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Simulation du mouvement pulmonaire personnalisé par réseau de neurones artificiels pour la radiothérapie externe

Abstract : The development of new techniques in the field of external radiotherapy opens new ways of gaining accuracy in dose distribution, in particular through the knowledge of individual lung motion. The numeric simulation NEMOSIS (Neural Network Motion Simulation System) we describe is based on artificial neural networks (ANN) and allows, in addition to determining motion in a personalized way, to reduce the necessary initial doses to determine it. In the first part, we will present current treatment options, lung motion as well as existing simulation or estimation methods. The second part describes the artificial neural network used and the steps for defining its parameters. An accurate evaluation of our approach was carried out on original patient data. The obtained results are compared with an existing motion estimated method. The extremely short computing time, in the range of milliseconds for the generation of one respiratory phase, would allow its use in clinical routine. Modifications to NEMOSIS in order to meet the requirements for its use in external radiotherapy are described, and a study of the motion of tumor outlines is carried out. This work lays the basis for lung motion simulation with ANNs and validates our approach. Its real time implementation coupled to its predication accuracy makes NEMOSIS promising tool for the simulation of motion synchronized with breathing.
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Submitted on : Wednesday, March 13, 2013 - 3:57:14 PM
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  • HAL Id : tel-00800360, version 1


Rémy Laurent. Simulation du mouvement pulmonaire personnalisé par réseau de neurones artificiels pour la radiothérapie externe. Médecine humaine et pathologie. Université de Franche-Comté, 2011. Français. ⟨NNT : 2011BESA2008⟩. ⟨tel-00800360⟩



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