title: Real-time prediction of respiratory motion traces for radiotherapy with ensemble learning.
abstract: In this paper, we introduce a hybrid method for prediction of respiratory motion to overcome the inherent delay in robotic radiosurgery while treating lung tumors. The hybrid method adopts least squares support vector machine (LS-SVM) based ensemble learning approach to exploit the relative advantages of the individual methods local circular motion (LCM) with extended Kalman filter (EKF) and autoregressive moving average (ARMA) model with fading memory Kalman filter (FMKF). The efficiency the proposed hybrid approach was assessed with the real respiratory motion traces of 31 patients while treating with CyberKnife(TM). Results show that the proposed hybrid method improves the prediction accuracy by approximately 10% for prediction horizons of 460 ms compared to the existing methods.
authors.forename: Sivanagaraja
authors.initials: S
authors.lastname: Tatinati
authors.forename: Kalyana C
authors.initials: KC
authors.lastname: Veluvolu
authors.lastname: Sun-Mog Hong
authors.forename: Kianoush
authors.initials: K
authors.lastname: Nazarpour
mesh_terms: D000465:Algorithms; D006801:Humans; D016018:Least-Squares Analysis; D008175:Lung Neoplasms / Q000532:radiotherapy / Q000601:surgery*; D008962:Models, Theoretical; D016343:Monitoring, Intraoperative / Q000379:methods*; D009038:Motion; D025341:Principal Component Analysis; D016634:Radiosurgery / Q000379:methods*; D012119:Respiration; D012371:Robotics / Q000379:methods; D060388:Support Vector Machine