Synthesis of CT images from digital body phantoms using CycleGAN.

Purpose: The potential of medical image analysis with neural networks is limited by the restricted availability of extensive data sets. The incorporation of synthetic training data is one approach to bypass this shortcoming, as synthetic data offer accurate annotations and unlimited data size.

Methods: We evaluated eleven CycleGAN for the synthesis of computed tomography (CT) images based on XCAT body phantoms.

Simulation-based deep artifact correction with Convolutional Neural Networks for limited angle artifacts.

Non-conventional scan trajectories for interventional three-dimensional imaging promise low-dose interventions and a better radiation protection to the personnel. Circular tomosynthesis (cTS) scan trajectories yield an anisotropical image quality distribution. In contrast to conventional Computed Tomographies (CT), the reconstructions have a preferred focus plane.