Erschienen: 27.08.2016 Abbildung von Lee / Motai | Prediction and Classification of Respiratory Motion | Softcover reprint of the original 1st ed. 2014 | 2016 | 525

Lee / Motai

Prediction and Classification of Respiratory Motion

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119,99 €

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Softcover reprint of the original 1st ed. 2014 2016. Buch. ix, 167 S. 2 s/w-Abbildungen, 65 Farbabbildungen, Bibliographien. Softcover

Springer. ISBN 978-3-662-51064-3

Format (B x L): 15,5 x 23,5 cm

Gewicht: 285 g

In englischer Sprache

Produktbeschreibung

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.

Gesamtwerk

Die 8. Auflage ist wieder auf sechs Bände angelegt. Darin finden sich übersichtlich und in systematischer Gliederung Vertragsmuster aus der Feder erfahrener Experten. Jedem dieser Muster folgen Anmerkungen, mit denen der dem Vertragsentwurf zu Grunde liegende Sachverhalt und die Gründe für die Wahl des spezifischen Formulars erläutert werden.

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