Deep learning based super‐resolution for CBCT dose reduction in radiotherapy
This study demonstrates that deep learning can be a valuable tool for CBCT dose reduction in CBCT-guided radiotherapy by acquiring low-dose CBCTs and restoring the image quality using deep learning super-resolution. The results suggest that higher quality images can be generated when super-resolution is performed in the image domain compared to the projection domain.
Authors
- Adrian Thummerer (ORCID: https://orcid.org/0000-0002-1874-5030)
- Stefanie Corradini (ORCID: https://orcid.org/0000-0001-8709-7252)
- Lukas Schmidt (ORCID: https://orcid.org/0000-0002-3206-6659)
- Jan Hofmaier (ORCID: https://orcid.org/0000-0002-7936-9753)
- Claus Belka (ORCID: https://orcid.org/0000-0002-1287-7825)
- Christopher Kurz
- Guillaume Landry (ORCID: https://orcid.org/0000-0003-1707-4068)
Institutions
- LMU Klinikum (DE)
- Deutsches Konsortium für Translationale Krebsforschung (DE)
- Ludwig-Maximilians-Universität München (DE)
Publication Details
- Journal
- Medical Physics
- Published
- 2024-12-03
- DOI
- https://doi.org/10.1002/mp.17557
- Citations
- 7
- Primary Topic
- Advanced Image Processing Techniques
- Type
- article
- Field-Weighted Citation Impact
- 1.74
Funders
- Deutsche Krebshilfe