A few-view reweighted sparsity hunting (FRESH) method for CT image reconstruction

In recent years, the total variation (TV) minimization method has been widely used for compressed sensing (CS) based CT image reconstruction. In this paper, we propose a few-view reweighted sparsity hunting (FRESH) method for CT image reconstruction, and demonstrate the superior performance of this method. Specifically, the key of the purposed method is that a reweighted total variation (RwTV) measure is used to characterize image sparsity in the cost function, outperforming the conventional TV counterpart. To solve the RwTV minimization problem efficiently, the Split-Bregman method and other state-of-the-art L1 optimization methods are compared. Inspired by the fast iterative shrinkage/thresholding algorithm (FISTA), a predication step is incorporated for fast computation in the Split-Bregman framework. Extensive numerical experiments have shown that our FRESH approach performs significantly better than competing algorithms in terms of image quality and convergence speed for few-view CT. High-quality images were reconstructed by our FRESH method after 250 iterations using only 15 few-view projections of the Forbild head phantom while other competitors needed more than 800 iterations. Remarkable improvements in details in the experimental evaluation using actual sheep thorax data further indicate the potential real-world application of the FRESH method.