Deep Autofocus with Cone-Beam CT Consistency Constraint

Abstract

High quality reconstruction with interventional C-arm conebeam 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 %. This outperforms the entropy-based state-of-the-art autofocus measure which achieves on average an artifact suppression of 54 %.

A learning-based method for online adjustment of C-arm Cone-beam CT source trajectories for artifact avoidance

“…Instead, an online calibration procedure which does not require precise knowledge about the 3D structure would be desirable. Relying on the joint encodings of a fully robotic C-arm for initialization, further fine-tuning of the pose parameters could be performed in an image-based manner, e.g., using autofocus measures [20]. To ensure robust and precise network predictions in a clinical environment, important steps are the generation of synthetic training data, which is representative of the variety of different anatomies and tools as well as views onto these.…”