A springback TV algorithm for image reconstruction from sparse view data in CT

OBJECTIVE: With the demand for reducing ray dose, image reconstruction from sparse-view projection data has become a hot research subject in computed tomography (CT). However, the traditional total variation (TV) algorithms based onℓ1norm may lead to over-smoothing, especially when handling extremely sparse projections. To address this issue, we propose a new TV algorithm formulated by the springback penalty, named springback TV (STV) algorithm. APPROACH: The STV model replaces the simpleℓ1norm approximation with a weakly convex penalty to better approximate theℓ0norm and enhance sparsity representation. Furthermore, we employ the difference of convex algorithm (DCA) and the fully linearized alternating direction method of multipliers (FL-ADMM) to efficiently solve the STV model, avoiding line search steps and accelerating internal iterations. MAIN RESULTS: Experiments based on mathematical phantoms and clinical CT image demonstrate that the proposed STV algorithm outperforms standard TV, Total p-Variation (TpV) and weighted difference of anisotropic and isotropic TV (WDAI-TV) in terms of reconstruction quality. SIGNIFICANCE: The proposed STV algorithm improves detail recovery while maintaining computational efficiency, providing a more robust and effective solution for sparse view CT image reconstruction under low-dose conditions.