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020 _a9783642415098
_9978-3-642-41509-8
024 7 _a10.1007/978-3-642-41509-8
_2doi
035 _ato000544839
040 _aSpringer
_cSpringer
_dRU-ToGU
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
100 1 _aLee, Suk Jin.
_eauthor.
_9452000
245 1 0 _aPrediction and Classification of Respiratory Motion
_helectronic resource
_cby Suk Jin Lee, Yuichi Motai.
260 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2014.
300 _aIX, 167 p. 67 illus., 65 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
490 1 _aStudies in Computational Intelligence,
_x1860-949X ;
_v525
505 0 _aReview: Prediction of Respiratory Motion -- Phantom: Prediction of Human Motion with Distributed Body Sensors -- Respiratory Motion Estimation with Hybrid Implementation -- Customized Prediction of Respiratory Motion -- Irregular Breathing Classification from Multiple Patient Datasets -- Conclusions and Contributions.
520 _aThis book describes recent radiotherapy technologies including tools for measuring target position during radiotherapy and tracking-based delivery systems. This book presents a customized prediction of respiratory motion with clustering from multiple patient interactions. The proposed method contributes to the improvement of patient treatments by considering breathing pattern for the accurate dose calculation in radiotherapy systems. Real-time tumor-tracking, where the prediction of irregularities becomes relevant, has yet to be clinically established. The statistical quantitative modeling for irregular breathing classification, in which commercial respiration traces are retrospectively categorized into several classes based on breathing pattern are discussed as well. The proposed statistical classification may provide clinical advantages to adjust the dose rate before and during the external beam radiotherapy for minimizing the safety margin. In the first chapter following the Introduction  to this book, we review three prediction approaches of respiratory motion: model-based methods, model-free heuristic learning algorithms, and hybrid methods. In the following chapter, we present a phantom study—prediction of human motion with distributed body sensors—using a Polhemus Liberty AC magnetic tracker. Next we describe respiratory motion estimation with hybrid implementation of extended Kalman filter. The given method assigns the recurrent neural network the role of the predictor and the extended Kalman filter the role of the corrector. After that, we present customized prediction of respiratory motion with clustering from multiple patient interactions. For the customized prediction, we construct the clustering based on breathing patterns of multiple patients using the feature selection metrics that are composed of a variety of breathing features. We have evaluated the new algorithm by comparing the prediction overshoot and the tracking estimation value. The experimental results of 448 patients’ breathing patterns validated the proposed irregular breathing classifier in the last chapter.
650 0 _aengineering.
_9224332
650 0 _aMedical records
_xData processing.
_9304692
650 0 _aArtificial intelligence.
_9274099
650 1 4 _aEngineering.
_9224332
650 2 4 _aComputational Intelligence.
_9307538
650 2 4 _aArtificial Intelligence (incl. Robotics).
_9274102
650 2 4 _aHealth Informatics.
_9303043
700 1 _aMotai, Yuichi.
_eauthor.
_9452001
710 2 _aSpringerLink (Online service)
_9143950
773 0 _tSpringer eBooks
830 0 _aStudies in Computational Intelligence,
_9305181
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-41509-8
912 _aZDB-2-ENG
999 _c402176