Application of Conditional Generative Adversarial Networks to Efficiently Generate Photon Phase Space in Medical Linear Accelerators of Different Primary Beam Parameters

Application of Conditional Generative Adversarial Networks to Efficiently Generate Photon Phase Space in Medical Linear Accelerators of Different Primary Beam Parameters

Successful application of external photon beam therapy in oncology requires that the dose delivered by a medical linear accelerator and distributed within the patient’s body is accurately calculated. Monte Carlo simulation is currently the most accurate method for this purpose but is computationally...

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Bibliographic Details
Main Authors: Mateusz Baran, Zbisław Tabor, Krzysztof Rzecki, Przemysław Ziaja, Tomasz Szumlak, Kamila Kalecińska, Jakub Michczyński, Bartłomiej Rachwał, Michael P. R. Waligórski, David Sarrut
Format: Article
Language:English
Published: MDPI AG 2023-06-01
Series:Applied Sciences
Subjects:
cancer
machine learning
medical simulations
neural networks
radiation therapy
Online Access:https://www.mdpi.com/2076-3417/13/12/7204

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https://www.mdpi.com/2076-3417/13/12/7204

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