Institute of Medical Information and Technology, School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
Comput Biol Med. 2010 Jun;40(6):565-71. doi: 10.1016/j.compbiomed.2010.03.012. Epub 2010 May 5.
Bayesian methods have been widely applied to the ill-posed problem of image reconstruction. Typically the prior information of the objective image is needed to produce reasonable reconstructions. In this paper, we propose a novel generalized Gibbs prior (GG-Prior), which exploits the basic affinity structure information in an image. The motivation for using the GG-Prior is that it has been shown to be effective noise suppression, while also maintaining sharp edges without oscillations. This feature makes it particularly attractive for the reconstruction of positron emission tomography (PET) where the aim is to identify the shape of objects from the background by sharp edges. We show that the standard paraboloidal surrogate coordinate ascent (PSCA) algorithm can be modified to incorporate the GG-Prior using a local linearized scheme in each iteration process. The proposed GG-Prior MAP reconstruction algorithm based on PSCA has been tested on simulated, real phantom data. Comparison studies with conventional filtered backprojection (FBP) method and Huber prior clearly demonstrate that the proposed GG-Prior performs better in lowering the noise, preserving the image edge and in higher signal noise ratio (SNR) condition.
贝叶斯方法已被广泛应用于图像重建这一不适定问题。通常需要目标图像的先验信息来生成合理的重建结果。在本文中，我们提出了一种新的广义吉布斯先验（GG-Prior），它利用了图像中的基本亲和结构信息。使用 GG-Prior 的动机是，它已被证明可以有效抑制噪声，同时保持边缘锐利而无振荡。这一特性使其特别适合于正电子发射断层扫描（PET）的重建，其目的是通过锐利的边缘从背景中识别物体的形状。我们表明，可以通过在每次迭代过程中使用局部线性化方案来修改标准的抛物面替代坐标上升（PSCA）算法以包含 GG-Prior。已经在模拟、真实体模数据上对基于 PSCA 的提出的 GG-Prior MAP 重建算法进行了测试。与传统的滤波反投影（FBP）方法和 Huber 先验的比较研究清楚地表明，提出的 GG-Prior 在降低噪声、保持图像边缘和在更高信噪比（SNR）条件下具有更好的性能。
