A Respiratory Motion Prediction Method Based on Improved Relevance Vector Machine

Fan, Qi; Yu, Xiaoyang; Zhao, Yanqiao; Yu, Shengsheng

doi:10.1007/s11036-020-01610-7

Cited by 4 publications

(4 citation statements)

References 23 publications

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“…Prediction of a confidence measure Correlation Prediction Correlation + prediction Ahn et al (2004), Fahmi et al (2018), Fayad et al (2011), Gierga et al (2005), Hoisak et al (2004), Ionascu et al (2007), Kanoulas et al (2007), Lee et al (2018), Mageras et al (2001), Ozhasoglu and Murphy (2002), Park et al (2018), Plathow et al (2005), Ruan et al (2008), Seppenwoolde et al (2007), Torshabi et al (2013), Užupytė et al (2017), Wilms et al (2014), Xu and Hamilton (2006), Yan et al (2006), yes no no no Beddar et al (2007), Berbeco et al (2005), Cerviño et al (2009), Chi et al (2006), , Feng et al (2009), Park et al (2016), Spadea et al (2010), Tsunashima et al (2004), Vedam et al (2003) yes no no partially Blanck et al (2012), Bukovsky et al (2010), Bukovsky et al (2015), Choi et al (2014), Dürichen et al (2013), Dürichen et al (2014), Ernst and Schweikard (2008), Ernst and Schweikard (2009), Ernst et al (2007), Ernst et al (2008), Ernst et al (2013), Fan et al (2020), Goodband and Haas (2008)…”

Section: Publications Goal(s)mentioning

confidence: 99%

Potential of a probabilistic framework for target prediction from surrogate respiratory motion during lung radiotherapy

et al. 2021

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Purpose. Respiration-induced motion introduces significant positioning uncertainties in radiotherapy treatments for thoracic sites. Accounting for this motion is a non-trivial task commonly addressed with surrogate-based strategies and latency compensating techniques. This study investigates the potential of a new unified probabilistic framework to predict both future target motion in real-time from a surrogate signal and associated uncertainty. Method. A Bayesian approach is developed, based on a Kalman filter theory adapted specifically for surrogate measurements. Breathing motions are collected simultaneously from a lung target, two external surrogates (abdominal and thoracic markers) and an internal surrogate (liver structure) for 9 volunteers during 4 min, in which severe breathing changes occur to assess the robustness of the method. A comparison with an artificial non-linear neural network (NN) is performed, although no confidence interval prediction is provided. A static worst-case scenario and a simple static design are investigated. Results. Although the NN can reduce the prediction errors from thoracic surrogate in some cases, the Bayesian framework outperforms in most cases the NN when using the other surrogates: bias on predictions is reduced by 38% and 16% on average when using respectively the liver and the abdomen for the simple scenario, and by respectively 40% and 31% for the worst-case scenario. The standard deviation of residuals is reduced on average by up to 42%. The Bayesian method is also found to be more robust to increasing latencies. The thoracic marker appears to be less reliable to predict the target position, while the liver shows to be a better surrogate. A statistical test confirms the significance of both observations. Conclusion. The proposed framework predicts both the future target position and the associated uncertainty, which can be valuably used to further assist motion management decisions. Further investigation is required to improve the predictions by using an adaptive version of the proposed framework.

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Section: Publications Goal(s)mentioning

confidence: 99%

Potential of a probabilistic framework for target prediction from surrogate respiratory motion during lung radiotherapy

et al. 2021

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“…Fan et al. employs relevance vector machines to construct a motion trajectory model for external markers on the chest 8 . Wang et al.…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, previous studies in surrogate signal selection and processing possess inherent limitations. Some studies require the consistent use of X‐ray or cone‐beam CT for patient monitoring, which is associated with radioactivity and potential harm 8,10 . Additionally, most studies focus on employing machine learning or simple surrogate signals like optical markers to construct respiratory correlation models between internal and surrogate signals 7,9,14 .…”

Section: Introductionmentioning

confidence: 99%

“…Some studies require the consistent use of X-ray or cone-beam CT for patient monitoring, which is associated with radioactivity and potential harm. 8,10 Additionally, most studies focus on employing machine learning or simple surrogate signals like optical markers to construct respiratory correlation models between internal and surrogate signals. 7,9,14 These approaches often lead to insufficient accuracy in tracking real patients' respiration.…”

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A back propagation neural network based respiratory motion modelling method

Jiang,

Li,

Yang

et al. 2024

Robotics Computer Surgery

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BackgroundThis study presents the development of a backpropagation neural network‐based respiratory motion modelling method (BP‐RMM) for precisely tracking arbitrary points within lung tissue throughout free respiration, encompassing deep inspiration and expiration phases.MethodsInternal and external respiratory data from four‐dimensional computed tomography (4DCT) are processed using various artificial intelligence algorithms. Data augmentation through polynomial interpolation is employed to enhance dataset robustness. A BP neural network is then constructed to comprehensively track lung tissue movement.ResultsThe BP‐RMM demonstrates promising accuracy. In cases from the public 4DCT dataset, the average target registration error (TRE) between authentic deep respiration phases and those forecasted by BP‐RMM for 75 marked points is 1.819 mm. Notably, TRE for normal respiration phases is significantly lower, with a minimum error of 0.511 mm.ConclusionsThe proposed method is validated for its high accuracy and robustness, establishing it as a promising tool for surgical navigation within the lung.

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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. 2022

Computer Methods and Programs in Biomedicine

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A Respiratory Motion Prediction Method Based on Improved Relevance Vector Machine

Cited by 4 publications

References 23 publications

Potential of a probabilistic framework for target prediction from surrogate respiratory motion during lung radiotherapy

Potential of a probabilistic framework for target prediction from surrogate respiratory motion during lung radiotherapy

A back propagation neural network based respiratory motion modelling method

Prediction of the position of external markers using a recurrent neural network trained with unbiased online recurrent optimization for safe lung cancer radiotherapy

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