BeamGAN: a physics-informed conditional generative adversarial network for Monte-Carlo-free portal dose image prediction from incident fluence maps
- DOI
- 10.1088/1361-6560/aebd61
- Published
- 2026-11-03
- Container
- Physics in Medicine & Biology
- Publisher
- IOP Publishing
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1088/1361-6560/aebd61,
title = {BeamGAN: a physics-informed conditional generative adversarial network for Monte-Carlo-free portal dose image prediction from incident fluence maps},
author = {Tianlong Ji and Wenjun Tan and Dazhe Zhao},
year = {2026},
journal = {Physics in Medicine \& Biology},
doi = {10.1088/1361-6560/aebd61},
url = {https://doi.org/10.1088/1361-6560/aebd61}
}RIS
TY - JOUR TI - BeamGAN: a physics-informed conditional generative adversarial network for Monte-Carlo-free portal dose image prediction from incident fluence maps AU - Tianlong Ji AU - Wenjun Tan AU - Dazhe Zhao PY - 2026 JO - Physics in Medicine & Biology DO - 10.1088/1361-6560/aebd61 UR - https://doi.org/10.1088/1361-6560/aebd61 ER -
APA
Ji, T., Tan, W., & Zhao, D. (2026). BeamGAN: a physics-informed conditional generative adversarial network for Monte-Carlo-free portal dose image prediction from incident fluence maps. Physics in Medicine & Biology. https://doi.org/10.1088/1361-6560/aebd61
Source records
- crossref · retrieved 2026-11-06T03:24:12.301Z