A deep learning architecture for limited-angle computed tomography reconstruction

Kerstin Hammernik, Tobias Würfl, Thomas Pock, Andreas Maier

Abstract:

Limited-angle computed tomography suffers from missing
data in the projection domain, which results in intensity inhomogeneities
and streaking artifacts in the image domain. We address both challenges
by a two-step deep learning architecture: First, we learn compensation
weights that account for the missing data in the projection domain and
correct for intensity changes. Second, we formulate an image restoration problem as a variational network to eliminate coherent streaking
artifacts. We perform our experiments on realistic data and we achieve
superior results for destreaking compared to state-of-the-art non-linear
filtering methods in literature. We show that our approach eliminates the
need for manual tuning and enables joint optimization of both correction
schemes.