Multi-stage Deep Learning Artifact Reduction for Computed Tomography
Jiayang Shi Daniel M. Pelt K. Joost Batenburg
In Computed Tomography (CT), an image of the interior structure of an object
is computed from a set of acquired projection images. The quality of these
reconstructed images is essential for accurate analysis, but this quality can
be degraded by a variety of imaging artifacts. To improve reconstruction
quality, the acquired projection images are often processed by a pipeline
consisting of multiple artifact-removal steps applied in various image domains
(e.g., outlier removal on projection images and denoising of reconstruction
images). These artifact-removal methods exploit the fact that certain artifacts
are easier to remove in a certain domain compared with other domains.
  Recently, deep learning methods have shown promising results for artifact
removal for CT images. However, most existing deep learning methods for CT are
applied as a post-processing method after reconstruction. Therefore, artifacts
that are relatively difficult to remove in the reconstruction domain may not be
effectively removed by these methods. As an alternative, we propose a
multi-stage deep learning method for artifact removal, in which neural networks
are applied to several domains, similar to a classical CT processing pipeline.
We show that the neural networks can be effectively trained in succession,
resulting in easy-to-use and computationally efficient training. Experiments on
both simulated and real-world experimental datasets show that our method is
effective in reducing artifacts and superior to deep learning-based
post-processing.