Imaging and Control for Adaptive Radiotherapy

Haas, Olivier C.L.; Bueno, Gloria; Spriestersbach, R.; Himmler, Heiko; Burnham, Keith J.; Mills, John A.

doi:10.3182/20050703-6-cz-1902.02118

Cited by 10 publications

(8 citation statements)

References 23 publications

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“…EPID requires no internal markers and therefore enables non-invasive therapy. This has motivated the research activities of robot assisted radiotherapy in the last years [11] [12]. Currently the main difficulty of motion determination with EPID is the low data rate.…”

Section: Motion Prediction By Data Fusionmentioning

confidence: 99%

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On challenges of robot assisted radiotherapy for lung tumors

Herrmann

Schilling

2010

2010 IEEE International Conference on Robotics and Biomimetics

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This paper is concerned with recent development in robot assisted radiotherapy for lung tumors. The paper first analyzes technical challenges regarding tumor position determination and prediction, by invasive and non-invasive methods, correlation models of the tumor motion and the respiratory signals, as well as modeling and control of the robot manipulator. Several currently available solutions and concepts are then briefly introduced. Emphasis is placed on the ATTS (adaptive tumor tracking system) system developed at the University of Würzburg. Within the ATTS, information of the tumor motion is used to control a robotic treatment couch HexaPOD, which moves in six degrees of freedom and maintains the tumor in a certain fixed spatial position. With this new technology it is possible to avoid gating during the radiotherapy as well as active breathing control, such that duration and cost of the therapy as well as trauma of the patient can be reduced. The tumor position is obtained by data fusion from two sensing system. Correlation methods for data fusion were extensively investigated and a prediction model for tumor motion is found that can forecast the tumor position for up to one respiratory period. Modeling of the HexaPOD treatment couch is addressed. The most suitable model is found to be a low-order linear dynamic equation together with a saturation component. Three different control methods and comparative test results are then presented to show feasibility of the proposed model and control schemes.

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Section: Motion Prediction By Data Fusionmentioning

confidence: 99%

“…The Coventry University has worked on modeling and control strategies for a patient support system (PSS) [11]. The proposed methods aim to provide accurate patient positioning during the therapy.…”

Section: B Patient Support Systemmentioning

confidence: 99%

On challenges of robot assisted radiotherapy for lung tumors

Herrmann

Schilling

2010

2010 IEEE International Conference on Robotics and Biomimetics

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“…Multi-body mechanical model. A SimMechanics TM model was developed based on the physical characteristics of each DOF of the PSS (Haas et al 2005). The mechanical components were represented as rigid bodies.…”

Section: Pss Modelingmentioning

confidence: 99%

Couch-based motion compensation: modelling, simulation and real-time experiments

et al. 2012

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The paper presents a couch-based active motion compensation strategy evaluated in simulation and validated experimentally using both a research and a clinical Elekta Precise Table™. The control strategy combines a Kalman filter to predict the surrogate motion used as a reference by a linear model predictive controller with the control action calculation based on estimated position and velocity feedback provided by an observer as well as predicted couch position and velocity using a linearized state space model. An inversion technique is used to compensate for the dead-zone nonlinearity. New generic couch models are presented and applied to model the Elekta Precise Table™ dynamics and nonlinearities including dead zone. Couch deflection was measured for different manufacturers and found to be up to 25 mm. A feed-forward approach is proposed to compensate for such couch deflection. Simultaneous motion compensation for longitudinal, lateral and vertical motions was evaluated using arbitrary trajectories generated from sensors or loaded from files. Tracking errors were between 0.5 and 2 mm RMS. A dosimetric evaluation of the motion compensation was done using a sinusoidal waveform. No notable differences were observed between films obtained for a fixed- or motion-compensated target. Further dosimetric improvement could be made by combining gating, based on tracking error together with beam on/off time, and PSS compensation.

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“…An EPID requires no marker to be implanted in the tumor, hence therapy is non-invasive. This has motivated the research and development activities in the area of robot-assisted radiotherapy in recent years [5,9] .…”

Section: Introductionmentioning

confidence: 99%

“…The possibilities range from moving the linear accelerator with a robot manipulator (e.g., the CyberKnife system [Accuray Inc., Sunnyvale, CA, USA] [1,29,39,40,44] or the VERO system [Brainlab AG, Feldkirchen, Germany and Mitsubishi Heavy Industries, Tokyo, Japan] [43] ) to moving the patient using a robotic treatment couch [5,9,36,45] .…”

Section: Introductionmentioning

confidence: 99%

Control of a HexaPOD treatment couch for robot-assisted radiotherapy

Hermann

Ma²,

Wilbert³

et al. 2012

Biomedizinische Technik/Biomedical Engineering

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Moving tumors, for example in the vicinity of the lungs, pose a challenging problem in radiotherapy, as healthy tissue should not be irradiated. Apart from gating approaches, one standard method is to irradiate the complete volume within which a tumor moves plus a safety margin containing a considerable volume of healthy tissue. This work deals with a system for tumor motion compensation using the HexaPOD® robotic treatment couch (Medical Intelligence GmbH, Schwabmünchen, Germany). The HexaPOD, carrying the patient during treatment, is instructed to perform translational movements such that the tumor motion, from the beams-eye view of the linear accelerator, is eliminated. The dynamics of the HexaPOD are characterized by time delays, saturations, and other non-linearities that make the design of control a challenging task. The focus of this work lies on two control methods for the HexaPOD that can be used for reference tracking. The first method uses a model predictive controller based on a model gained through system identification methods, and the second method uses a position control scheme useful for reference tracking. We compared the tracking performance of both methods in various experiments with real hardware using ideal reference trajectories, prerecorded patient trajectories, and human volunteers whose breathing motion was compensated by the system.

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Imaging and Control for Adaptive Radiotherapy

Cited by 10 publications

References 23 publications

On challenges of robot assisted radiotherapy for lung tumors

On challenges of robot assisted radiotherapy for lung tumors

Couch-based motion compensation: modelling, simulation and real-time experiments

Control of a HexaPOD treatment couch for robot-assisted radiotherapy

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