• Progress in Medical Physics
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• v.19 no.1
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• pp.14-20
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• 2008

Accounting for tumor motion in treatment planning and delivery is one of the most recent and significant challenges facing radiotherapy. The purpose of this study was to investigate the correlation and clarified the relationship between the motion of an external marker using the Real-Time Position Management (RPM) System and an internal organ motion signal obtained fluoroscope. We enrolled 10 patients with locally advanced lung cancer and liver cancer, retrospectively. The external marker was a plastic box, which is part of the RPM used to track the patient's respiration. We investigated the quantitatively correlation between the motions of an external marker with RPM and internal motion with fluoroscope. The internal fiducial motion is predominant in the caraniocaudal direction, with a range of $1.3{\sim}3.5cm$ with fluoroscopic unit. The external fiducial motion is predominant in the caraniocaudal direction, with a range of $0.43{\sim}2.19cm$ with RPM gating. The two measurements ratio is from 1.31 to 5.56. When the regularization guided standard deviation is from 0.08 to 0.87, mean 0.204 cm, except only for patients #3 separated by a mean 0.13 cm, maximum of 0.23 cm. This result is a good correlation between internal tumor motion imaged by fluoroscopic unit and external marker motion with RPM during expiration within 0.23 cm. We have demonstrated that gating may be best performed but special attention should be paid to gating for patients whose fiducials do not move in synchrony, because targeting on the correct phase difference alone would not guarantee that the entire tumor volume is within the treatment field.

• Progress in Medical Physics
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• v.20 no.3
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• pp.132-138
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• 2009

Studies on target motion in 4-dimensional radiotherapy are being world-widely conducted to enhance treatment record and protection of normal organs. Prediction of tumor motion might be very useful and/or essential for especially free-breathing system during radiation delivery such as respiratory gating system and tumor tracking system. Neural network is powerful to express a time series with nonlinearity because its prediction algorithm is not governed by statistic formula but finds a rule of data expression. This study intended to assess applicability of neural network method to predict tumor motion in 4-dimensional radiotherapy. Scaled Conjugate Gradient algorithm was employed as a learning algorithm. Considering reparation data for 10 patients, prediction by the neural network algorithms was compared with the measurement by the real-time position management (RPM) system. The results showed that the neural network algorithm has the excellent accuracy of maximum absolute error smaller than 3 mm, except for the cases in which the maximum amplitude of respiration is over the range of respiration used in the learning process of neural network. It indicates the insufficient learning of the neural network for extrapolation. The problem could be solved by acquiring a full range of respiration before learning procedure. Further works are programmed to verify a feasibility of practical application for 4-dimensional treatment system, including prediction performance according to various system latency and irregular patterns of respiration.