机译:动态对比度增强的磁共振成像(DCE-MRI)用于量化灌注和血管渗透性。在大多数情况下,在动脉输入功能(AIF)之间存在推注到达时间(BAT)延迟,并且造影剂到达需要估计的感兴趣组织。用于蝙蝠估计的现有方法对组织浓度曲线量身定制,其在患者数据中经常观察到峰值的快速上坡。然而,它们可能给出不具有这种特征形状的曲线的差,例如小动物的组织浓度曲线。在本文中,我们提出了一种用于蝙蝠估计的方法,其信号不具有快速上升到峰值。该模型基于样品曲线,能够适应大量浓度曲线。此外,该方法估计在连续时间尺度上的蝙蝠。所有相关模型参数都由广义交叉验证自动确定。我们使用小型动物和患者环境的模拟浓度曲线来评估我们方法的准确性和稳健性。所提出的方法优于小型动物数据的最先进的方法,为患者数据提供了竞争结果。最后,在体内获得的RAT数据中测试,在最先进的方法上还改善了BAT估计的准确性。结果表明,该方法适用于DCE-MRI数据的准确蝙蝠估计,特别是对于小动物。

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