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Modeling and performance evaluation of a robotic treatment couch for tumor tracking

  • Alexander Jöhl , Stephanie Lang , Stefanie Ehrbar , Matthias Guckenberger , Stephan Klöck , Mirko Meboldt and Marianne Schmid Daners EMAIL logo
Published/Copyright: March 25, 2016

Abstract

Tumor motion during radiation therapy increases the irradiation of healthy tissue. However, this problem may be mitigated by moving the patient via the treatment couch such that the tumor motion relative to the beam is minimized. The treatment couch poses limitations to the potential mitigation, thus the performance of the Protura (CIVCO) treatment couch was characterized and numerically modeled. The unknown parameters were identified using chirp signals and verified with one-dimensional tumor tracking. The Protura tracked chirp signals well up to 0.2 Hz in both longitudinal and vertical directions. If only the vertical or only the longitudinal direction was tracked, the Protura tracked well up to 0.3 Hz. However, there was unintentional yet substantial lateral motion in the former case. And during vertical motion, the extension caused rotation of the Protura around the lateral axis. The numerical model matched the Protura up to 0.3 Hz. Even though the Protura was designed for static positioning, it was able to reduce the tumor motion by 69% (median). The correlation coefficient between the tumor motion reductions of the Protura and the model was 0.99. Therefore, the model allows tumor-tracking results of the Protura to be predicted.


Corresponding author: Marianne Schmid Daners, Product Development Group Zurich, Department of Mechanical and Process Engineering, ETH Zurich, CLA G21.1, Tannenstrasse 3, Zurich 8092, Switzerland, Phone: +41 44 632 24 47, E-mail:

Nomenclature
B

base-leg joint location

B0

initial base-leg joint location

CG

center of gravity

DoF

degree(s) of freedom

E

platform-extension joint

ECG

center of gravity of extension

F

input forces of mechanical system

J

leg-platform joint location

LCG

center of gravity of leg

M

generic mass matrix of (1)

O

inertial origin of system

PID

proportional-integral-differential

T

geometrical center of platform of treatment couch

TCG

center of gravity of platform of treatment couch

eRMS

root mean squared error

t

vector from origin to center of platform

tref

reference platform position

q

quaternion vector for rotation of platform

qref

reference quaternion for rotation of platform

u

one base-leg position (a component of u)

u

base-leg positions

uref

reference base-leg positions

utraj

derivative limited reference base-leg positions

ϕ

angle of extension relative to platform

ωIT

rotational velocity of platform

Acknowledgments:

This work was supported by the Swiss National Science Foundation (SNSF) through “Development of prediction models for liver, lung, and breast tumors and implementation and verification of prediction filters for advanced couch tracking in a clinical environment”, Grant No. CR32I3_153491.

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Supplemental Material:

The online version of this article (DOI: 10.1515/bmt-2015-0187) offers supplementary material, available to authorized users.


Received: 2015-9-29
Accepted: 2016-2-20
Published Online: 2016-3-25
Published in Print: 2016-10-1

©2016 Walter de Gruyter GmbH, Berlin/Boston

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