Prediction and Classification of Respiratory Motion

verfasst von: Suk Jin Lee, Yuichi Motai

Verlag: Springer Berlin Heidelberg

Buchreihe : Studies in Computational Intelligence

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

Einloggen, um Zugang zu erhalten

Über dieses Buch

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.

Inhaltsverzeichnis

Frontmatter

Chapter 1. Introduction

Abstract

Rapid developments in radiotherapy systems open a new era for the treatment of thoracic and abdominal tumors with accurate dosimetry [1].

Suk Jin Lee, Yuichi Motai

Chapter 2. Review: Prediction of Respiratory Motion

Abstract

Radiation therapy is a cancer treatment method that employs high-energy radiation beams to destroy cancer cells by damaging the ability of these cells to reproduce [1].

Suk Jin Lee, Yuichi Motai

Chapter 3. Phantom: Prediction of Human Motion with Distributed Body Sensors

Abstract

Tracking human motion with distributed body sensors has the potential to promote a large number of applications such as health care, medical monitoring, and sports medicine. In distributed sensory systems, the system architecture and data processing cannot perform the expected outcomes because of the limitations of data association. For the collaborative and complementary applications of motion tracking (Polhemus Liberty AC magnetic tracker), we propose a distributed sensory system with multi-channel interacting multiple model estimator (MC-IMME). To figure out interactive relationships among distributed sensors, we used a Gaussian mixture model (GMM) for clustering. With a collaborative grouping method based on GMM and expectation-maximization (EM) algorithm for distributed sensors, we can estimate the interactive relationship of multiple sensor channels and achieve the efficient target estimation to employ a tracking relationship within a cluster. Using multiple models with improved control of filter divergence, the proposed MC-IMME can achieve the efficient estimation of the measurement as well as the velocity from measured datasets with distributed sensory data. We have newly developed MC-IMME to improve overall performance with a Markov switch probability and a proper grouping method. The experiment results showed that the prediction overshoot error can be improved in the average 19.31 % with employing a tracking relationship.

Suk Jin Lee, Yuichi Motai

Chapter 4. Respiratory Motion Estimation with Hybrid Implementation

Abstract

The extended Kalman filter (EKF) can be used for the purpose of training nonlinear neural networks to perform desired input–output mappings. To improve the computational requirements of the EKF, Puskorius et al. proposed the decoupled extended Kalman filter (DEKF) as a practical remedy for the proper management of computational resources. This approach, however, sacrifices computational accuracy of estimates because it ignores the interactions between the estimates of mutually exclusive weights. To overcome such a limitation, therefore, we proposed hybrid implementation based on EKF (HEKF) for respiratory motion estimate, which uses the channel number for the mutually exclusive groups and the coupling technique to compensate the computational accuracy. Moreover, the authors restricted to a DEKF algorithm for which the weights connecting inputs to a node are grouped together. If there are multiple input training sequences with respect to time stamp, the complexity can increase by the power of input channel number. To improve the computational complexity, we split the complicated neural network into a couple of the simple neural networks to adjust separate input channels. The experiment results validated that the prediction overshoot of the proposed HEKF was improved by 62.95 % in the average prediction overshoot values. The proposed HEKF showed the better performance by 52.40 % improvement in the average of the prediction time horizon. We have evaluated that a proposed HEKF can outperform DEKF by comparing the prediction overshoot values, the performance of tracking estimation value and the normalized root mean squared error (NRMSE).

Suk Jin Lee, Yuichi Motai

Chapter 5. Customized Prediction of Respiratory Motion

Abstract

Accurate prediction of the respiratory motion would be beneficial to the treatment of thoracic and abdominal tumors. However, a wide variety of breathing patterns can make it difficult to predict the breathing motion with explicit models. We proposed a respiratory motion predictor, i.e., customized prediction with multiple patient interactions using Neural network (CNN).

Suk Jin Lee, Yuichi Motai

Chapter 6. Irregular Breathing Classification from Multiple Patient Datasets

Abstract

Complicated breathing behaviors including uncertain and irregular patterns can affect the accuracy of predicting respiratory motion for precise radiation dose delivery [1‐6]. So far investigations on irregular breathing patterns have been limited to respiratory monitoring of only extreme inspiration and expiration [7]. Using breathing traces acquired on a Cyberknife treatment facility, we retrospectively categorized breathing data into several classes based on the extracted feature metrics derived from breathing data of multiple patients. The novelty of this study is that the classifier using neural networks can provide clinical merit for the statistically quantitative modeling of irregular breathing motion based on a regular ratio representing how many regular/irregular patterns exist within an observation period. We propose a new approach to detect irregular breathing patterns using neural networks, where the reconstruction error can be used to build the distribution model for each breathing class. The sensitivity, specificity and Receiver operating characteristic (ROC) curve of the proposed irregular breathing pattern detector was analyzed. The experimental results of 448 patients’ breathing patterns validated the proposed irregular breathing classifier.

Suk Jin Lee, Yuichi Motai

Chapter 7. Conclusions and Contributions

Abstract

The following conclusions can be made from the results obtained from Chap. 4

Suk Jin Lee, Yuichi Motai

Backmatter

Titel: Prediction and Classification of Respiratory Motion
verfasst von: Suk Jin Lee
Yuichi Motai
Verlag: Springer Berlin Heidelberg
Electronic ISBN: 978-3-642-41509-8
Print ISBN: 978-3-642-41508-1
DOI: https://doi.org/10.1007/978-3-642-41509-8