title: Real-time prediction and gating of respiratory motion using an extended Kalman filter and Gaussian process regression.
abstract: Motion-adaptive radiotherapy aims to deliver a conformal dose to the target tumour with minimal normal tissue exposure by compensating for tumour motion in real time. The prediction as well as the gating of respiratory motion have received much attention over the last two decades for reducing the targeting error of the treatment beam due to respiratory motion. In this article, we present a real-time algorithm for predicting and gating respiratory motion that utilizes a model-based and a model-free Bayesian framework by combining them in a cascade structure. The algorithm, named EKF-GPR(+), implements a gating function without pre-specifying a particular region of the patient's breathing cycle. The algorithm first employs an extended Kalman filter (LCM-EKF) to predict the respiratory motion and then uses a model-free Gaussian process regression (GPR) to correct the error of the LCM-EKF prediction. The GPR is a non-parametric Bayesian algorithm that yields predictive variance under Gaussian assumptions. The EKF-GPR(+) algorithm utilizes the predictive variance from the GPR component to capture the uncertainty in the LCM-EKF prediction error and systematically identify breathing points with a higher probability of large prediction error in advance. This identification allows us to pause the treatment beam over such instances. EKF-GPR(+) implements the gating function by using simple calculations based on the predictive variance with no additional detection mechanism. A sparse approximation of the GPR algorithm is employed to realize EKF-GPR(+) in real time. Extensive numerical experiments are performed based on a large database of 304 respiratory motion traces to evaluate EKF-GPR(+). The experimental results show that the EKF-GPR(+) algorithm effectively reduces the prediction error in a root-mean-square (RMS) sense by employing the gating function, albeit at the cost of a reduced duty cycle. As an example, EKF-GPR(+) reduces the patient-wise RMS error to 37%, 39% and 42% in percent ratios relative to no prediction for a duty cycle of 80% at lookahead lengths of 192 ms, 384 ms and 576 ms, respectively. The experiments also confirm that EKF-GPR(+) controls the duty cycle with reasonable accuracy.
authors.affiliation: School of Electronics Engineering, Kyungpook National University, Deagu, Korea.
authors.forename: W
authors.initials: W
authors.lastname: Bukhari
authors.forename: S-M
authors.initials: SM
authors.lastname: Hong
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D006801:Humans; D008962:Models, Theoretical; D009068:Movement; D009369:Neoplasms / Q000503:physiopathology / Q000532:radiotherapy*; D016571:Neural Networks, Computer; D016011:Normal Distribution; D011857:Radiographic Image Interpretation, Computer-Assisted; D011881:Radiotherapy, Computer-Assisted / Q000379:methods*; D012044:Regression Analysis; D012119:Respiration; D015656:Respiratory Mechanics; D060388:Support Vector Machine