Objectives: In the past, guided image filtering (GIF)-based methods often utilized total variation (TV)-based methods to reconstruct guidance images. And they failed to reconstruct the intricate details of complex clinical images accurately. To address these problems, we propose a new sparse-view CT reconstruction method based on group-based sparse representation using weighted guided image filtering.
Methods: In each iteration of the proposed algorithm, the result constrained by the group-based sparse representation (GSR) is used as the guidance image. Then, the weighted guided image filtering (WGIF) was used to transfer the important features from the guidance image to the reconstruction of the SART method.
Results: Three representative slices were tested under 64 projection views, and the proposed method yielded the best visual effect. For the shoulder case, the PSNR can achieve 48.82, which is far superior to other methods.
Conclusions: The experimental results demonstrate that our method is more effective in preserving structures, suppressing noise, and reducing artifacts compared to other methods.
Keywords: SART; computed tomography (CT); group-sparsity regularization (GSR); sparse-view; weighted guided image filtering (WGIF).
© 2024 Walter de Gruyter GmbH, Berlin/Boston.