Image reconstruction method for limited-angle CT based on total variation minimization using guided image filtering

Radiation is harmful to the human body, which is coupled with the fact that scanning conditions pose a number of restrictions. As a result, the projection data of a scanned object are generally acquired within a limited-angle range in practical computed tomography (CT) applications. Under this circumstance, classical image reconstruction methods cannot obtain high-quality images, and limited-angle artifacts appear in the reconstructed image. In recent years, the l₁ norm of a gradient image-based total variation minimization (TVL1) image reconstruction method has often been used to deal with the image reconstruction problem from undersampling projection data, but limited-angle artifacts have been encountered near the edges for limited-angle CT. The l₀ norm of a gradient image-based total variation minimization (TVL0) image reconstruction method can better preserve the edges, but it cannot obtain acceptable results when the scanning angle range is further reduced. Inspired by the advantages of guided image filtering (GIF), which can better smooth an image and preserve its structure, we used it to improve the reconstructed image quality for limited-angle CT by transferring reconstructed results of the TVL1 method to those of the TVL0 method. Simulation experiments show that the proposed method can better preserve structures and suppress limited-angle artifacts and noise than several related reconstruction methods.