Respiratory motion forecasting
2 papers with code • 0 benchmarks • 1 datasets
Respiratory motion forecasting to compensate for the latency of the radiotherapy treatment systems and target more accurately chest tumors.
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Most implemented papers
Prediction of the Position of External Markers Using a Recurrent Neural Network Trained With Unbiased Online Recurrent Optimization for Safe Lung Cancer Radiotherapy
Prediction with online learning of recurrent neural networks (RNN) allows for adaptation to non-stationary respiratory signals, but classical methods such as RTRL and truncated BPTT are respectively slow and biased.
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
The amplitude of the motion of the tracked points ranged from 12. 0mm to 22. 7mm.
