CT image reconstruction from sparse projections using adaptive total generalised variation with soft thresholding

Abstract

CT imaging plays a vital role in non-invasive diagnosis and in surgical planning of critical diseases. It is essential to reduce radiation dose during CT imaging as excessive exposure may cause harm to human tissues. To reduce radiation dose, CT image is acquired using limited number of X-ray projections and then to reconstruct the image an adaptive total generalised variation (TGV) minimisation method has been proposed. The simulation results have been compared with the existing TV, TGV and TGV with hard thresholding methods. Typically two types of noises, Gaussian and Poisson distributed noises, are introduced during CT imaging process. So, these two types of noises have been added in measured samples. It is found that after applying soft thresholding and FISTA algorithm with the proposed method, better results have been obtained in noisy imaging environment. The reconstructed CT image quality has been compared using parameters like FSSIM, PSNR, NRMSE and MAE.