Limited-Angle Tomography Reconstruction via Deep End-To-End Learning on
  Synthetic Data
Thomas Germer Jan Robine Sebastian Konietzny Stefan Harmeling Tobias Uelwer
Computed tomography (CT) has become an essential part of modern science and
medicine. A CT scanner consists of an X-ray source that is spun around an
object of interest. On the opposite end of the X-ray source, a detector
captures X-rays that are not absorbed by the object. The reconstruction of an
image is a linear inverse problem, which is usually solved by filtered back
projection. However, when the number of measurements is small, the
reconstruction problem is ill-posed. This is for example the case when the
X-ray source is not spun completely around the object, but rather irradiates
the object only from a limited angle. To tackle this problem, we present a deep
neural network that is trained on a large amount of carefully-crafted synthetic
data and can perform limited-angle tomography reconstruction even for only
30{\deg} or 40{\deg} sinograms. With our approach we won the first place in the
Helsinki Tomography Challenge 2022.