Yan Hui, Yin Fang-Fang, Zhu Guo-Pei, Ajlouni Munther, Kim Jae Ho
Department of Radiation Oncology, Duke University Medical Center, Durham, NC 27710, USA.
Phys Med Biol. 2006 Jan 7;51(1):31-44. doi: 10.1088/0031-9155/51/1/003. Epub 2005 Dec 15.
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.
开发了一种自适应预测方法，通过外部标记位置推断内部目标位置。首先，开发一个预测模型（或自适应神经网络），根据目标的先前位置推断其位置。对于内部目标和外部标记的运动，创建两个相同类型的网络。接下来，建立一个线性模型来关联两个神经网络的预测误差。基于此，内部目标位置的预测误差可以通过外部标记预测误差的线性组合来重建。最后，通过网络估计内部目标的下一个位置，随后用重建的预测误差进行校正。以类似的方式，通过预测和校正其先前位置来推断未来位置。该方法通过临床数据进行了检验。结果表明，与外部标记和内部目标运动之间的相关性相比，预测信号与实际内部运动之间的相关性平均提高了10%。基于外部标记与内部目标运动之间观察到的临床数据（平均相关系数为0.75），实现了内部目标位置的预测误差（平均为23%）。初步结果表明，该方法有助于利用外部标记信号的附加信息提高内部目标运动的可预测性。外部和内部信号之间的一致相关性对于预测准确性很重要。
