Zusammenfassung
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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Zaech JN, Gao C, Bier B, et al. Learning to avoid poor images: towards task-aware c-arm cone-beam CT trajectories. In: Proc – MICCAI 2019. Cham: Springer International Publishing; 2019. p. 11–19.
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© 2020 Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
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Zaech, JN. et al. (2020). Abstract: Learning to Avoid Poor Images. 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_39
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DOI: https://doi.org/10.1007/978-3-658-29267-6_39
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