A few-view ct reconstruction method based on a dynamic two-step sart-tv iterative algorithm

. Few-view CT reconstruction can effectively reduce the radiation dose and the positioning requirements required during CT imaging. The algebraic reconstruction technique (ART) based on the total variation (TV) is an effective reconstruction method. It introduces the TV constraint using a basic reconstruction algorithm to improve the reconstructed image details. However, the reconstruction speed of this method is not satisfying, and the reconstruction quality needs to be further improved. In this paper, we propose an iterative reconstruction method based on a dynamic two-step simultaneous ART-TV. It adopts a double-loop structure similar to ART-TV. In this proposed method, the simultaneous ART is used to obtain the initial iteration value of the inner loop, the iterative reweighted shrinkage (IRS) is introduced into the TV gradient descent to solve the TV-norm optimization problem in the inner loop, and a dynamic relaxation factor is used to further improve the convergence speed. The experimental results show that, compared with SART, simultaneous ART-TV, and soft-threshold filtering, the proposed method effectively improves the global convergence speed, and the reconstructed CT image is qualitatively and quantitatively better. The details of the reconstructed images can be effectively improved by using TV constraints, and the dynamic relaxation factor further improves the overall convergence speed. The proposed method has faster convergence speed and better image reconstruction performance.