Zusammenfassung
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 effcient 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%.
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© 2020 Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
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Preuhs, A. et al. (2020). Deep Autofocus with Cone-Beam CT Consistency Constraint. In: Tolxdorff, T., Deserno, T., Handels, H., Maier, A., Maier-Hein, K., Palm, C. (eds) Bildverarbeitung für die Medizin 2020. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-29267-6_34
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DOI: https://doi.org/10.1007/978-3-658-29267-6_34
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