  Synchrotron-based x-ray tomography is a noninvasive imaging technique that
allows for reconstructing the internal structure of materials at high spatial
resolutions from tens of micrometers to a few nanometers. In order to resolve
sample features at smaller length scales, however, a higher radiation dose is
required. Therefore, the limitation on the achievable resolution is set
primarily by noise at these length scales. We present \TOMOGAN{}, a denoising
technique based on generative adversarial networks, for improving the quality
of reconstructed images for low-dose imaging conditions. We evaluate our
approach in two photon-budget-limited experimental conditions: (1) sufficient
number of low-dose projections (based on Nyquist sampling), and (2)
insufficient or limited number of high-dose projections. In both cases the
angular sampling is assumed to be isotropic, and the photon budget throughout
the experiment is fixed based on the maximum allowable radiation dose on the
sample. Evaluation with both simulated and experimental datasets shows that our
approach can significantly reduce noise in reconstructed images, improving the
structural similarity score of simulation and experimental data from 0.18 to
0.9 and from 0.18 to 0.41, respectively. Furthermore, the quality of the
reconstructed images with filtered back projection followed by our denoising
approach exceeds that of reconstructions with the simultaneous iterative
reconstruction technique, showing the computational superiority of our
approach.
