Prog Med Phys.  2017 Dec;28(4):171-180. 10.14316/pmp.2017.28.4.171.

Simulation and Experimental Studies of Real-Time Motion Compensation Using an Articulated Robotic Manipulator System

• Lee M
• Cho MS
• Lee H
• Chung H
• Cho B

• KMID: 2401379
• DOI: http://doi.org/10.14316/pmp.2017.28.4.171

Abstract

The purpose of this study is to install a system that compensated for the respiration motion using an articulated robotic manipulator couch which enables a wide range of motions that a Stewart platform cannot provide and to evaluate the performance of various prediction algorithms including proposed algorithm. For that purpose, we built a miniature couch tracking system comprising an articulated robotic manipulator, 3D optical tracking system, a phantom that mimicked respiratory motion, and control software. We performed simulations and experiments using respiratory data of 12 patients to investigate the feasibility of the system and various prediction algorithms, namely linear extrapolation (LE) and double exponential smoothing (ES2) with averaging methods. We confirmed that prediction algorithms worked well during simulation and experiment, with the ES2-averaging algorithm showing the best results. The simulation study showed 43% average and 49% maximum improvement ratios with the ES2-averaging algorithm, and the experimental study with the QUASARâ„¢ phantom showed 51% average and 56% maximum improvement ratios with this algorithm. Our results suggest that the articulated robotic manipulator couch system with the ES2-averaging prediction algorithm can be widely used in the field of radiation therapy, providing a highly efficient and utilizable technology that can enhance the therapeutic effect and improve safety through a noninvasive approach.

Keyword

Articulated robotic manipulator; Real-time tumor tracking; Prediction algorithm; ES2-averaging

MeSH Terms

Compensation and Redress*
Humans
Respiration

Figure

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  Fig. 1 Schematic illustration of a couch-based real-time motion compensation system.

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  Fig. 2 Double exponential smoothing algorithm with the averaging method (ES2-averaging).

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  Fig. 3 Marker displacement on the rotation phantom and couch, obtained using the tracking system without applying a prediction algorithm. The inset shows a segment of the same signal.

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  Fig. 4 Improvement ratios for the simulation and experimental studies.

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  Fig. 5 Marker displacements for one (patient 5) out of 12 patients when applying the various prediction algorithms in the experimental study. The thick line indicates the phantom motion; ideally, this should appear still, indicating no relative movement between the phantom and couch.

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