Publikationen Mitarbeiter
- Achim Schweikard
- Ralf Bruder
- Sven Böttger
- Floris Ernst
- Timo Hackbarth
- Jannis Hagenah
- Felix von Haxthausen
- Svenja Ipsen
- Phillip Jauer
- Ivo Kuhlemann
- Elmar Rückert
- Verónica García Vázquez
Publikationen ehemalige Mitarbeiter
- Mohammed Al-Badri
- Rehman Ansari
- Norbert Binder
- Christoph Bodensteiner
- Robert Dürichen
- Markus Finke
- Fernando Gasca
- Hamid Hadjar
- Maximilian Heinig
- Volker Martens
- Lars Matthäus
- Christoph Metzner
- Lukas Ramrath
- Lars Richter
- Alexander Schlaefer
- Osama Shahin
- Birgit Stender
- Patrick Stüber
- Tiberiu Viulet
- Benjamin Wagner
- Christian Wilde
- Tobias Wissel
Correlation between external and internal respiratory motion: a validation study
| Type of publication: | Artikel |
| Zeitschrift: | International Journal of Computer Assisted Radiology and Surgery |
| Band: | 7 |
| Nummer: | 3 |
| Jahr: | 2012 |
| Seiten: | 483-492 |
| Verlag: | Springer |
| Notiz: | Motion Compensation in Radiosurgery |
| DOI: | 10.1007/s11548-011-0653-6 |
| Abriss: | Purpose In motion-compensated image-guided radiotherapy, accurate tracking of the target region is required. This tracking process includes building a correlation model between external surrogate motion and the motion of the target region. A novel correlation method is presented and compared with the commonly used polynomial model. Methods and Materials The CyberKnife system (Accuray, Inc., Sunnyvale/CA) uses a polynomial correlation model to relate externally measured surrogate data (optical fibres on the patient’s chest emitting red light) to infrequently acquired internal measurements (X-ray data). A new correlation algorithm based on $${\varepsilon}$$ -Support Vector Regression (SVR) was developed. Validation and comparison testing were done with human volunteers using live 3D ultrasound and externally measured infrared light-emitting diodes (IR LEDs). Seven data sets (5:03–6:27 min long) were recorded from six volunteers. Results Polynomial correlation algorithms were compared to the SVR-based algorithm demonstrating an average increase in root mean square (RMS) accuracy of 21.3% (0.4 mm). For three signals, the increase was more than 29% and for one signal as much as 45.6% (corresponding to more than 1.5 mm RMS). Further analysis showed the improvement to be statistically significant. Conclusion The new SVR-based correlation method outperforms traditional polynomial correlation methods for motion tracking. This method is suitable for clinical implementation and may improve the overall accuracy of targeted radiotherapy. |
| Nutzerfelder: | file={ebss_12b.pdf:ebss_12b.pdf:PDF} |
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