Prediction of Lung Tumor Motion Based on Recurrent Neural Network

Prediction of the Position of External Markers Using a Recurrent Neural Network Trained With Unbiased Online Recurrent Optimization for Safe Lung Cancer Radiotherapy

“…The RMSE error that we achieved is approximately 2 to 4 times lower than the RMSEs reported by Sharp et al, who used a multilayer perceptron (MLP) with one hidden layer and breathing records of the same frequency (10Hz) with similar amplitudes [36]. The RMSE that we found is within the range reported by Kai et al, who predicted the position of an internal marker using an RNN with 1 hidden layer trained with BPTT with a much higher frequency (30 Hz) [12].…”

“…2), and to achieve this, various prediction methods have been proposed [41,18,11]. Among those methods, artificial neural networks (ANNs) with different architectures and training algorithms have been studied in the context of radiotherapy [28,36,9,13,26,6,27,19,15,20,4,37,12,43,40,21,46,22,45,17,42,32]. ANNs are efficient for performing prediction with a high response time, which is the time interval in advance for which the prediction is made, also called the look-ahead time or horizon, and for non-stationary and complex signals.…”

“…They are able to efficiently learn features and long-term dependencies from sequential and time-series data [34]. As a result, almost all of the recent research about ANNs applied to time series forecasting for motion management in radiotherapy focuses on RNNs [12,43,46,21,22,45,17,42,32]. RNN models such as long short term memory (LSTM) networks have also been used in related medical data processing problems such as cardiorespiratory motion prediction from X-ray angiography sequences [1] and next-frame prediction in medical image sequences, including chest dynamic imaging [29,33].…”

Prediction of the motion of chest internal points using a recurrent neural network trained with real-time recurrent learning for latency compensation in lung cancer radiotherapy

“…RNNs have been applied in many areas such as meteorology to predict wind speed [14] and air quality [15], and finance to predict stock prices [16] and currency exchange rates [17]. Concerning lung radiotherapy, Kai et al used an RNN with a single hidden layer, trained with back-propagation through time (BPTT), for the prediction of the position of an implanted marker [18]. Also, an online training approach based on extended Kalman filtering (EKF) has been applied to an RNN with a single hidden layer for the prediction of breathing data from the Cyberknife system [19].…”

“…The optimal choice of the RNN parameters is discussed thoroughly. In contrast to the related studies about marker position prediction with ANNs mentioned in Section 1.3, our study describes the simultaneous prediction of the position of three markers [12,13,18], rather than the position of one marker only. Finally, we propose a simple method to reconstruct and predict 3D lung tumor images given only the trajectory of internal markers and an initial 3D image of that tumor (Fig.…”