Motion Compensation in Radiotherapy

Project Description

CyberKnife
CyberKnife

In radiosurgery, the accurate targeting of tumors anywhere in the body has become possible since several years. Current clinical applications manage to deliver a lethal dose of radiation to the cancerous region with an accuracy of about 2-3mm. Nevertheless, the tumour motion (induced by breathing, hearth beat or shifting of the patient) has to be compensated to be able to perform a precise irradiation. Conventional approaches are based on gating techniques, irradiation of tumour at specific phases of the respiration, or increasing of the target volume, until the complete tumour movement is covered.

An approach, developped in a collaboration of Prof. Schweikard and accuray Inc., Sunnyvale, CA, deals with this problem by tracking the motion of the patient's chest or abdomen using stereoscopic infrared camera systems. A mathematic model, the correlation model, can be computed based on the information of the external surrogates, which allows conclusions of the actual tumour movement. A robot based radiotherapy system, as e.g. the CyberKnife system, can use this information to compensate patient and respiration movements in real time.

Areas of Research:

Prediction of Breathing Motion:

Error surface of three LMS-based prediction algorithms
Error surface of three LMS-based prediction algorithms

A new problem arising from this approach is the fact that neither the recording of the patient's position nor the repositioning of the robotic system is instantaneous. Currently employed systems exhibit delays between approximately 65 and 300 ms. This results in targeting errors of up to several millimeters. The systematic error can be reduced by time series prediction of the external surrogates. Beside the classical regression approaches, as e.g. the least mean square algorithm, current research focuses on machine learning approaches based on kernel methods and statistical learning. The continuous improvement of these algorithms is one main topic of this research project.

Camera setup to capture respiratory motion

In our lab, we also measure actual human respiration. To do so, 20 infrared LEDs were attached to the chest of a test person. These LEDs were subsequently tracked using a high-speed IR tracking system (atracsys accuTrack compact). To be able to accurately position the camera and to ensure camera stability, the camera was mounted on a robotic arm. A short sequence of the respiratory motion recorded can be seen in the following movie.

Camera setup to capture respiratory motion
Camera setup to capture respiratory motion

In our lab, we also measure actual human respiration. To do so, 20 infrared LEDs were attached to the chest of a test person. These LEDs were subsequently tracked using a high-speed IR tracking system (atracsys accuTrack compact). To be able to accurately position the camera and to ensure camera stability, the camera was mounted on a robotic arm. A short sequence of the respiratory motion recorded can be seen in the following movie.

Detection of Tumor Motion (Correlation Models):

Once the motion of the patient's chest is known, conclusions about the position of the tumor are drawn. This is done by using a correlation model mimicking the relation between surface motion and target motion. How this model is constructed and validated is also a matter of ongoing research.

Multivariate Motion Compensation:

Current clinical praxis is the use of three optical infrared markers, which can be placed at any position of the chest or abdomen of the patient. As several studies have indicated, the correlation accuracy depends significantly on the marker placement and on the breathing characteristics of the patient. We investigate how this dependency can be reduced using multivariate measurement setups, e.g. acceleration, strain, air flow, surface electromyography (EMG). Aim of this research is the development of multi-modal prediction and correlation models. Special focus is placed on real time feature detection algorithms to detect the most relevant and least redundant sensors to increase the robustness of the complete system.

a) Sensor setup of a multivariate measurement with flow sensor (FLOW), optical marker 1-3 (OM 1-3), acceleration sensor (ACC), strain sensor (STRAIN) and ultrasound transducer (US),
b) Example of an ultrasound image and the selected target area (red dot) in the liver,
c) mean absolute correlation coefficients and standard deviation of all external sensors with respect to OM1, OM3 and US.

Probabilistic Motion Compensation:

Up to this point, surrogate based motion compensation requires a prediction and correlation model. The two models are used in sequence, meaning that the output of first model is used as the input to the second model (the order is arbitrary). Consequently, errors associated with the first model influence the result of the second model. In this context, Multi-Task Gaussian Process (MTGP) models have been investigated. These models offer for the first time the possibility to solve efficiently both problems and within one model. Studies have shown that this lead to a reduction of the total error. MTGP models are an extension of Gaussian Processes models, which are frequently used within the field of machine learning for regression tasks. The essential advantage of MTGPs is that multiple signals which are acquired at different sampling frequencies (even discrete time points) can be modelled simultaneously. The prediction accuracy is increased as the correlation between the signals is learned automatically.

MTGP Toolbox

The MTGP framework is very flexible and can be used for various biomedical problems as for instance the analysis of vital-sign data of intensive care unit patients. In cooperation with the Computational Health Informatics Lab (University of Oxford) a Matlab toolbox was developed. A detailed description of the toolbox and several illustrative examples can be found here. [Link Toolbox]

Modelling of multivariate signals with arbitrary sampling rates using MTGPs.
Modelling of multivariate signals with arbitrary sampling rates using MTGPs.

Publications

2009

Floris Ernst, and Achim Schweikard,
Forecasting Respiratory Motion with Accurate Online Support Vector Regression (SVRpred), International Journal of Computer Assisted Radiology and Surgery , vol. 4, no. 5, pp. 439-447, 2009.
DOI:10.1007/s11548-009-0355-5
File: s11548-009-0355-5
Floris Ernst, Volker Martens, Stefan Schlichting, Armin Beširević, Markus Kleemann, Christoph Koch, Dirk Petersen, and Achim Schweikard,
Correlating Chest Surface Motion to Motion of the Liver using ε-SVR -- a Porcine Study, Yang, Guang-Zhong and Hawkes, David J. and Rueckert, Daniel and Noble, Alison and Taylor, C., Eds. London, United Kingdom; London: Springer, 2009. pp. 356-364.
DOI:10.1007/978-3-642-04271-3_44
File: 978-3-642-04271-3_44
Floris Ernst, and Achim Schweikard,
A Survey of Algorithms for Respiratory Motion Prediction in Robotic Radiosurgery, Fischer, Stefan and Maehle, Erik, Eds. Lübeck, Germany: Bonner Köllen, 2009. pp. 1035-1043.
File:
Ralf Bruder, T. Cai, Floris Ernst, and Achim Schweikard,
3D ultrasound-guided motion compensation for intravascular radiation therapy, Berlin, Germany , 2009. pp. 25-26.
DOI:10.1007/s11548-009-0309-y
File: s11548-009-0309-y

2008

Floris Ernst, and Achim Schweikard,
A family of linear algorithms for the prediction of respiratory motion in image-guided radiotherapy, Barcelona, Spain , 2008. pp. 31-32.
DOI:10.1007/s11548-008-0169-x
File: s11548-008-0169-x
Floris Ernst, Alexander Schlaefer, Sonja Dieterich, and Achim Schweikard,
A Fast Lane Approach to LMS Prediction of Respiratory Motion Signals, Biomedical Signal Processing and Control , vol. 3, no. 4, pp. 291-299, 2008.
DOI:10.1016/j.bspc.2008.06.001
File: j.bspc.2008.06.001
Eric Barnes,
Faster respiratory motion prediction aids radiotherapy, 2008.
File: redirect.asp
Konrad L. Strulik, Min H. Cho, Brian T. Collins, Noureen Khan, Filip Banovac, Rebecca Slack, and Kevin Cleary,
Fiducial migration following small peripheral lung tumor image-guided CyberKnife stereotactic radiosurgery: Visualization, Image-guided Procedures, and Modeling. Proceedings of SPIE, San Diego, CA , 2008. pp. 69181A-69181A-9.
DOI:10.1117/12.769042
File: 12.769042
Matthias Knöpke, and Floris Ernst,
Flexible Markergeometrien zur Erfassung von Atmungs- und Herzbewegungen an der Körperoberfläche, Bartz, Dirk and Bohn, S. and Hoffmann, J., Eds. Leipzig, Germany , 2008. pp. 15-16.
Norman Rzezovski, and Floris Ernst,
Graphical Tool for the Prediction of Respiratory Motion Signals, Bartz, Dirk and Bohn, S. and Hoffmann, J., Eds. Leipzig, Germany , 2008. pp. 179-180.
Floris Ernst, and Achim Schweikard,
Predicting Respiratory Motion Signals for Image-Guided Radiotherapy using Multi-step Linear Methods (MULIN), International Journal of Computer Assisted Radiology and Surgery , vol. 3, no. 1--2, pp. 85-90, 2008.
DOI:10.1007/s11548-008-0211-z
File: s11548-008-0211-z
Floris Ernst, and Achim Schweikard,
Prediction of respiratory motion using a modified Recursive Least Squares algorithm, Bartz, Dirk and Bohn, S. and Hoffmann, J., Eds. Leipzig, Germany , 2008. pp. 157-160.

2007

Alexander Muacevic, Christian Drexler, A. Wowra, Achim Schweikard, Alexander Schlaefer, R. T. Hoffmann, R. Wilkowski, and H. Winter,
Technical Description, Phantom Accuracy, and Clinical Feasibility for Single-session Lung Radiosurgery Using Robotic Image-guided Real-time Respiratory Tumor Tracking, Technology in Cancer Research and Treatment , vol. 6, no. 4, pp. 321-328, 2007.
Floris Ernst, Ralf Bruder, and Alexander Schlaefer,
Processing of Respiratory Signals from Tracking Systems for Motion Compensated IGRT, Minneapolis-St. Paul, MN, USA , 2007. pp. 2565.
DOI:10.1118/1.2761413
File: 1.2761413
Floris Ernst, Alexander Schlaefer, and Achim Schweikard,
Prediction of Respiratory Motion with Wavelet-based Multiscale Autoregression, Ayache, Nicholas and Ourselin, S. and Maeder, A., Eds. Brisbane, Australia: Springer, 2007. pp. 668-675.
DOI:10.1007/978-3-540-75759-7\_81
File: 978-3-540-75759-7\_81
Lukas Ramrath, Alexander Schlaefer, Floris Ernst, Sonja Dieterich, and Achim Schweikard,
Prediction of respiratory motion with a multi-frequency based Extended Kalman Filter, Berlin, Germany , 2007. pp. 56-58.
DOI:10.1007/s11548-007-0083-7
File: s11548-007-0083-7

2006

Pantaleo Romanelli, Achim Schweikard, Alexander Schlaefer, and John R. Adler Jr.,
Computer aided robotic radiosurgery, Computer Aided Surgery , vol. 11, no. 4, pp. 161-174, 2006.
DOI:10.1080/10929080600886393
File: 10929080600886393
Achim Schweikard, and John R. Adler Jr.,
New Technologies in Radiation Oncology, Schlegel, Wolfgang C. and Bortfeld, Thomas and Grosu, A.-L., Eds. Springer, 2006, pp. 337-343.
DOI:10.1007/3-540-29999-8_26
File: 3-540-29999-8_26
Achim Schweikard,
Robotic Radiosurgery: The Kindest Cut of All, 2006. The Institution of Engineering and Technology Seminar on (Ref. No. 2006/11372), 2006. pp. 73-92.

2005

Alexander Muacevic, Berndt Wowra, and Achim Schweikard,
Cyberknife Radiochirurgie, Medizintechnik in Bayern , pp. 32-36, 2005.
Achim Schweikard, Hiroya Shiomi, M. Uchida, and John R. Adler Jr.,
Extracranial Stereotactic Radiotherapy and Radiosurgery, Slotman, Solberg, Wurm, Eds. New York: Taylor and Francis, 2005, pp. 71-87.
ISBN:0824726979
Achim Schweikard, and John R. Adler Jr.,
Predictive Compensation of Breathing Motion in Lung Cancer Radiosurgery, Heidelberg: Springer Verlag, 2005.
ISBN:3-540-00321-5
Achim Schweikard, Hiroya Shiomi, and John R. Adler Jr.,
Respiration tracking in radiosurgery without fiducials, International Journal of Medical Robotics and Computer Assisted Surgery , vol. 1, no. 2, pp. 19-27, 2005.
DOI:10.1002/rcs.38
File: rcs.38

2004

Achim Schweikard, Hiroya Shiomi, J. Fisseler, M. Dötter, Kajetan Berlinger, H.-B. Gehl, and John R. Adler Jr.,
Fiducial-Less Respiration Tracking in Radiosurgery, Springer, 2004. pp. 992-9.
Achim Schweikard, Hiroya Shiomi, and John R. Adler Jr.,
Respiration Tracking in Radiosurgery, Medical Physics , vol. 31, no. 10, pp. 2738-2741, 2004. American Association of Physicists in Medicine.
DOI:10.1118/1.1774132
File: 1.1774132