DCE-MRI的空间二组织室模型
Spatial two tissue compartment model for DCE-MRI
摘要：
在动态对比增强磁共振成像（DCE-MRI）的定量分析中，隔室模型允许用具有生物学意义的动力学参数来描述造影剂的摄取。由于简单的模型往往不能充分描述所观察到的吸收行为，更复杂的隔室模型已被提出。然而，由更复杂的房室模型引起的非线性回归问题常常遭受参数冗余。在本文中，我们将空间光滑的两个组织房室模型的动力学参数，通过施加高斯马尔可夫随机场先验。我们分析在何种程度上这种空间正则化有助于避免参数冗余，并获得稳定的参数估计。选择一个完整的贝叶斯方法，我们得到后验和点估计运行马尔可夫链蒙特卡罗模拟。所提出的方法进行评估，模拟浓度时间曲线，以及从乳腺癌研究的体内数据。
英文摘要：
In the quantitative analysis of Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) compartment models allow to describe the uptake of contrast medium with biological meaningful kinetic parameters. As simple models often fail to adequately describe the observed uptake behavior, more complex compartment models have been proposed. However, the nonlinear regression problem arising from more complex compartment models often suffers from parameter redundancy. In this paper, we incorporate spatial smoothness on the kinetic parameters of a two tissue compartment model by imposing Gaussian Markov random field priors on them. We analyse to what extent this spatial regularisation helps to avoid parameter redundancy and to obtain stable parameter estimates. Choosing a full Bayesian approach, we obtain posteriors and point estimates running Markov Chain Monte Carlo simulations. The proposed approach is evaluated for simulated concentration time curves as well as for in vivo data from a breast cancer study.
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