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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.
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.
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