Ensemble framework based real-time respiratory motion prediction for adaptive radiotherapy applications

Sivanagaraja Tatinati, Kianoush Nazarpour, Wei Tech Ang, Kalyana C. Veluvolu

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Successful treatment of tumors with motion-adaptive radiotherapy requires accurate prediction of respiratory motion, ideally with a prediction horizon larger than the latency in radiotherapy system. Accurate prediction of respiratory motion is however a non-trivial task due to the presence of irregularities and intra-trace variabilities, such as baseline drift and temporal changes in fundamental frequency pattern. In this paper, to enhance the accuracy of the respiratory motion prediction, we propose a stacked regression ensemble framework that integrates heterogeneous respiratory motion prediction algorithms. We further address two crucial issues for developing a successful ensemble framework: (1) selection of appropriate prediction methods to ensemble (level-0 methods) among the best existing prediction methods; and (2) finding a suitable generalization approach that can successfully exploit the relative advantages of the chosen level-0 methods. The efficacy of the developed ensemble framework is assessed with real respiratory motion traces acquired from 31 patients undergoing treatment. Results show that the developed ensemble framework improves the prediction performance significantly compared to the best existing methods.
Original languageEnglish
Pages (from-to)749-757
Number of pages9
JournalMedical Engineering and Physics
Issue number8
Early online date26 May 2016
Publication statusPublished - 1 Aug 2016

Keywords / Materials (for Non-textual outputs)

  • Motion-adaptive radiotherapy
  • Respiratory motion prediction
  • Ensemble learning
  • Nonlinear mapping


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