Adaptive prediction of internal target motion using external marker motion: a technical study.

Published

Journal Article

An adaptive prediction approach was developed to infer internal target position by external marker positions. First, a prediction model (or adaptive neural network) is developed to infer target position from its former positions. For both internal target and external marker motion, two networks with the same type are created. Next, a linear model is established to correlate the prediction errors of both neural networks. Based on this, the prediction error of an internal target position can be reconstructed by the linear combination of the prediction errors of the external markers. Finally, the next position of the internal target is estimated by the network and subsequently corrected by the reconstructed prediction error. In a similar way, future positions are inferred as their previous positions are predicted and corrected. This method was examined by clinical data. The results demonstrated that an improvement (10% on average) of correlation between predicted signal and real internal motion was achieved, in comparison with the correlation between external markers and internal target motion. Based on the clinical data (with correlation coefficient 0.75 on average) observed between external marker and internal target motions, a prediction error (23% on average) of internal target position was achieved. The preliminary results indicated that this method is helpful to improve the predictability of internal target motion with the additional information of external marker signals. A consistent correlation between external and internal signals is important for prediction accuracy.

Full Text

Duke Authors

Cited Authors

  • Yan, H; Yin, F-F; Zhu, G-P; Ajlouni, M; Kim, JH

Published Date

  • January 7, 2006

Published In

Volume / Issue

  • 51 / 1

Start / End Page

  • 31 - 44

PubMed ID

  • 16357429

Pubmed Central ID

  • 16357429

International Standard Serial Number (ISSN)

  • 0031-9155

Digital Object Identifier (DOI)

  • 10.1088/0031-9155/51/1/003

Language

  • eng

Conference Location

  • England