Identificacion Principal del registro INGC-EBK-000699

Control Number Identifier AR-LpUFI

LAST MODIFICATION DATE 20160824173837

CONTROL FIELD cr nn 008mamaa

CONTROL FIELD 131025s2014 gw | s |||| 0|eng d

a International Standard Book Number 9783642415098

a Standard number or code 10.1007/978-3-642-41509-8

a Personal name Lee, Suk Jin.

a Title Prediction and Classification of Respiratory Motion

h Medium [libro electrónico] /

c Statement of responsibility, etc by Suk Jin Lee, Yuichi Motai.

a Place of publication, distribution, etc Berlin, Heidelberg :

b Name of publisher, distributor, etc Springer Berlin Heidelberg :

b Name of publisher, distributor, etc Imprint: Springer,

c Date of publication, distribution, etc 2014.

a Extent ix, 167 p.

b Other physical details il.

a Series statement Studies in Computational Intelligence,

x International Standard Serial Number 1860-949X ;

v Volume number/sequential designation 525

a 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.

a 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 studyâ_"prediction of human motion with distributed body sensorsâ_"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 patientsâ_T breathing patterns validated the proposed irregular breathing classifier in the last chapter.

a Topical term or geographic name as entry element Engineering.

a Topical term or geographic name as entry element Computational Intelligence.

a Topical term or geographic name as entry element Artificial Intelligence (incl. Robotics).

a Topical term or geographic name as entry element Health Informatics.

a Personal name Motai, Yuichi.

u Uniform Resource Identifier (R) http://dx.doi.org/10.1007/978-3-642-41509-8

c item type EBK

a descripción COM