基于参数估计的低剂量CT图像统计去噪

doi:10.3969/j.issn.1001-2400.2011.03.016

Abstract

Abstract:

Improvement of the SNR of low-dose CT images is a crucial issue for the low-dose CT application. In this paper, we propose a novel adaptive statistical noise reduction algorithm for low-dose CT sinogram. The algorithm first adopts an EM algorithm to adaptively estimate the parameters of the image model based on the non-stationary Gaussian noise property in the low-dose CT projection data, and then uses the MAP estimation to restore the sinogram. In the parameters estimation procedure, a Gibbs sampler is used to handle the complicated computation problem. In addition, two initialization strategies are used in the algorithm to accelerate the convergence speed too. The effectiveness of the proposed algorithm is validated by both computer simulations and experimental studies. The advantage of the proposed approach over other methods is quantified by noise-resolution tradeoff curves.

Key words: low-dose CT, noise reduction, parameter estimation, maximum a posteriori estimation

CLC Number:

TP391.41

ZHANG Yuanke；ZHANG Junying；LU Hongbing. Parameter estimation and statistical noise reduction for low-dose CT sinogram[J].J4, 2011, 38(3): 99-106.

References

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Parameter estimation and statistical noise reduction for low-dose CT sinogram

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