von Mühlenen, Nair Nan. Reinforcement Learning for Automatic Dose Control in Radio Therapy. 2021, Master Thesis, University of Basel, Faculty of Medicine.
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Official URL: https://edoc.unibas.ch/88240/
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Abstract
Proton therapy is a treatment method for various cancer types. One of the challenges found while performing proton therapy is motion management. Especially the breathing motion of the patient poses a challenge. We propose an artificial intelligence (AI) trained with reinforcement learning (RL) to control the delivery of proton therapy and actively adapting to the breathing motion of the patient. The real-life environment of treatment delivery was simplified into a virtual 2D environment, and in a first step a static target was used to represent the tumour. We used an advantage actor-critic (A2C) model in combination with different neural networks (NN) to train on the virtual environment. The network which combined a simple convolutional neural network (CNN) with a long-short term memory cell (LSTMcell) performed better that the networks that only composed of a simple CNN. We were able to show that with an adequate network the agent was able to hit the target and that it was able to improve performance over time.
Advisors: | Cattin, Philippe Claude |
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Committee Members: | Sandkühler, Robin |
Faculties and Departments: | 03 Faculty of Medicine > Departement Biomedical Engineering > Imaging and Computational Modelling > Center for medical Image Analysis & Navigation (Cattin) |
UniBasel Contributors: | Cattin, Philippe Claude |
Item Type: | Thesis |
Thesis Subtype: | Master Thesis |
Thesis no: | UNSPECIFIED |
Thesis status: | Complete |
Language: | English |
edoc DOI: | |
Last Modified: | 27 Apr 2022 04:30 |
Deposited On: | 26 Apr 2022 09:32 |
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