Department of Biomedical, Electronic and Telecommunication Engineering, University Federico II of Naples, via Claudio 21, 80131 Naples, Italy.
Med Biol Eng Comput. 2011 Apr;49(4):485-95. doi: 10.1007/s11517-010-0695-x. Epub 2010 Nov 3.
Traditionally, tracer kinetic modelling and pixel classification of DCE-MRI studies are accomplished separately, although they could greatly benefit from each other. In this article, we propose an expectation-maximisation scheme for simultaneous pixel classification and compartmental modelling of DCE-MRI studies. The key point in the proposed scheme is the estimation of the kinetic parameters (K(trans) and K(ep)) of the two-compartmental model. Typically, they are estimated via nonlinear least-squares fitting. In our scheme, by exploiting the iterative nature of the EM algorithm, we use instead a Taylor expansion of the modelling equation. We developed the theoretical framework for the particular case of two classes and evaluated the performances of the algorithm by means of simulations. Results indicate that the accuracy of the proposed method supersedes the traditional pixel-by-pixel scheme and approaches the theoretical lower bound imposed by the Cramer-Rao theorem. Preliminary results on real data were also reported.
传统上，DCE-MRI 研究中的示踪剂动力学建模和像素分类是分别进行的，尽管它们可以相互受益。在本文中，我们提出了一种用于 DCE-MRI 研究的同时像素分类和隔室建模的期望最大化方案。该方案的关键点在于估计双隔室模型的动力学参数（K(trans) 和 K(ep)）。通常，它们是通过非线性最小二乘拟合来估计的。在我们的方案中，通过利用 EM 算法的迭代性质，我们使用建模方程的泰勒展开式代替。我们为两类的特殊情况开发了理论框架，并通过模拟评估了算法的性能。结果表明，所提出方法的准确性超过了传统的逐像素方案，并接近由克拉美-罗定理施加的理论下限。还报告了真实数据的初步结果。
