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

Pohl, Michel; Uesaka, Mitsuru; Demachi, Kazuyuki; Chhatkuli, Ritu Bhusal

doi:10.1016/j.compmedimag.2021.101941

Cited by 8 publications

(15 citation statements)

References 40 publications

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“…The prediction error decreased as the number of hidden units increased. That fact has previously been observed in the case of RTRL [32].…”

Section: Discussionsupporting

confidence: 78%

“…10). Similarly, it has been reported in [32] that increasing the number of hidden units of a vanilla RNN with a single hidden layer trained with RTRL to predict breathing signals led to a decrease of the prediction MAE. Fig.…”

Section: Influence Of the Hyper-parameters On Prediction Accuracysupporting

confidence: 67%

“…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.…”

Section: Compensation Of Treatment System Latency Via Predictionmentioning

confidence: 99%

“…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].…”

Section: Compensation Of Treatment System Latency Via Predictionmentioning

confidence: 99%

“…Adaptive or dynamic learning has been applied many times to radiotherapy, and several studies demonstrated the benefit of that approach in comparison with static models [15,40,22]. Real-time recurrent learning (RTRL) [44,8] is one of these dynamic approaches that has already been used for predicting tumor motion from the Cyberknife Synchrony system [22] and the position of internal points in the chest [32].…”

Section: Compensation Of Treatment System Latency Via Predictionmentioning

confidence: 99%

See 4 more Smart Citations

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

Pohl,

Uesaka,

Takahashi

et al. 2021

Preprint

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During lung cancer radiotherapy, the position of infrared reflective objects on the chest can be recorded to estimate the tumor location. However, radiotherapy systems usually have a latency inherent to robot control limitations that impedes the radiation delivery precision. Not taking this phenomenon into account may cause unwanted damage to healthy tissues and lead to side effects such as radiation pneumonitis. In this research, we use nine observation records of the three-dimensional position of three external markers on the chest and abdomen of healthy individuals breathing during intervals from 73s to 222s. The sampling frequency is equal to 10Hz and the amplitudes of the recorded trajectories range from 6mm to 40mm in the superior-inferior direction. We forecast the location of each marker simultaneously with a horizon value (the time interval in advance for which the prediction is made) between 0.1s and 2.0s, using a recurrent neural network (RNN) trained with unbiased online recurrent optimization (UORO). We compare its performance with an RNN trained with real-time recurrent learning, least mean squares (LMS), and offline linear regression. Training and cross-validation are performed during the first minute of each sequence. On average, UORO achieves the lowest root-mean-square (RMS)

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“…The prediction error decreased as the number of hidden units increased. That fact has previously been observed in the case of RTRL [32].…”

Section: Discussionsupporting

confidence: 78%

Section: Influence Of the Hyper-parameters On Prediction Accuracysupporting

confidence: 67%

Section: Compensation Of Treatment System Latency Via Predictionmentioning

confidence: 99%

Section: Compensation Of Treatment System Latency Via Predictionmentioning

confidence: 99%

Section: Compensation Of Treatment System Latency Via Predictionmentioning

confidence: 99%

See 3 more Smart Citations

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

Pohl,

Uesaka,

Takahashi

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Artificial intelligence‐based motion tracking in cancer radiotherapy: A review

Salari,

Wang,

Wynne

et al. 2024

J Applied Clin Med Phys

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Radiotherapy aims to deliver a prescribed dose to the tumor while sparing neighboring organs at risk (OARs). Increasingly complex treatment techniques such as volumetric modulated arc therapy (VMAT), stereotactic radiosurgery (SRS), stereotactic body radiotherapy (SBRT), and proton therapy have been developed to deliver doses more precisely to the target. While such technologies have improved dose delivery, the implementation of intra‐fraction motion management to verify tumor position at the time of treatment has become increasingly relevant. Artificial intelligence (AI) has recently demonstrated great potential for real‐time tracking of tumors during treatment. However, AI‐based motion management faces several challenges, including bias in training data, poor transparency, difficult data collection, complex workflows and quality assurance, and limited sample sizes. This review presents the AI algorithms used for chest, abdomen, and pelvic tumor motion management/tracking for radiotherapy and provides a literature summary on the topic. We will also discuss the limitations of these AI‐based studies and propose potential improvements.

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Lightweight attention temporal convolutional network based on multi-scale feature fusion for respiratory prediction in tumor radiotherapy

Shi,

Liu,

Zhao

et al. 2025

Biomedical Signal Processing and Control

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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

Cited by 8 publications

References 40 publications

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

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

Artificial intelligence‐based motion tracking in cancer radiotherapy: A review

Lightweight attention temporal convolutional network based on multi-scale feature fusion for respiratory prediction in tumor radiotherapy

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