Deep autofocus with cone-beam CT consistency constraint
This work addresses motion artifacts in medical imaging for interventional CBCT, offering a significant improvement over existing methods, though it is incremental as it builds on prior learning-based approaches.
The paper tackles the problem of rigid patient motion during interventional C-arm CBCT acquisition, which corrupts geometry information and degrades reconstruction quality. By extending a learning-based method with a CBCT consistency constraint, it achieves an average artifact suppression of 93%, outperforming the state-of-the-art entropy-based autofocus measure at 54%.
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].