NDLI: Comparison between acceleration-enhanced adaptive filters and neural network filters for respiratory motion prediction

Content Provider	IEEE Xplore Digital Library
Author	Buzurovic, I. Ke Huang Podder, T.K. Yan Yu
Copyright Year	2012
Description	Author affiliation: East Carolina University, Medical Physics Division, Greenville, NC, USA (Podder, T.K.) \|\| University of Michigan, Medical Physics Division, Ann Arbor, USA (Ke Huang) \|\| Thomas Jefferson University, Medical Physics Division, Philadelphia, PA, USA (Buzurovic, I.; Yan Yu)
Abstract	The prediction of respiration-induced organ motion is crucial in some applications such as dynamic delivery of radiation dose. In this paper, we have proposed the novel approach to construct an acceleration-enhanced (AE) filter that is comprised of two independent adaptive channels. The filters use the adapted position and adapted acceleration, together with a weight factor to provide prediction for respiratory motion. The proposed AE approach is universal and can be applied to the different filters. The performances of the adaptive normalized least mean square (nLMS) filter, the artificial neural network (ANN) filter, and their AE counterparts were compared for respiratory motion prediction during normal and irregular respiration. The results revealed that the adaptive ANN and nLMS filters were successful to perform predictions for normal and irregular respiration, respectively. AE filters showed more accurate prediction than their conventional counterparts. Implementing the AE approach, it was observed that the AE-ANN filter had the best performance in the prediction of normal respiratory motion, whereas the AE-nLMS filter excelled in the prediction of irregular respiratory motion.
Starting Page	181
Ending Page	184
File Size	521663
Page Count	4
File Format	PDF
ISBN	9781467315692
e-ISBN	9781467315722
DOI	10.1109/NEUREL.2012.6420003
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-09-20
Publisher Place	Serbia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Adaptive filters Artificial neural networks Acceleration Training Filtering algorithms Vectors Tumors prediction algorithms neural network filters
Content Type	Text
Resource Type	Article

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