MARC details
000 -LEADER |
fixed length control field |
04125nam a22004815i 4500 |
001 - CONTROL NUMBER |
control field |
978-3-642-41509-8 |
003 - CONTROL NUMBER IDENTIFIER |
control field |
DE-He213 |
005 - DATE AND TIME OF LATEST TRANSACTION |
control field |
20170628040135.0 |
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
fixed length control field |
cr nn 008mamaa |
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
fixed length control field |
131025s2014 gw | s |||| 0|eng d |
020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
International Standard Book Number |
9783642415098 |
-- |
978-3-642-41509-8 |
024 7# - OTHER STANDARD IDENTIFIER |
Standard number or code |
10.1007/978-3-642-41509-8 |
Source of number or code |
doi |
050 #4 - LIBRARY OF CONGRESS CALL NUMBER |
Classification number |
Q342 |
072 #7 - SUBJECT CATEGORY CODE |
Subject category code |
UYQ |
Source |
bicssc |
072 #7 - SUBJECT CATEGORY CODE |
Subject category code |
COM004000 |
Source |
bisacsh |
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER |
Classification number |
006.3 |
Edition number |
23 |
100 1# - MAIN ENTRY--PERSONAL NAME |
Personal name |
Lee, Suk Jin. |
Relator term |
author. |
245 10 - TITLE STATEMENT |
Title |
Prediction and Classification of Respiratory Motion |
Medium |
[electronic resource] / |
Statement of responsibility, etc |
by Suk Jin Lee, Yuichi Motai. |
264 #1 - |
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Berlin, Heidelberg : |
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Springer Berlin Heidelberg : |
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Imprint: Springer, |
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2014. |
300 ## - PHYSICAL DESCRIPTION |
Extent |
IX, 167 p. 67 illus., 65 illus. in color. |
Other physical details |
online resource. |
336 ## - |
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text |
-- |
txt |
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rdacontent |
337 ## - |
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computer |
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c |
-- |
rdamedia |
338 ## - |
-- |
online resource |
-- |
cr |
-- |
rdacarrier |
347 ## - |
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text file |
-- |
PDF |
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rda |
490 1# - SERIES STATEMENT |
Series statement |
Studies in Computational Intelligence, |
International Standard Serial Number |
1860-949X ; |
Volume number/sequential designation |
525 |
505 0# - FORMATTED CONTENTS NOTE |
Formatted contents note |
Review: 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 ## - SUMMARY, ETC. |
Summary, etc |
This 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 - SUBJECT ADDED ENTRY--TOPICAL TERM |
Topical term or geographic name as entry element |
Engineering. |
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
Topical term or geographic name as entry element |
Medical records |
General subdivision |
Data processing. |
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
Topical term or geographic name as entry element |
Artificial intelligence. |
650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM |
Topical term or geographic name as entry element |
Engineering. |
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM |
Topical term or geographic name as entry element |
Computational Intelligence. |
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM |
Topical term or geographic name as entry element |
Artificial Intelligence (incl. Robotics). |
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM |
Topical term or geographic name as entry element |
Health Informatics. |
700 1# - ADDED ENTRY--PERSONAL NAME |
Personal name |
Motai, Yuichi. |
Relator term |
author. |
710 2# - ADDED ENTRY--CORPORATE NAME |
Corporate name or jurisdiction name as entry element |
SpringerLink (Online service) |
773 0# - HOST ITEM ENTRY |
Title |
Springer eBooks |
776 08 - ADDITIONAL PHYSICAL FORM ENTRY |
Display text |
Printed edition: |
International Standard Book Number |
9783642415081 |
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE |
Uniform title |
Studies in Computational Intelligence, |
-- |
1860-949X ; |
Volume number/sequential designation |
525 |
856 40 - ELECTRONIC LOCATION AND ACCESS |
Uniform Resource Identifier |
<a href="http://dx.doi.org/10.1007/978-3-642-41509-8">http://dx.doi.org/10.1007/978-3-642-41509-8</a> |
912 ## - |
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ZDB-2-ENG |