  Image reconstruction from computed tomography (CT) measurement is a
challenging statistical inverse problem since a high-dimensional conditional
distribution needs to be estimated. Based on training data obtained from
high-quality reconstructions, we aim to learn a conditional density of images
from noisy low-dose CT measurements. To tackle this problem, we propose a
hybrid conditional normalizing flow, which integrates the physical model by
using the filtered back-projection as conditioner. We evaluate our approach on
a low-dose CT benchmark and demonstrate superior performance in terms of
structural similarity of our flow-based method compared to other deep learning
based approaches.
