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

2019

Sven Böttger, Felix Haxthausen, Markus Kleemann, Floris Ernst, and Achim Schweikard,
Robotics from the bench -- Research for ultrasound automation with augmented reality visualization, 2019.
File: MIC_2019_Program.pdf
Judit Boda‐Heggemann, Anika Jahnke, Mark K. H. Chan, Floris Ernst, Ardekani Leila Ghaderi, Ulrike Attenberger, Peter Hunold, Jost Philipp Schäfer, Stefan Wurster, Dirk Rades, Guido Hildebrandt, Frank Lohr, Jürgen Dunst, Frederik Wenz, and Oliver Blanck,
In-vivo treatment accuracy analysis of active motion-compensated liver SBRT through registration of plan dose to post-therapeutic MRI-morphologic alterations, Radiotherapy and Oncology , vol. 134, pp. 158-165, 2019.
File: S0167814019300283

2018

Svenja Ipsen,
[I094] Ultrasound guidance in radiotherapy - Renaissance through innovation, in Physica Medica , Elsevier, 2018. pp. 57.
DOI:10.1016/j.ejmp.2018.06.166
File: j.ejmp.2018.06.166
Svenja Ipsen, Ralf Bruder, Ivo Kuhlemann, Philipp Jauer, Laura Motisi, Florian Cremers, Floris Ernst, and Achim Schweikard,
A visual probe positioning tool for 4D ultrasound-guided radiotherapy, 2018. pp. 883-886.
DOI:10.1109/EMBC.2018.8512390
File: EMBC.2018.8512390
Suzanne Lydiard, Vincent Caillet, Svenja Ipsen, Ricky T. O'Brien, Oliver Blanck, Per Rugaard Poulsen, Jeremy Booth, and Paul J. Keall,
Investigating MLC tracking in stereotactic arrhythmic radioablation (STAR) treatments for atrial fibrillation, Physics in Medicine and Biology , vol. 63, no. 19, pp. 195008, 2018.
DOI:10.1088/1361-6560/aadf7c
File: aadf7c
Nicholas Lowther, Svenja Ipsen, Steven Marsh, Oliver Blanck, and Paul J. Keall,
Investigation of the XCAT phantom as a validation tool in cardiac MRI tracking algorithms, Physica Medica , vol. 45, no. 1, pp. 44-51, 2018.
DOI:10.1016/j.ejmp.2017.12.003
File: j.ejmp.2017.12.003
Svenja Ipsen, Ralf Bruder, Floris Ernst, and Achim Schweikard,
WE-HI-KDBRB1-02: Characterization of 4D ultrasound systems with streaming interfaces for real-time motion compensation in radiotherapy, in Medical Physics , 2018. pp. E644.

2017

Suzanne Lydiard, Vincent Caillet, Svenja Ipsen, Ricky T. O'Brien, Ralf Bruder, Oliver Blanck, Jeremy Booth, and Paul J. Keall,
MO-AB-FS4-10: First Cardiac Radiosurgery MLC Tracking Results, in Medical Physics , 2017. pp. 3034.
Svenja Ipsen, Ralf Bruder, Esben Schjødt Worm, Rune Hansen, Per Rugaard Poulsen, Morten Høyer, and Achim Schweikard,
MO-DE-708-6: In-vivo comparison of real-time 4D ultrasound tracking with electromagnetic transponders in the liver during free breathing, in Medical Physics , 2017. pp. 3069.
Svenja Ipsen, Ralf Bruder, Esben Schjødt Worm, Rune Hansen, Per Rugaard Poulsen, Morten Høyer, and Achim Schweikard,
Simultaneous acquisition of 4D ultrasound and wireless electromagnetic tracking for in-vivo accuracy validation, Current Directions in Biomedical Engineering , vol. 3, no. 2, pp. 75-78, 2017.
DOI:10.1515/cdbme-2017-0016
File: cdbme-2017-0016

2016

Svenja Ipsen, Ralf Bruder, and Achim Schweikard,
P28: Towards 6dof tracking of deformable objects for 4D ultrasound-guided radiation therapy, Sydney, Australia: Springer Netherlands, 2016.
DOI:10.1007/s13246-016-0494-2
File: s13246-016-0494-2
Oliver Blanck, Svenja Ipsen, Mark K. H. Chan, Matthias Kerl, P. Hunold, Volkmar Jacobi, Ralf Bruder, Achim Schweikard, Dirk Rades, Thomas J. Vogl, Peter Kleine, F. Bode, and Jürgen Dunst,
Treatment Planning Considerations for Robotic Guided Cardiac Radiosurgery for Atrial Fibrillation, Cureus , vol. 8, no. 7, pp. e705, 2016.
DOI:10.7759/cureus.705
File: cureus.705
Svenja Ipsen, Oliver Blanck, Nicholas Lowther, Gary Liney, Robba Rai, F. Bode, Jürgen Dunst, Achim Schweikard, and Paul J. Keall,
Towards real-time MRI-guided 3D localization of deforming targets for non-invasive cardiac radiosurgery, Physics in Medicine and Biology , vol. 61, no. 22, pp. 7848-63, 2016.
DOI:10.1088/0031-9155/61/22/7848
File: 7848
Svenja Ipsen, Ralf Bruder, Ricky T. O'Brien, Paul J. Keall, Achim Schweikard, and Per Rugaard Poulsen,
TH-AB-202-05: BEST IN PHYSICS (JOINT IMAGING-THERAPY): First Online Ultrasound-Guided MLC Tracking for Real-Time Motion Compensation in Radiotherapy, in Medical Physics , 2016. pp. 3857-3857.
DOI:10.1118/1.4958069
File: 1.4958069
Svenja Ipsen, Ralf Bruder, Rick O'Brian, Paul J. Keall, Achim Schweikard, and Per Rugaard Poulsen,
Online 4D ultrasound guidance for real-time motion compensation by MLC tracking, Medical Physics , pp. 5695-5704, 2016.
DOI:10.1118/1.4962932
File: 1.4962932
Stefan Gerlach, Ivo Kuhlemann, Philipp Jauer, Ralf Bruder, Floris Ernst, Christoph Fürweger, and Alexander Schlaefer,
Feasibility of robotic ultrasound guided SBRT of the prostate, Heidelberg , 2016.
Svenja Ipsen, Nicholas Lowther, Gary Liney, Dirk Rades, Jürgen Dunst, Achim Schweikard, Paul J. Keall, and Oliver Blanck,
Echtzeit-Lokalisation in simulierten und realen MRT-Daten für nicht invasive Radiochirurgie des Herzens in einem MR-Linac, 2016.
Svenja Ipsen, Ralf Bruder, Philipp Jauer, Floris Ernst, Oliver Blanck, and Achim Schweikard,
An improved tracking framework for ultrasound probe localization in image-guided radiosurgery, Current Directions in Biomedical Engineering , vol. 2, no. 1, pp. 409-413, 2016.
DOI:10.1515/cdbme-2016-0091
File: cdbme-2016-0091

2015

Ralf Bruder, Floris Ernst, Oliver Blanck, Jürgen Dunst, and Achim Schweikard,
4D Ultrasound Image Guidance for Cardiac Radiosurgery, Cookham, UK , 2015.
Kenneth Poels, Jennifer Dhont, Dirk Verellen, Oliver Blanck, Floris Ernst, Jef Vandemeulebroucke, Tom Depuydt, Guy Storme, and Mark De Ridder,
A comparison of two clinical correlation models used for real-time tumor tracking of semi-periodic motion: A focus on geometrical accuracy in lung and liver cancer patients, Radiotherapy and Oncology , vol. 115, no. 3, pp. 419-424, 2015.
DOI:10.1016/j.radonc.2015.05.004
File: j.radonc.2015.05.004
Dimitre Hristov, Renhui Gong, Jeffrey Schlosser, Ralf Bruder, and Achim Schweikard,
Augmented reality system for robotic ultrasound guidance of external beam radiation therapy, Barcelona, Spain , 2015.
Oliver Blanck, Melanie Grehn, S. Wurster, Guido Hildebrandt, Jürgen Dunst, F. A. Siebert, Achim Schweikard, and Floris Ernst,
Dosimetrischer Einfluss von residualen Trackingfehlern in der robotergestützten Radiochirurgie von Lebertumoren, Hamburg, Germany , 2015.
Svenja Ipsen, Brad Oborn, F. Bode, Gary Liney, P. Hunold, Dirk Rades, Achim Schweikard, Jürgen Dunst, Paul J. Keall, and Oliver Blanck,
Echtzeit-Zielverfolgung für nicht invasive MRT-gestützte Herzradiochirurgie, Hamburg, Germany , 2015.
Ivo Kuhlemann, Philipp Jauer, Achim Schweikard, and Floris Ernst,
Patient localization for robotized ultrasound-guided radiation therapy, 2015. pp. 105-112.
Floris Ernst, and Philipp Saß,
Respiratory motion tracking using Microsoft's Kinect v2 camera, Current Directions in Biomedical Engineering , vol. 1, no. 1, pp. 192-195, 2015.
DOI:10.1515/cdbme-2015-0048
File: cdbme-2015-0048