  Reconstructing a dynamic object with affine motion in computerized tomography
(CT) leads to motion artifacts if the motion is not taken into account. In most
cases, the actual motion is neither known nor can be determined easily. As a
consequence, the respective model that describes CT is incomplete. The
iterative RESESOP-Kaczmarz method can - under certain conditions and by
exploiting the modeling error - reconstruct dynamic objects at different time
points even if the exact motion is unknown. However, the method is very
time-consuming. To speed the reconstruction process up and obtain better
results, we combine the following three steps: 1. RESESOP-Kacmarz with only a
few iterations is implemented to reconstruct the object at different time
points. 2. The motion is estimated via landmark detection, e.g. using deep
learning. 3. The estimated motion is integrated into the reconstruction
process, allowing the use of dynamic filtered backprojection. We give a short
review of all methods involved and present numerical results as a proof of
principle.
