Inferential modeling and predictive feedback control in real-time motion compensation using the treatment couch during radiotherapy.
Tumor motion induced by respiration presents a challenge to the reliable delivery of conformal radiation treatments. Real-time motion compensation represents the technologically most challenging clinical solution but has the potential to overcome the limitations of existing methods. The performance of a real-time couch-based motion compensation system is mainly dependent on two aspects: the ability to infer the internal anatomical position and the performance of the feedback control system. In this paper, we propose two novel methods for the two aspects respectively, and then combine the proposed methods into one system. To accurately estimate the internal tumor position, we present partial-least squares (PLS) regression to predict the position of the diaphragm using skin-based motion surrogates. Four radio-opaque markers were placed on the abdomen of patients who underwent fluoroscopic imaging of the diaphragm. The coordinates of the markers served as input variables and the position of the diaphragm served as the output variable. PLS resulted in lower prediction errors compared with standard multiple linear regression (MLR). The performance of the feedback control system depends on the system dynamics and dead time (delay between the initiation and execution of the control action). While the dynamics of the system can be inverted in a feedback control system, the dead time cannot be inverted. To overcome the dead time of the system, we propose a predictive feedback control system by incorporating forward prediction using least-mean-square (LMS) and recursive least square (RLS) filtering into the couch-based control system. Motion data were obtained using a skin-based marker. The proposed predictive feedback control system was benchmarked against pure feedback control (no forward prediction) and resulted in a significant performance gain. Finally, we combined the PLS inference model and the predictive feedback control to evaluate the overall performance of the feedback control system. Our results show that, with the tumor motion unknown but inferred by skin-based markers through the PLS model, the predictive feedback control system was able to effectively compensate intra-fraction motion.
Qiu
Peng
P
Department of Electrical and Computer Engineering, University of Maryland, College Park, MD 20742, USA.
D'Souza
Warren D
WD
McAvoy
Thomas J
TJ
Ray Liu
K J
KJ
D000465:Algorithms; D016477:Artifacts; D001513:Beds; D003198:Computer Simulation; D003199:Computer Systems; D005246:Feedback; D008954:Models, Biological; D009068:Movement; D009369:Neoplasms / Q000000981:diagnostic imaging* / Q000532:radiotherapy*; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D011881:Radiotherapy, Computer-Assisted / Q000379:methods; D020266:Radiotherapy, Conformal / Q000379:methods; D015656:Respiratory Mechanics; D012149:Restraint, Physical / Q000379:methods*; D012371:Robotics / Q000379:methods*