  High quality reconstruction with interventional C-arm cone-beam computed
tomography (CBCT) requires exact geometry information. If the geometry
information is corrupted, e. g., by unexpected patient or system movement, the
measured signal is misplaced in the backprojection operation. With prolonged
acquisition times of interventional C-arm CBCT the likelihood of rigid patient
motion increases. To adapt the backprojection operation accordingly, a motion
estimation strategy is necessary. Recently, a novel learning-based approach was
proposed, capable of compensating motions within the acquisition plane. We
extend this method by a CBCT consistency constraint, which was proven to be
efficient for motions perpendicular to the acquisition plane. By the
synergistic combination of these two measures, in and out-plane motion is well
detectable, achieving an average artifact suppression of 93 [percent]. This
outperforms the entropy-based state-of-the-art autofocus measure which achieves
on average an artifact suppression of 54 [percent].
