基于MCMC方法的自适应低剂量CT图像去噪

针对低剂量CT图像的低信噪比的问题,提出了一种新的基于MCMC方法的低剂量CT投影图像的自适应降噪算法.该算法是在对投影图像先验模型中的平滑参数以及噪声方差进行自适应估计的基础上,求解理想投影图像在观察投影图像条件下的期望值,以此期望值作为理想投影图像的估计值,从而达到图像降噪的目的.其中对先验概率模型中的平滑参数以及非平稳噪声的方差在运用EM算法进行估计过程中,引入MCMC技术中的Gibbs采样,很好解决了参数估计中的计算问题,并在此基础上,通过再一次运用MCMC的Gibbs采样,以获得理想数据的条件期望值.计算机仿真实验以及真实投影图像的实验均表明了本文所提出的算法在低剂量CT图像降噪中能够取得良好的效果.

Adaptive Noise Reduction of Low-dose CT Sinograms Based on MCMC Method

Improving of the SNR of the low-dose CT image is a crucial issue for the low-dose CT application. In this paper, we proposed a novel adaptive noise reduction algorithm for low-dose CT sinogram based on the MCMC method. The algorithm first adaptively estimated the smoothness parameter of the priori model and the noise variance, and then utilized the conditional expectation of the noisy sinogram as the restored sinogram. The parameters were estimated by an EM algorithm and in this procedure a Gibbs sampler was used to draw samples from the local posterior distribution to handle the complicated computation problem, and then the Gibbs sampler was used once again to compute the conditional expectation of the noisy sinogram. The effectiveness of the proposed algorithm was validated by both computer simulations and experimental studies. The gain of the proposed approach over other methods was quantified by noise-resolution tradeoff curves.