A Fast Neural Network Approach to Predict Lung Tumor Motion during Respiration for Radiation Therapy Applications
Identifikátory výsledku
Kód výsledku v 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>
Výsledek na webu
<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>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
A Fast Neural Network Approach to Predict Lung Tumor Motion during Respiration for Radiation Therapy Applications
Popis výsledku v původním jazyce
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.
Název v anglickém jazyce
A Fast Neural Network Approach to Predict Lung Tumor Motion during Respiration for Radiation Therapy Applications
Popis výsledku anglicky
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.
Klasifikace
Druh
J<sub>x</sub> - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)
CEP obor
BC - Teorie a systémy řízení
OECD FORD obor
—
Návaznosti výsledku
Projekt
—
Návaznosti
S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2015
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
BioMed Research International
ISSN
2314-6133
e-ISSN
—
Svazek periodika
2015(2015)
Číslo periodika v rámci svazku
2015
Stát vydavatele periodika
US - Spojené státy americké
Počet stran výsledku
13
Strana od-do
—
Kód UT WoS článku
000352887600001
EID výsledku v databázi Scopus
2-s2.0-84928313253