Iterative CT Reconstruction Using

Abstract. There is a critical need to reconstruct clinically usable images at a low dose. One way of achieving this is to reconstruct with as few pro-jections as possible. Due to the undersampling, streak artifacts degrade image quality for traditional CT reconstruction. Compressed sensing (CS) [1] theory uses sparsity as a prior and improves the reconstruc-tion quality considerably using only few projections. CS formulates the reconstruction problem to an optimization problem. The objective func-tion consists of one data fidelity term and one regularization term which enforce the sparsity under a certain sparsifying transform. Curvelet is an effective sparse representation for objects [2]. In this work, we in-troduce to use curvelet as the sparsifying transform in the CS based re-construction framework. The algorithm was evaluated with one physical phantom dataset and one in vitro dataset and was compared against and two state-of-art approach, namely, wavelet-based regularization (WR) [3] and total variation based regularization methods (TVR) [4]. The results show that the reconstruction quality of our approach is superior to the reconstruction quality of WR and TVR.