Self-guided image filtering reconstruction algorithm based on the ASD-POCS algorithm framework

Computed Tomography (CT) technology is widely applied in the fields of medical imaging and industrial non-destructive testing.However,its application is often limited by high radiation doses or long scanning times.Sparse-angle and limited-angle sampling strategies can effectively mitigate these issues.However,in medical diagnosis,implementing sparse-angle sampling requires frequent switching of tube voltage or pre-collimators,which involves complex technology.Although limited-angle sampling is easier to implement,the high correlation between the acquired projection data often leads to noticeable artifacts in reconstructed images.The segmental limited-angle (SLA) sampling technique circumvents the rapid switching of voltage or pre-collimators,reduces the correlation of the projection data,and helps improve image quality.Nevertheless,SLA projection data still inherits some characteristics of limited-angle projection data,and reconstructed images may still exhibit significant artifacts.In this paper,we propose an algorithm for solving a self-guided image filtering (SGIF) reconstruction model within the framework of the adaptive steepest descent-projection onto convex sets (ASD-POCS) algorithm.Numerical simulations demonstrate that the proposed algorithm effectively improves the quality of SLA CT images.