A Self-supervised Deep Learning Network for Low-Dose CT Reconstruction

Low dose CT is of great interest in these days. Dose reduction raises noise level in projection and decrease image quality in reconstructions. Model based image reconstructions can combine noise statistical model together with prior knowledge in an optimization problem so that significantly reduce noise and artefacts. In this work, we propose a deep learning neural network for low lose CT reconstruction in the sense of penalized weighted least-squares (PWLS) so that a self-supervised learning can be done with no ground-truth information needed. Instead of minimizing cost function for each image, the network learns to minimize the cost function for the whole training set. Features learned can be robust. No iterative processes are needed for real data reconstruction with such a trained network. We carried on our experiments on data from a practical dental CT. Applausible reconstructions with great noise reductions are obtained.