Hu X P, Johnson V, Wong W H, Chen C T
Department of Radiology, University of Chicago Hospitals, Illinois.
Magn Reson Imaging. 1991;9(4):611-20. doi: 10.1016/0730-725x(91)90049-r.
In the past several years, image processing techniques based on Bayesian models have received considerable attention. In our earlier work, we developed a novel Bayesian approach which was primarily aimed at the processing and reconstruction of images in positron emission tomography. In this paper, we describe how the technique has been adopted to process magnetic resonance images in order to reduce noise and artifacts, thereby improving image quality. In this framework, the image is assumed to be a statistical variable whose posterior probability density conditional on the observed image is modeled by the product of the likelihood function of the observed data with a prior density based our prior knowledge. A Gibbs random field incorporating local continuity information and with edge-detection capability is used as the prior model. Based on the formalism of the posterior density, we can compute an estimate of the image using an iterative technique. We have implemented this technique and applied it to phantom and clinical images. Our results indicate that the approach works reasonably well for reducing noise, enhancing edges, and removing ringing artifact.
在过去几年中，基于贝叶斯模型的图像处理技术受到了广泛关注。在我们早期的工作中，我们开发了一种新颖的贝叶斯方法，其主要目的是处理和重建正电子发射断层扫描中的图像。在本文中，我们描述了如何采用该技术来处理磁共振图像，以减少噪声和伪影，从而提高图像质量。在此框架下，图像被假定为一个统计变量，其基于观测图像的后验概率密度由观测数据的似然函数与基于我们先验知识的先验密度的乘积建模。一个包含局部连续性信息且具有边缘检测能力的吉布斯随机场被用作先验模型。基于后验密度的形式，我们可以使用迭代技术计算图像的估计值。我们已经实现了该技术并将其应用于体模和临床图像。我们的结果表明，该方法在减少噪声、增强边缘和去除振铃伪影方面效果良好。
