Respiratory Motion Compensation with Relevance Vector Machines

Dürichen, Robert; Wissel, Tobias; Ernst, Floris; Schweikard, Achim

doi:10.1007/978-3-642-40763-5_14

Cited by 13 publications

(10 citation statements)

References 6 publications

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“…The Kalman filter constant velocity (KFCV) prediction filter uses a Kalman filter with a constant velocity model The relevance vector machine (RVM) prediction filter uses the RVM algorithm, which is based on a sparse Bayesian learning framework . The RVM prediction filter evaluates at every time step but trains in every tenth time step only. The neural network (NN) prediction filter trains the NN using a sliding window of past values and uses the current values to predict the future values of the trace .…”

Section: Methodsmentioning

confidence: 99%

Performance comparison of prediction filters for respiratory motion tracking in radiotherapy

et al. 2019

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Purpose In precision radiotherapy, the intrafractional motion causes substantial uncertainty. Traditionally, the target volume is expanded to cover the tumor in all positions. Alternative approaches are gating and adaptive tracking, which require a time delay as small as possible between the actual tumor motion and the reaction to effectively compensate the motion. Current treatment machines often exhibit large time delays. Prediction filters offer a promising means to mitigate these time delays by predicting the future respiratory motion. Methods A total of 18 prediction filters were implemented and their hyperparameters optimized for various time delays and noise levels. A set of 93 traces were standardized to a sampling frequency of 25 Hz and smoothed using the Fourier transform with a 3 Hz cutoff frequency. The hyperparameter optimization was carried out with ten traces, and the optimal hyperparameters were evaluated on the remaining 83 traces. Results For smooth traces, the wavelet least mean squares prediction filter and the linear filter reached normalized root mean square errors of below 0.05 for time delays of 160 and 480 ms, respectively. For noisy signals, the performance of the prediction filters deteriorated and led to similar results. Conclusions Linear methods for prediction filters are sufficient for respiratory motion signals. Reducing the measurement noise generally improves the performance of the prediction filters investigated in this study, even during breathing irregularities.

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Section: Methodsmentioning

confidence: 99%

Performance comparison of prediction filters for respiratory motion tracking in radiotherapy

et al. 2019

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“…Several studies have used ML algorithms for predicting tumour motion based on past motion, 72–89 including in MRI‐guided radiotherapy 90–92 and ultrasound‐guided radiotherapy 93 . A comparison study of ML algorithms was made by Sharp et al.…”

Section: Prediction Of Tumour Motionmentioning

confidence: 99%

“…Several studies have used ML algorithms for predicting tumour motion based on past motion, [72][73][74][75][76][77][78][79][80][81][82][83][84][85][86][87][88][89] including in MRI-guided radiotherapy [90][91][92] and ultrasound-guided radiotherapy. 93 A comparison study of ML algorithms was made by Sharp et al and showed that most ML algorithms have a lower localisation error compared to no prediction.…”

Section: Prediction Of Tumour Motionmentioning

confidence: 99%

A review of artificial intelligence applications for motion tracking in radiotherapy

2021

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During radiotherapy, the organs and tumour move as a result of the dynamic nature of the body; this is known as intrafraction motion. Intrafraction motion can result in tumour underdose and healthy tissue overdose, thereby reducing the effectiveness of the treatment while increasing toxicity to the patients. There is a growing appreciation of intrafraction target motion management by the radiation oncology community. Real‐time image‐guided radiation therapy (IGRT) can track the target and account for the motion, improving the radiation dose to the tumour and reducing the dose to healthy tissue. Recently, artificial intelligence (AI)‐based approaches have been applied to motion management and have shown great potential. In this review, four main categories of motion management using AI are summarised: marker‐based tracking, markerless tracking, full anatomy monitoring and motion prediction. Marker‐based and markerless tracking approaches focus on tracking the individual target throughout the treatment. Full anatomy algorithms monitor for intrafraction changes in the full anatomy within the field of view. Motion prediction algorithms can be used to account for the latencies due to the time for the system to localise, process and act.

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“…The data used in this paper are from the open data set collected by the Robot and Cognitive Systems Research Institute of Lubeck University [30], Germany. The data set includes (1) breath tracking with multiple external reference positions (15-20 minutes sampling data of 6 patients) with sampling frequency of 17 Hz; and (2) Principal Component Analysis (PCA) data of three external marker points.…”

Section: A Data Preprocessingmentioning

confidence: 99%

Rapid Prediction of Respiratory Motion Based on Bidirectional Gated Recurrent Unit Network

et al. 2020

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In chest and abdomen robotic radiosurgery, due to the motion delay of the robotic manipulator, the tumor position tracking process has a period of delay. This delay ultimately affects the accuracy of radiosurgery treatment. To address the influence of the delay in robotic radiosurgery, a Long-and-Short-Term Memory (LSTM) network as a deep Recurrent Neural Network (RNN) has been applied in a prediction network model for respiratory motion tracking in recent years. However, patients' respiratory state may change in the process of treatment, which may influence the accuracy of prediction. Therefore, it is necessary to update the prediction network through additional data, such as the actual position of the tumor obtained by X-ray imaging. However, the LSTM network has a long update time, and it may not be able to complete the prediction model update in a cycle of X-ray acquisition. To solve this problem, a fast prediction model based on Bidirectional Gated Recurrent Unit (Bi-GRU), is proposed in this paper. This method can reduce the average updating time of the network model by 30%.

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Respiratory Motion Compensation with Relevance Vector Machines

Cited by 13 publications

References 6 publications

Performance comparison of prediction filters for respiratory motion tracking in radiotherapy

Performance comparison of prediction filters for respiratory motion tracking in radiotherapy

A review of artificial intelligence applications for motion tracking in radiotherapy

Rapid Prediction of Respiratory Motion Based on Bidirectional Gated Recurrent Unit Network

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