title: Ensemble framework based real-time respiratory motion prediction for adaptive radiotherapy applications.
abstract: 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.
authors.affiliation: School of Electronics Engineering, College of IT Engineering, Kyungpook National University, Daegu, South Korea.
authors.forename: Sivanagaraja
authors.initials: S
authors.lastname: Tatinati
authors.affiliation: School of Electrical and Electronic Engineering, Newcastle University, Newcastle-upon-Tyne NE1-7RU, UK; Institute of Neuroscience, Newcastle University, Newcastle-upon-Tyne NE2 4HH, UK.
authors.forename: Kianoush
authors.initials: K
authors.lastname: Nazarpour
authors.affiliation: School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore.
authors.forename: Wei
authors.initials: W
authors.lastname: Tech Ang
authors.affiliation: School of Electronics Engineering, College of IT Engineering, Kyungpook National University, Daegu, South Korea; School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore. Electronic address: veluvolu@ee.knu.ac.
authors.forename: Kalyana C
authors.initials: KC
authors.lastname: Veluvolu
mesh_terms: D000465:Algorithms; D009068:Movement; D011880:Radiotherapy Planning, Computer-Assisted; D011881:Radiotherapy, Computer-Assisted; D012119:Respiration; D060388:Support Vector Machine; D013997:Time Factors
keywords: Ensemble learning; Motion-adaptive radiotherapy; Nonlinear mapping; Respiratory motion prediction