Lung Tumor Motion Prediction by Neural Networks

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F12%3A00196637" target="_blank" >RIV/68407700:21220/12:00196637 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Lung Tumor Motion Prediction by Neural Networks
Original language description
In tracking radiotherapy, the respiratory motion of the lungs affects the precision of the radiation dose delivered into a moving lung tumor. Thus, it has been complicated to synchronize the radiation beam with the tumor position. For medical treatment,there is an objective to minimize inaccurate delivery of doses to healthy tissues surrounding the lung tumor during radiotherapy. In order to improve the efficiency of radiation tracking therapy, this thesis presents a study of neural-network predictiontechnique with retraining for real time prediction of lung tumor motion position. The study focuses not only on the classical perceptron model, but also on a class of higher-order neural network model, which has more attractive attributes regarding its optimization convergence and computational efficiency, which are discussed in detail in the thesis. The implemented static feed-forward neural architectures used the Levenberg-Marquardt algorithm, and the presented modification resulted in
Czech name
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Czech description
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Publication year
2012
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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