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Title: Predictive Local Receptive Fields Based Respiratory Motion Tracking For Motion-Adaptive Radiotherapy
Authors: Wang, Yubo
Tatinati, Sivanagaraja
Huang, Liyu
Hong, Kim Jeong
Shafiq, Ghufran
Veluvolu, Kalyana C.
Hoong, Andy Khong Wai
Keywords: Local Receptive Fields
Motion-Adaptive Radiotherapy
Issue Date: 2017
Source: Wang, Y., Tatinati, S., Huang, L., Hong, K. J., Shafiq, G., Veluvolu, K. C., et al. (2017). Predictive Local Receptive Fields Based Respiratory Motion Tracking For Motion-Adaptive Radiotherapy. 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
Conference: 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Abstract: Extracranial robotic radiotherapy employs external markers and a correlation model to trace the tumor motion caused by the respiration. The real-time tracking of tumor motion however requires a prediction model to compensate the latencies induced by the software (image data acquisition and processing) and hardware (mechanical and kinematic) limitations of the treatment system. A new prediction algorithm based on local receptive fields extreme learning machines (pLRF-ELM) is proposed for respiratory motion prediction. All the existing respiratory motion prediction methods model the non-stationary respiratory motion traces directly to predict the future values. Unlike these existing methods, the pLRFELM performs prediction by modeling the higher-level features obtained by mapping the raw respiratory motion into the random feature space of ELM instead of directly modeling the raw respiratory motion. The developed method is evaluated using the dataset acquired from 31 patients for two horizons in-line with the latencies of treatment systems like CyberKnife. Results showed that pLRF-ELM is superior to that of existing prediction methods. Results further highlight that the abstracted higher-level features are suitable to approximate the nonlinear and non-stationary characteristics of respiratory motion for accurate prediction.
DOI: 10.1109/EMBC.2017.8037453
Schools: School of Electrical and Electronic Engineering 
Rights: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [].
Fulltext Permission: open
Fulltext Availability: With Fulltext
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