Overview
- Recent research in Prediction and Classification of Respiratory Motion
- Introduction to recent algorithms describing respiratory motion
- Written by experts in the field
- Includes supplementary material: sn.pub/extras
Part of the book series: Studies in Computational Intelligence (SCI, volume 525)
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Table of contents (7 chapters)
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Front Matter
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Back Matter
Keywords
About this book
This book describes recent radiotherapy technologies including tools for measuring target position during radiotherapy and tracking-based delivery systems.Â
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 thetracking estimation value. The experimental results of 448 patients’ breathing patterns validated the proposed irregular breathing classifier in the last chapter.
Authors and Affiliations
Bibliographic Information
Book Title: Prediction and Classification of Respiratory Motion
Authors: Suk Jin Lee, Yuichi Motai
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-642-41509-8
Publisher: Springer Berlin, Heidelberg
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2014
Hardcover ISBN: 978-3-642-41508-1Published: 12 November 2013
Softcover ISBN: 978-3-662-51064-3Published: 27 August 2016
eBook ISBN: 978-3-642-41509-8Published: 25 October 2013
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: IX, 167
Number of Illustrations: 2 b/w illustrations, 65 illustrations in colour
Topics: Computational Intelligence, Artificial Intelligence, Health Informatics