3-D Computed Laminography Based on Prior Images and Total Variation
Zhiguo Gui, Yuhang Liu, Yi Liu, Wenting Liu, Rongbiao Yan, Lei Wang, Pengcheng Zhang, Quan Zhang, Huazhong Shu
Abstract
The 3-D sparse-view computed laminography (CL) is an important research issue due to its considerable potential in lowering costs. To solve the artifacts and noise caused by sparse-view scanning, the total variation (TV) method is widely used. However, TV only utilizes the prior information of the reconstructed image itself. Therefore, the prior-image-based reconstruction method (PIBR) is proposed to reduce the artifacts by using a prior image. In this study, we proposed a 3-D CL method based on the final prior image and TV for sparse-view scanning, which is divided into two subproblems corresponding to two solution steps: the novel PIBR step and the denoising step. In the first step, we devise the final prior image and its corresponding prior mask to ensure the effectiveness of the PIBR. The final prior image is obtained based on the prior images, which are reconstructed from two different oblique angle projections. The prior mask is a binary mask, which is used to record the position of the prior image information. Then, the denoising step is based on the TV minimization method using the split-Bregman (SB) frame. Soft shrinkage operations and fast Fourier transform (FFT) are used to efficiently implement the SB frame. Compared with several iterative reconstruction methods, the experimental results demonstrate the effectiveness of the proposed method in terms of preserving edges, suppressing interslices aliasing and denoising.