A Fast Neural Network Approach to Predict Lung Tumor Motion during Respiration for Radiation Therapy Applications

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F15%3A00228642" target="_blank" >RIV/68407700:21220/15:00228642 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1155/2015/489679" target="_blank" >http://dx.doi.org/10.1155/2015/489679</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1155/2015/489679" target="_blank" >10.1155/2015/489679</a>

Result language

angličtina

Original language name

A Fast Neural Network Approach to Predict Lung Tumor Motion during Respiration for Radiation Therapy Applications

Original language description

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 of total treatment time.

Czech name

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Czech description

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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

BC - Theory and management systems

OECD FORD branch

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Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2015

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

BioMed Research International

ISSN

2314-6133

e-ISSN

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Volume of the periodical

2015(2015)

Issue of the periodical within the volume

2015

Country of publishing house

US - UNITED STATES

Number of pages

13

Pages from-to

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UT code for WoS article

000352887600001

EID of the result in the Scopus database

2-s2.0-84928313253