A Fast Neural Network Approach to Predict Lung Tumor Motion during Respiration for Radiation Therapy Applications
Joint Authors
Ichiji, Kei
Cejnek, Matous
Slama, Matous
Benes, Peter M.
Bila, Jiri
Homma, Noriyasu
Bukovsky, Ivo
Source
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-03-29
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
During radiotherapy treatment for thoracic and abdomen cancers, for example, lung cancers, respiratory motion moves the target tumor and thus badly affects the accuracy of radiation dose delivery into the target.
A real-time image-guided technique can be used to monitor such lung tumor motion for accurate dose delivery, but the system latency up to several hundred milliseconds for repositioning the radiation beam also affects the accuracy.
In order to compensate the latency, neural network prediction technique with real-time retraining can be used.
We have investigated real-time prediction of 3D time series of lung tumor motion on a classical linear model, perceptron model, and on a class of higher-order neural network model that has more attractive attributes regarding its optimization convergence and computational efficiency.
The implemented static feed-forward neural architectures are compared when using gradient descent adaptation and primarily the Levenberg-Marquardt batch algorithm as the ones of the most common and most comprehensible learning algorithms.
The proposed technique resulted in fast real-time retraining, so the total computational time on a PC platform was equal to or even less than the real treatment time.
For one-second prediction horizon, the proposed techniques achieved accuracy less than one millimeter of 3D mean absolute error in one hundred seconds oftotal treatment time.
American Psychological Association (APA)
Bukovsky, Ivo& Homma, Noriyasu& Ichiji, Kei& Cejnek, Matous& Slama, Matous& Benes, Peter M.…[et al.]. 2015. A Fast Neural Network Approach to Predict Lung Tumor Motion during Respiration for Radiation Therapy Applications. BioMed Research International،Vol. 2015, no. 2015, pp.1-13.
https://search.emarefa.net/detail/BIM-1055675
Modern Language Association (MLA)
Bukovsky, Ivo…[et al.]. A Fast Neural Network Approach to Predict Lung Tumor Motion during Respiration for Radiation Therapy Applications. BioMed Research International No. 2015 (2015), pp.1-13.
https://search.emarefa.net/detail/BIM-1055675
American Medical Association (AMA)
Bukovsky, Ivo& Homma, Noriyasu& Ichiji, Kei& Cejnek, Matous& Slama, Matous& Benes, Peter M.…[et al.]. A Fast Neural Network Approach to Predict Lung Tumor Motion during Respiration for Radiation Therapy Applications. BioMed Research International. 2015. Vol. 2015, no. 2015, pp.1-13.
https://search.emarefa.net/detail/BIM-1055675
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1055675