Variational Bayesian blind restoration reconstruction based on shear wave transform for low-dose medical CT image

Yunshan Sun,Teng Fei,Xiaopei Liu,Liyi Zhang

doi:10.1186/s13640-017-0234-x

Abstract

A variational Bayesian blind restoration reconstruction based on shear wave transform for low-dose medical computed tomography (CT) image is proposed. The proposed algorithm eliminates the effects of the point spread function in the process of low-dose medical CT image reconstruction and improves the reconstructed image quality. The shear wave transform is used to sparsely represent the CT image, which can speed up the efficiency of image processing. In the Bayesian framework, a posteriori probability objective function with unknown parameters is constructed. These unknown parameters include the shear wave coefficients, the point spread function, and the hyper parameters. It specified a Laplacian distribution model for the prior probability distribution of the shear wave coefficients. The autoregressive model is used as the prior model of the point spread function. All of hyper parameters follow the gamma distribution. The variational Bayesian method is used to estimate all of unknown parameters and solve the above posteriori probability objective function. These generalized parameter estimators are used to realize the low-dose CT image blind restoration reconstruction. Computer simulation results indicate that a good performance-reconstructed image can be obtained and some metrics such as peak signal-to-noise ratio (PSNR), universal image quality index (UIQI), structural similarity index metric (SSIM), and sum of square differences error (SSDE) are improved.

Highlights

Previous studies have established that high doses of X-ray radiation can cause cancer, leukemia, or other genetic diseases [1,2,3]
This paper proposes a variational Bayesian blind restoration reconstruction based on shear wave transform for low-dose medical computed tomography (CT) image
4 Conclusion This paper proposes a variational Bayesian blind restoration reconstruction based on shear wave transform for low-dose medical CT image

Summary

Introduction

Previous studies have established that high doses of X-ray radiation can cause cancer, leukemia, or other genetic diseases [1,2,3]. Low-dose medical CT imaging is to reconstruct CT image by incomplete projection data or coarse data. Some reconstruction methods based on the Nyquist sampling theorem restrict low-dose medical CT reconstruction by incomplete projection data. In the process of CT image formation, it is often affected by the point spread function of the system This will result in image degradation and artifacts. When the image is reconstructed from incomplete projection data, The basic idea of compress sensing (CS) is that if there is a sparse signal or the signal is sparse in a transform domain, the original signal can be reconstructed through a small amount of samples with high precision [4, 5].

Methods

Discussion

Conclusion