4D-CT Reconstrucion Using Sparsity Level Constrained Compressed Sensing

4D-CT is an important tool for treatment simulation and treatment planning in radiotherapy. In order to capture the tumor and tissue movement over time, 4D-CT has to acquire more projection images compared to 3D-CT. This leads to more radiation dose, which is the main concern of the application. Using fewer projections can reduce the radiation dose. However, lack of projections degrades the reconstructed image quality for traditional methods. In this paper, we propose a novel method based on iterative hard thresholding and compressed sensing. We combine the prior knowledge from both methods in our reconstruction problem formulation. In the experiments, we validate our method with XCAT phantom data. The Euclidean norm of the reconstructed images and the ground truth are calculated for evaluation. The results show that our method outperforms the traditional reconstruction method.