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Studies in Computational Intelligence

Prediction and Classification of Respiratory Motion

Authors: Lee, Suk Jin, Motai, Yuichi

  • Recent research in Prediction and Classification of Respiratory Motion
  • Introduction to recent algorithms describing respiratory motion
  • Written by experts in the field
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  • Due: October 13, 2016
  • ISBN 978-3-662-51064-3
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About this book

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.

Table of contents (7 chapters)

  • Introduction

    Lee, Suk Jin (et al.)

    Pages 1-5

  • Review: Prediction of Respiratory Motion

    Lee, Suk Jin (et al.)

    Pages 7-37

  • Phantom: Prediction of Human Motion with Distributed Body Sensors

    Lee, Suk Jin (et al.)

    Pages 39-66

  • Respiratory Motion Estimation with Hybrid Implementation

    Lee, Suk Jin (et al.)

    Pages 67-89

  • Customized Prediction of Respiratory Motion

    Lee, Suk Jin (et al.)

    Pages 91-107

Buy this book

eBook $99.00
price for USA (gross)
  • ISBN 978-3-642-41509-8
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $129.00
price for USA
  • ISBN 978-3-642-41508-1
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $129.00
price for USA
  • Customers within the U.S. and Canada please contact Customer Service at 1-800-777-4643, Latin America please contact us at +1-212-460-1500 (Weekdays 8:30am – 5:30pm ET) to place your order.
  • Due: October 13, 2016
  • ISBN 978-3-662-51064-3
  • Free shipping for individuals worldwide
Rent the ebook  
  • Rental duration: 1 or 6 month
  • low-cost access
  • online reader with highlighting and note-making option
  • can be used across all devices
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Bibliographic Information

Bibliographic Information
Book Title
Prediction and Classification of Respiratory Motion
Authors
Series Title
Studies in Computational Intelligence
Series Volume
525
Copyright
2014
Publisher
Springer-Verlag Berlin Heidelberg
Copyright Holder
Springer-Verlag Berlin Heidelberg
eBook ISBN
978-3-642-41509-8
DOI
10.1007/978-3-642-41509-8
Hardcover ISBN
978-3-642-41508-1
Softcover ISBN
978-3-662-51064-3
Series ISSN
1860-949X
Edition Number
1
Number of Pages
IX, 167
Number of Illustrations and Tables
2 b/w illustrations, 65 illustrations in colour
Topics