Deep learning for online adaptive radiotherapy

Elmahdy, M.S.E. (2022)

Doctoral Thesis

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

 

• Radiotherapy
• Registration
• Segmentation
• Prostate
• CT
• Transfer Learning
• MRI Reconstruction
• Adaptive treatment

• All authors: Elmahdy, M.S.E.
• Supervisor: Heide, U.A. van der; Lelieveldt, B.P.F.
• Co-supervisor: Staring, M.
• Committee: Rasch, C.; Osch, M.J.P. van; Pluim, J.P.W.; Isgum, I.
• Qualification: Doctor (dr.)
• Awarding Institution: Faculty of Medicine, Leiden University Medical Center (LUMC), Leiden University
• Date: 2022-03-15
• ISBN (print): 9789492597939