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
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.
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.
BibTeX: Download