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

Abstract

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. In the other two perpendicular planes, limited angle artifacts are introduced. A reduction of these artifacts leads to enhanced image quality while maintaining the low dose. We apply Deep Artifact Correction (DAC) to this task. cTS simulations of a digital phantom are used to generate training data. Three U-Net-based networks and a 3D-ResNet are trained to estimate the correction map between the cTS and the phantom. We show that limited angle artifacts can be mitigated using simulation-based DAC. The U-Net-corrected cTS achieved a Root Mean Squared Error (RMSE) of 124.24 Hounsfield Units (HU) on 60 simulated test scans in comparison to the digital phantoms. This equals an error reduction of 59.35% from the cTS. The achieved image quality is similar to a simulated cone beam CT (CBCT). Our network was also able to mitigate artifacts in scans of objects which strongly differ from the training data. Application to real cTS test scans showed an error reduction of 45.18% and 26.4% with the 3D-ResNet in reference to a high-dose CBCT.