Proton dose calculation on cone-beam computed tomography using unsupervised 3D deep learning networks
All three evaluated networks generated synthetic CT images with dose distributions comparable to those of conventional fan-beam CT. The synthetic CT generation was fast, making all networks feasible for adaptive proton therapy.
Authors
- Kenneth Jensen (ORCID: https://orcid.org/0000-0002-2364-965X)
- L.P. Muren (ORCID: https://orcid.org/0000-0002-7418-5832)
- U.V. Elstrøm
- Ole Nørrevang (ORCID: https://orcid.org/0000-0002-2100-0208)
- Vicki Trier Taasti (ORCID: https://orcid.org/0000-0002-4588-9769)
- Casper Dueholm Vestergaard
- Jintao Ren (ORCID: https://orcid.org/0000-0002-1558-7196)
Institutions
- Maastricht University Medical Centre (NL)
- Aarhus University Hospital (DK)
- Maastro Clinic (NL)
Publication Details
- Journal
- Physics and Imaging in Radiation Oncology
- Published
- 2024-10-01
- DOI
- https://doi.org/10.1016/j.phro.2024.100658
- Citations
- 6
- Primary Topic
- Medical Imaging Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 2.43