A parametric model of the brain vascular system for estimation of the arterial input function (AIF) at the tissue level.
A parametric model of the brain vascular system for estimation of the arterial input function (AIF) at the tissue level.
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DOI：
10.1002/nbm.3695
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发表时间：
2017-05
影响因子：
2.9
通讯作者：
Jiang Q
中科院分区：
文献类型：
--
作者：
Nejad-Davarani SP;Bagher-Ebadian H;Ewing JR;Noll DC;Mikkelsen T;Chopp M;Jiang Q
在本文中，我们介绍了一种新的脑血管系统模型，它是基于流体动力学和血管形态定律而发展起来的。该模型用于处理血管结构不同层次的动脉输入函数(AIF)的离散度和延迟，并估计DCE图像中的局部AIF。我们开发了一种基于单纯形算法和Akaike信息准则的方法，用于估计DCE图像中采样的造影剂浓度信号属于血管树的不同层或来自该结构不同节点的不同信号电平的组合的可能性。为了对该方法进行评估，我们对该结构不同层次上的模拟局部AIF信号进行了测试。即使在信噪比为5.5的情况下，我们的方法也能够准确地检测出模拟信号的分支电平。当两个相同功率级的信号组合在一起时，我们的方法能够在50%的阈值下分离出复合AIF的基信号。我们将这种方法应用于动态增强计算机断层扫描(DCE-CT)数据，并使用该方法估计的参数创建了大脑的到达时间图。我们的模型修正后的AIF可用于求解药物动力学方程，以便更准确地估计DCE成像研究中的血管通透性参数。我们提出了一个基于血管形态和流体动力学定律的脑血管系统模型，用于在DSC和DCE MRI和DCE-CT研究中估计局部动脉输入功能。在这些研究中，使用这种局部动脉输入功能可以减少估计渗透性和灌流参数的误差。该模型在DCE-CT图像上进行了测试，使用模型参数创建了到达时间图，与大脑中的预期值相匹配。
In this paper, we introduce a novel model of the brain vascular system, which is developed based on laws of fluid dynamics and vascular morphology. This model is used to address dispersion and delay of the Arterial Input Function (AIF) at different levels of the vascular structure and to estimate the local AIF in DCE images. We developed a method based on the Simplex algorithm and Akaike Information Criterion for estimating the likelihood of the contrast agent concentration signal sampled in DCE images to belong to different layers of the vascular tree or being a combination of different signal levels from different nodes of this structure. To evaluate this method, we tested the method on simulated local AIF signals at different levels of this structure. Even down to an SNR of 5.5 our method was able to accurately detect the branching level of the simulated signals. When two signals with the same power levels were combined, our method was able to separate the base signals of the composite AIF at the 50% threshold. We applied this method to Dynamic Contract Enhanced Computed Tomography (DCE-CT) data and using the parameters estimated by our method, we created an arrival time map of the brain. Our model corrected AIFcan be used for solving the pharmacokinetic equations for more accurate estimation of vascular permeability parameters in DCE imaging studies. We have presented a model of the cerebral vascular system based on vascular morphology and laws of fluid dynamics, to be used for estimating the local arterial input function in DSC and DCE MRI and DCE-CT studies. Using this local arterial input function can reduce errors in estimation of permeability and perfusion parameters in these studies. The model was tested on DCE-CT images by creating an arrival time map using the model parameters, which matched the expected values in the brain.