动态PET数据参数成像的计算方法
Computational approaches for parametric imaging of dynamic PET data
摘要：
核医学数据的参数成像利用动态功能图像，以便重建与注入生物组织中的特定示踪剂的代谢相关的动力学参数的图。从计算的角度来看，实现参数图像需要逐像素数值解的分区反问题，通常是不适定和非线性。在本文中，我们介绍了一个快速的数值优化方案参数成像依赖于一个正规化版本的标准仿射缩放置信域方法。这种方法的验证是实现在一个模拟框架，脑成像和性能的比较是相对于一个正则化的高斯-牛顿计划和一个标准的非线性最小二乘算法。
英文摘要：
Parametric imaging of nuclear medicine data exploits dynamic functional images in order to reconstruct maps of kinetic parameters related to the metabolism of a specific tracer injected in the biological tissue. From a computational viewpoint, the realization of parametric images requires the pixel-wise numerical solution of compartmental inverse problems that are typically ill-posed and nonlinear. In the present paper we introduce a fast numerical optimization scheme for parametric imaging relying on a regularized version of the standard affine-scaling Trust Region method. The validation of this approach is realized in a simulation framework for brain imaging and comparison of performances is made with respect to a regularized Gauss-Newton scheme and a standard nonlinear least-squares algorithm.
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