A new motion management method for lung tumor tracking radiation therapy

Authors:
Noriyasu Homma;Masao Sakai;Haruna Endo;Masatoshi Mitsuya;Yoshihiro Takai;Makoto Yoshizawa
Affiliations:
Tohoku University, Cyberscience Center, Sendai, Japan;Tohoku University, Center Inform. Tech. Edu., Sendai, Japan;Tohoku University, Graduate School of Medicine, Sendai, Japan;Tohoku University, Graduate School of Medicine, Sendai, Japan;Tohoku University, Graduate School of Medicine, Sendai, Japan;Tohoku University, Cyberscience Center, Sendai, Japan
Venue:
WSEAS TRANSACTIONS on SYSTEMS
Year:
2009

Citing 5
Cited 0

Time Series Analysis, Forecasting and Control

Time Series Analysis, Forecasting and Control
An iterative algorithm for automatic fitting of continuous piecewise linear models

WSEAS Transactions on Signal Processing
A new fast forecasting technique using high speed neural networks

WSEAS Transactions on Signal Processing
Multi step ahead prediction of north and south hemisphere sun spots chaotic time series using focused time lagged recurrent neural network model

WSEAS Transactions on Information Science and Applications
The leaf sweep algorithm for an immobile and moving target as an optimal control problem in radiotherapy delivery

Mathematical and Computer Modelling: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

We propose a new motion management method for lung tumor tracking radiotherapy by using a novel time series prediction technique. In radiotherapy, the target motion often affects the conformability of the therapeutic dose distribution delivered to thoracic and abdominal tumors, and thus tumor motion monitoring systems have been developed. Even we can observe tumor motion accurately, however, radiotherapy systems may inherently have mechanical and computational delays to be compensated for synchronizing dose delivery with the motion. For solving the delay problem, we develop a novel system to predict complex time series of the lung tumor motion. An essential core of the system is an adaptive prediction modeling with a phase locking technique by which time-varying cyclic dynamics is transferred into a time invariant one. Simulation studies demonstrate that the proposed system can achieve a clinically useful high accuracy and long-term prediction of the average error 1.05 ± 0.99 [mm] at 1 [sec] ahead prediction.