Abstract: Learning to avoid poor images: towards task-aware c-arm cone-beam ct trajectories
Zaech JN, Gao C, Bier B, Taylor R, Maier A, Navab N, Unberath M (2020)
Publication Type: Conference contribution
Publication year: 2020
Journal
Publisher: Springer
Pages Range: 185-
Conference Proceedings Title: Informatik aktuell
Event location: Berlin
ISBN: 9783658292669
DOI: 10.1007/978-3-658-29267-6_39
Abstract
Metal artifacts in computed tomography (CT) arise from a mismatch between physics of image formation and idealized assumptions during tomographic reconstruction. These artifacts are particularly strong around metal implants, inhibiting widespread adoption of 3D cone-beam CT (CBCT) despite clear opportunity for intra-operative verification of implant positioning, e. g. in spinal fusion surgery. On synthetic and real data, we demonstrate that much of the artifact can be avoided by acquiring better data for reconstruction in a task-aware and patient-specific manner, and describe the first step towards the envisioned task-aware CBCT protocol.
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APA:
Zaech, J.N., Gao, C., Bier, B., Taylor, R., Maier, A., Navab, N., & Unberath, M. (2020). Abstract: Learning to avoid poor images: towards task-aware c-arm cone-beam ct trajectories. In Thomas Tolxdorff, Thomas M. Deserno, Heinz Handels, Andreas Maier, Klaus H. Maier-Hein, Christoph Palm (Eds.), Informatik aktuell (pp. 185-). Berlin: Springer.
MLA:
Zaech, Jan Nico, et al. "Abstract: Learning to avoid poor images: towards task-aware c-arm cone-beam ct trajectories." Proceedings of the International workshop on Algorithmen - Systeme - Anwendungen, 2020, Berlin Ed. Thomas Tolxdorff, Thomas M. Deserno, Heinz Handels, Andreas Maier, Klaus H. Maier-Hein, Christoph Palm, Springer, 2020. 185-.
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