School of Biomedical Engineering and Imaging Sciences, King's College London, United Kingdom; The Alan Turing Institute London, United Kingdom.
School of Biomedical Engineering and Imaging Sciences, King's College London, United Kingdom.
Med Image Anal. 2020 Feb;60:101611. doi: 10.1016/j.media.2019.101611. Epub 2019 Nov 9.
Myocardial blood flow can be quantified from dynamic contrast-enhanced magnetic resonance (MR) images through the fitting of tracer-kinetic models to the observed imaging data. The use of multi-compartment exchange models is desirable as they are physiologically motivated and resolve directly for both blood flow and microvascular function. However, the parameter estimates obtained with such models can be unreliable. This is due to the complexity of the models relative to the observed data which is limited by the low signal-to-noise ratio, the temporal resolution, the length of the acquisitions and other complex imaging artefacts. In this work, a Bayesian inference scheme is proposed which allows the reliable estimation of the parameters of the two-compartment exchange model from myocardial perfusion MR data. The Bayesian scheme allows the incorporation of prior knowledge on the physiological ranges of the model parameters and facilitates the use of the additional information that neighbouring voxels are likely to have similar kinetic parameter values. Hierarchical priors are used to avoid making a priori assumptions on the health of the patients. We provide both a theoretical introduction to Bayesian inference for tracer-kinetic modelling and specific implementation details for this application. This approach is validated in both in silico and in vivo settings. In silico, there was a significant reduction in mean-squared error with the ground-truth parameters using Bayesian inference as compared to using the standard non-linear least squares fitting. When applied to patient data the Bayesian inference scheme returns parameter values that are in-line with those previously reported in the literature, as well as giving parameter maps that match the independant clinical diagnosis of those patients.
心肌血流可以通过拟合示踪动力学模型来从动态对比增强磁共振（MR）图像中定量，该模型适用于观察到的成像数据。使用多室交换模型是可取的，因为它们是基于生理学的，可以直接解决血流和微血管功能的问题。然而，使用这种模型获得的参数估计可能不可靠。这是由于模型相对于观察数据的复杂性，而观察数据受到低信噪比、时间分辨率、采集长度和其他复杂成像伪影的限制。在这项工作中，提出了一种贝叶斯推断方案，该方案允许从心肌灌注磁共振数据中可靠地估计两室交换模型的参数。贝叶斯方案允许将关于模型参数生理范围的先验知识纳入其中，并有助于利用相邻体素可能具有相似动力学参数值的附加信息。分层先验用于避免对患者健康状况做出先验假设。我们为示踪动力学建模的贝叶斯推断提供了理论介绍和特定应用的详细信息。该方法在体内和体内环境中都得到了验证。在体内，与使用标准非线性最小二乘拟合相比，贝叶斯推断显著降低了与真实参数的均方误差。当应用于患者数据时，贝叶斯推断方案返回的参数值与文献中先前报道的参数值一致，并且给出的参数图与这些患者的独立临床诊断相匹配。
