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"Deep learning for online adaptive radiotherapy"
Mohamed S. Elmahdy
Abstract
Adapting a radiotherapy treatment plan to the daily anatomy is a crucial task to ensure adequate irradiation of the target without unnecessary exposure of healthy tissue.This adaptation can be performed by automatically generating contours of the daily anatomy together with fast re-optimization of the treatment plan. These measurescan compensate for the daily variation and ensure the delivery of the prescribed dose distribution at small margins and high robustness settings. In this thesis, we focused on developing a deep learning-based methodology for automatic contouring for real-time adaptive radiotherapy either guided by CT or MR imaging modalities.
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Copyright © 2022 by the authors.
Personal use of this material is permitted. However, permission to reprint or republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the copyright holder.
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BibTeX entry
| @phdthesis{Elmahdy:2022, |
| author |
= {Elmahdy, Mohamed S.}, |
| title |
= {Deep learning for online adaptive radiotherapy}, |
| school |
= {Leiden University Medical Center}, |
| address |
= {Albinusdreef 2, 2333 ZA Leiden}, |
| month |
= {March}, |
| year |
= {2022}, |
| } |
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| last modified: 07-12-2023 |
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