A Kernel-based Framework for Intra-fractional Respiratory Motion Estimation in Radiation Therapy

Geimer T, Unberath M, Birlutiu A, Taubmann O, Wölfelschneider J, Bert C, Maier A (2017)


Publication Language: English

Publication Type: Conference contribution

Publication year: 2017

Publisher: IEEE Computer Society

Edited Volumes: Proceedings - International Symposium on Biomedical Imaging

Pages Range: 1036-1039

Conference Proceedings Title: Proceedings of the 2017 IEEE International Symposium on Biomedical Imaging

Event location: Melbourne, Australia

ISBN: 9781509011711

URI: https://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2017/Geimer17-AKF.pdf

DOI: 10.1109/ISBI.2017.7950693

Abstract

In radiation therapy, tumor tracking allows to adjust the beam such that it follows the respiration-induced tumor motion. However, most clinical approaches rely on implanted fiducial markers to locate the tumor and, thus, only provide sparse information. Motion models have been investigated to estimate dense internal displacement fields from an external surrogate signal, such as range imaging. With increasing surrogate complexity, we propose a respiratory motion estimation framework based on kernel ridge regression to cope with high-dimensional domains. This approach was validated on five patient datasets, consisting of a planning 4DCT and a follow-up 4DCT for each patient. Mean residual error was at best 2.73 ± 0.25 mm, but varied greatly between patients.

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APA:

Geimer, T., Unberath, M., Birlutiu, A., Taubmann, O., Wölfelschneider, J., Bert, C., & Maier, A. (2017). A Kernel-based Framework for Intra-fractional Respiratory Motion Estimation in Radiation Therapy. In Proceedings of the 2017 IEEE International Symposium on Biomedical Imaging (pp. 1036-1039). Melbourne, Australia: IEEE Computer Society.

MLA:

Geimer, Tobias, et al. "A Kernel-based Framework for Intra-fractional Respiratory Motion Estimation in Radiation Therapy." Proceedings of the 2017 IEEE International Symposium on Biomedical Imaging, Melbourne, Australia IEEE Computer Society, 2017. 1036-1039.

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