Guo-Pei Zhu | PubMed Health

PubMed2006

The correlation evaluation of a tumor tracking system using multiple external markers.

The purpose of this study is to evaluate the correlations between external markers and internal targets for radiation therapy of lung cancer patients. Using an infrared camera system coupled with a clinical simulator, the simultaneous motions of multiple external markers and an internal target were obtained. The correlation between external and internal signals was analyzed using a cross-covariance function. A linear regression model was employed to generate a composite signal from multiple external markers in order to predict the internal target motion. The external and internal signals, and their correlations, demonstrated a wide range of variation with respect to marker location, motion dimension, and breathing pattern. The performance of the composite signal indicates that when more external signals were taken into account, the mean correlation between the composite signal and internal signal was improved. This implies that a combination of multiple external signals might be an improved way to predict internal target motion. Also, since the characteristics of respiratory signals can vary significantly, certain methods of preprocessing and external signal combination are necessary.

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PubMed2005

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

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.

Algorithms↗