000 04169nam a22005055i 4500
001 978-3-642-41509-8
003 DE-He213
005 20200421111843.0
007 cr nn 008mamaa
008 131025s2014 gw | s |||| 0|eng d
020 _a9783642415098
_9978-3-642-41509-8
024 7 _a10.1007/978-3-642-41509-8
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
100 1 _aLee, Suk Jin.
_eauthor.
245 1 0 _aPrediction and Classification of Respiratory Motion
_h[electronic resource] /
_cby Suk Jin Lee, Yuichi Motai.
264 1 _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
347 _atext file
_bPDF
_2rda
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.
650 0 _aHealth informatics.
650 0 _aArtificial intelligence.
650 0 _aComputational intelligence.
650 1 4 _aEngineering.
650 2 4 _aComputational Intelligence.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aHealth Informatics.
700 1 _aMotai, Yuichi.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642415081
830 0 _aStudies in Computational Intelligence,
_x1860-949X ;
_v525
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-41509-8
912 _aZDB-2-ENG
942 _cEBK
999 _c55667
_d55667