Predicting Respiratory Motion for Real-Time Tumour Tracking in Radiotherapy

Krilavičius, Tomas; liobaite, Indre; Simonavičius, Henrikas; Jaruevicius, Laimonas

doi:10.1109/cbms.2016.52

Cited by 9 publications

(13 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lung tumor displacement and baseline drift due to intra‐ and interfraction breathing motion variability may lead to a failure in accurate radiation delivery and tumor motion predictions . Most breathing management techniques rely on internal and external surrogates to improve tumor motion controlled and predictions, but the respiratory motion of surrogates does not always accurately match tumor motion.…”

Section: Discussionmentioning

confidence: 99%

“…Respiratory surrogates are often used to predict tumor motion during breathing and compensate for tumor motion with respiratory gating and tracking, and to derive system latency between tumor positioning and radiation delivery . However, tumor motion is not always accurately correlated to the internal/external surrogates due to breathing and heartbeat .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Audiovisual biofeedback improves the correlation between internal/external surrogate motion and lung tumor motion

et al. 2018

View full text Add to dashboard Cite

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Audiovisual biofeedback improves the correlation between internal/external surrogate motion and lung tumor motion

et al. 2018

View full text Add to dashboard Cite

show abstract

“…In five of the sequences, the breathing motion is normal and in the four remaining sequences, the individuals were asked to perform actions such as talking or laughing. More details concerning the dataset can be found in [16].…”

Section: Marker Position Datamentioning

confidence: 99%

“…High fluctuations of the prediction signal result in difficulties concerning robot control during the treatment. The jitter J is minimized when the prediction is constant, thus there is a trade-off between accuracy and jitter [16].…”

Section: Experimental Designmentioning

confidence: 99%

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

View full text Add to dashboard Cite

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)

show abstract

“…18) A prediction algorithm needs to allow fast calibration for each new patient and should be able to reflect, in real time, the movement of the tumor with the patient's respiration.…”

Section: Prediction Algorithmmentioning

confidence: 99%

Simulation and Experimental Studies of Real-Time Motion Compensation Using an Articulated Robotic Manipulator System

et al. 2017

View full text Add to dashboard Cite

The purpose of this study is to install a system that compensated for the respiration motion using an articulated robotic manipulator couch which enables a wide range of motions that a Stewart platform cannot provide and to evaluate the performance of various prediction algorithms including proposed algorithm. For that purpose, we built a miniature couch tracking system comprising an articulated robotic manipulator, 3D optical tracking system, a phantom that mimicked respiratory motion, and control software. We performed simulations and experiments using respiratory data of 12 patients to investigate the feasibility of the system and various prediction algorithms, namely linear extrapolation (LE) and double exponential smoothing (ES2) with averaging methods. We confirmed that prediction algorithms worked well during simulation and experiment, with the ES2-averaging algorithm showing the best results. The simulation study showed 43% average and 49% maximum improvement ratios with the ES2-averaging algorithm, and the experimental study with the QUASAR TM phantom showed 51% average and 56% maximum improvement ratios with this algorithm. Our results suggest that the articulated robotic manipulator couch system with the ES2-averaging prediction algorithm can be widely used in the field of radiation therapy, providing a highly efficient and utilizable technology that can enhance the therapeutic effect and improve safety through a noninvasive approach.

show abstract

Predicting Respiratory Motion for Real-Time Tumour Tracking in Radiotherapy

Abstract: Conclusions. The developed algorithmic solution performs well to be prototyped and deployed in applications of radiotherapy.

Cited by 9 publications

References 38 publications

Audiovisual biofeedback improves the correlation between internal/external surrogate motion and lung tumor motion

Audiovisual biofeedback improves the correlation between internal/external surrogate motion and lung tumor motion

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

Simulation and Experimental Studies of Real-Time Motion Compensation Using an Articulated Robotic Manipulator System

Contact Info

Product

Resources

About