Compressive Computed Tomography Image Reconstruction with Denoising Message Passing Algorithms

In this work the reconstruction of images from a limited number of projections is considered in order to achieve X-ray radiation dose reduction in Computed Tomography (CT) while achieving high diagnostic performances. The feasibility of message passing Compressive Sensing (CS) imaging algorithms to CT image reconstruction has been studied, aiming to extend the algorithm from its theoretical domain of i.i.d. random matrices. We have proposed a denoising-based Turbo CS algorithm (D-Turbo) and we have extended the application of the denoising approximate message passing (D-AMP) algorithm to partial Radon Projection data, exploiting a generic denoiser in a CS reconstruction algorithm. The proposed CS message passing approaches have been tested on simulated CT data using the BM3D denoiser yielding an improvement in the reconstruction quality compared to existing direct and iterative methods. The results show the effectiveness of using a generic denoiser Turbo CS or message passing algorithm to few projections CT reconstruction.