Novel U-net based deep neural networks for transmission tomography.

Background: The fusion of computer tomography and deep learning is an effective way of achieving improved image quality and artifact reduction in reconstructed images.

Objective: In this paper, we present two novel neural network architectures for tomographic reconstruction with reduced effects of beam hardening and electrical noise.

Methods: In the case of the proposed novel architectures, the image reconstruction step is located inside the neural networks, which allows the network to be trained by taking the mathematical model of the projections into account.