Objective: In order to solve the problem of image quality degradation of CT reconstruction under sparse angle projection, we propose to develop and test a new sparse angle CT reconstruction method based on group sparse.
Methods: In this method, the group-based sparse representation is introduced into the statistical iterative reconstruction framework as a regularization term to construct the objective function. The group-based sparse representation no longer takes a single patch as the minimum unit of sparse representation, while it uses Euclidean distance as a similarity measure, thus it divides similar patch into groups as basic units for sparse representation. This method fully considers the local sparsity and non-local self-similarity of image. The proposed method is compared with several commonly used CT image reconstruction methods including FBP, SART, SART-TV and GSR-SART with experiments carried out on Sheep_Logan phantom and abdominal and pelvic images.
Results: In three experiments, the visual effect of the proposed method is the best. Under 64 projection angles, the lowest RMSE is 0.004776 and the highest VIF is 0.948724. FSIM and SSIM are all higher than 0.98. Under 50 projection angles, the index of the proposed method remains achieving the best image quality.
Conclusion: Qualitative and quantitative results of this study demonstrate that this new proposed method can not only remove strip artifacts, but also effectively protect image details.
Keywords: Computed tomography imaging; dictionary learning; group sparse representation; sparse angle.