Correlation between external and internal respiratory motion: A validation study

Floris Ernst*, Ralf Bruder, Alexander Schlaefer, Achim Schweikard

*Korrespondierende/r Autor/-in für diese Arbeit
19 Zitate (Scopus)


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 ε-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 1.5mm 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.

ZeitschriftInternational Journal of Computer Assisted Radiology and Surgery
Seiten (von - bis)483-492
PublikationsstatusVeröffentlicht - 01.01.2012


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