dc.contributor.authorWang, Yubo
dc.contributor.authorTatinati, Sivanagaraja
dc.contributor.authorHuang, Liyu
dc.contributor.authorHong, Kim Jeong
dc.contributor.authorShafiq, Ghufran
dc.contributor.authorVeluvolu, Kalyana C.
dc.contributor.authorHoong, Andy Khong Wai
dc.date.accessioned2017-07-18T07:28:39Z
dc.date.available2017-07-18T07:28:39Z
dc.date.copyright2017-07-01
dc.date.issued2017
dc.identifier.citationWang, 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.en_US
dc.identifier.urihttp://hdl.handle.net/10220/42910
dc.description.abstractExtracranial 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.en_US
dc.format.extent4 p.en_US
dc.language.isoenen_US
dc.relation.ispartofseries
dc.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: [http://dx.doi.org/10.1109/EMBC.2017.8037453].en_US
dc.subjectMotion-Adaptive Radiotherapyen_US
dc.subjectLocal Receptive Fieldsen_US
dc.titlePredictive Local Receptive Fields Based Respiratory Motion Tracking For Motion-Adaptive Radiotherapyen_US
dc.typeConference Paper
dc.contributor.conference39th Annual International Conference of the IEEE Engineering in Medicine and Biology Societyen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.identifier.doihttp://dx.doi.org/10.1109/EMBC.2017.8037453
dc.description.versionAccepted versionen_US
dc.identifier.rims199464


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