Efficient DCE-MRI Parameter and Uncertainty Estimation Using a Neural Network.
Efficient DCE-MRI Parameter and Uncertainty Estimation Using a Neural Network.
复制标题
使用神经网络进行高效 DCE-MRI 参数和不确定性估计。
DOI：
10.1109/tmi.2019.2953901
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发表时间：
2020-05
影响因子：
10.6
通讯作者：
Nayak KS
中科院分区：
文献类型：
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作者：
Bliesener Y;Acharya J;Nayak KS
定量DCE-MRI提供对示踪动力学参数的体素估计，这些参数在评估健康和疾病方面有价值。这些地图受到许多已知可变性来源的影响。使用目前的方法计算这种可变性的成本很高，而且通常不会报告。在这里，我们展示了一种新的方法来同时估计示踪剂动力学参数及其不确定性，因为示踪剂动力学模型的内在特性，以非常低的计算时间。我们训练并使用神经网络来估计示踪剂运动参数的近似联合后验分布。不确定度是针对每个体素进行估计的，并且特定于患者、检查和病变。我们演示了这些方法产生准确示踪动力学图的能力。我们将预测的参数范围与由噪声和数字参考对象的后处理差异引入的不确定性进行比较。预测的参数范围与在不同噪声实现和回归算法中观察到的示踪剂动力学参数范围具有很好的相关性。我们还证明了这种方法的价值，以区分显着和微不足道的变化，脑肿瘤药代动力学随时间的推移。这是通过强制解决应用示踪剂动力学模型中的模型奇点的一致性来实现的。
Quantitative DCE-MRI provides voxel-wise estimates of tracer-kinetic parameters that are valuable in the assessment of health and disease. These maps suffer from many known sources of variability. This variability is expensive to compute using current methods, and is typically not reported. Here, we demonstrate a novel approach for simultaneous estimation of tracer-kinetic parameters and their uncertainty due to intrinsic characteristics of the tracer-kinetic model, with very low computation time. We train and use a neural network to estimate the approximate joint posterior distribution of tracer-kineticparameters. Uncertainties are estimated for each voxel and are specific to the patient, exam, and lesion. We demonstrate the methods’ ability to produce accurate tracer-kinetic maps. We compare predicted parameter ranges with uncertainties introduced by noise and by differences in post-processing in a digital reference object. The predicted parameter ranges correlate well with tracer-kineticparameter ranges observed across different noise realizations and regression algorithms. We also demonstrate the value of this approach to differentiate significant from insignificant changes in brain tumor pharmacokinetics over time. This is achieved by enforcing consistency in resolving model singularities in the applied tracer-kinetic model.