Abstract
A novel iterative algorithm is proposed for sparse-view cone beam computed tomography (CBCT) reconstruction based on the weighted Schatten p-norm minimization (WSNM). By using the half quadratic splitting, the sparse-view CBCT reconstruction task is decomposed into two sub-problems that can be solved through alternating iteration: simple reconstruction and image denoising. The WSNM that fits well with the low-rank hypothesis of CBCT data is introduced to improve the denoising sub-problem as a regularization term. The experimental results based on the digital brain phantom and clinical CT data indicated the advantages of the proposed algorithm in both structural information preservation and artifacts suppression, which performs better than the classical algorithms in quantitative and qualitative evaluations.
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