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Adaptive radiotherapy: The Elekta Unity MR-linac concept
Dennis Winkel1, Gijsbert H Bol1, Petra S Kroon1
1Department of Radiotherapy, University Medical Center, Utrecht, The Netherlands.
Clinical and Translational Radiation Oncology
|July 26, 2019
Summary
Daily full online replanning on the MR-linac is the most robust method for adaptive radiotherapy, proving feasible for specific sites within acceptable times. Faster methods require careful dosimetric evaluation for their specific use cases.
Area of Science:
- Medical Physics
- Radiation Oncology
- Radiotherapy Technology
Background:
- The MR-linac enables visualization of anatomical changes during radiotherapy for plan adaptation.
- A trade-off exists between adaptation time and dosimetric gain in adaptive radiotherapy.
- State-of-the-art 1.5 T MRI-guided online adaptive radiotherapy methods require evaluation.
Purpose of the Study:
- To present and apply various daily plan adaptation methods for MR-linac radiotherapy.
- To gain insight into the behavior of 1.5 T MRI-guided online adaptive radiotherapy.
- To evaluate the feasibility and robustness of different adaptive planning strategies across various tumor sites.
Main Methods:
- Simulated online plan adaptation for five diverse cases: prostate, rectum, esophagus, and lymph node oligometastases (single and multiple targets).
- Evaluated plans based on clinical dose constraints.
- Measured the time required for plan adaptation and optimization.
Main Results:
- Plan adaptation time varied significantly, ranging from 17 to 485 seconds.
- More advanced adaptation methods generally yielded more plans meeting clinical dose criteria.
- Full online replanning successfully created plans meeting all clinical dose constraints for all tested cases.
Conclusions:
- Daily full online replanning is the most robust adaptive planning method for the Unity MR-linac.
- This method is feasible for specific tumor sites within clinically acceptable timeframes.
- Faster adaptive methods necessitate thorough dosimetric exploration for specific use cases before implementation.
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