Automatic regularization parameter tuning based on CT Image statistics

Abstract

Regularization parameter selection is pivotal in optimizing reconstructed images which controls a balance between fidelity and penalty term. Images reconstructed with the optimal regularization parameter will keep the detail preserved and the noise restrained at the same time. In previous work, we have used CT image statistics to select the optimal regularization parameter by calculating the second order derivates of image variance (Soda-curve). But same as L-curve method, it also needs multiple reconstruction in different regularization parameters which will spend plenty of time. In this paper, we dive into the relationship between image statistics changes and regularization parameter during the iteration. Meanwhile, we propose a method based on the empirical regularity found in the iterations to tune the regularization parameter automatically in order to maintain the image quality. Experiments show that the images reconstructed with the regularization parameters tuned by the proposed method have higher image quality as well as less time when compared to L-curve based results.