  Over the last years, deep learning methods have become an increasingly
popular choice to solve tasks from the field of inverse problems. Many of these
new data-driven methods have produced impressive results, although most only
give point estimates for the reconstruction. However, especially in the
analysis of ill-posed inverse problems, the study of uncertainties is
essential. In our work, we apply generative flow-based models based on
invertible neural networks to two challenging medical imaging tasks, i.e.
low-dose computed tomography and accelerated medical resonance imaging. We test
different architectures of invertible neural networks and provide extensive
ablation studies. In most applications, a standard Gaussian is used as the base
distribution for a flow-based model. Our results show that the choice of a
radial distribution can improve the quality of reconstructions.
