Predicting anatomical variations in radiotherapy with a vector quantized variational autoencoder generative model
The VQ-VAE model effectively predicts anatomical changes during radiotherapy based on planning CT, demonstrating its potential to inform adaptive decision-making in radiotherapy.
Authors
- Ming Zhang (ORCID: https://orcid.org/0009-0007-5561-9109)
- Long Yang
- Jiazhou Wang (ORCID: https://orcid.org/0000-0002-9012-6605)
- Weigang Hu (ORCID: https://orcid.org/0000-0002-4409-6804)
- Ziwei Li (ORCID: https://orcid.org/0000-0003-4893-0971)
- Zhenhao Li (ORCID: https://orcid.org/0000-0001-7181-6689)
- Yue Zou (ORCID: https://orcid.org/0000-0002-2568-1442)
- Xiaojie Yin
Institutions
- Shanghai Medical College of Fudan University (CN)
- Fudan University Shanghai Cancer Center (CN)
- Shanghai Institute of Hematology (CN)
Publication Details
- Journal
- Medical Physics
- Published
- 2025-09-01
- DOI
- https://doi.org/10.1002/mp.18120
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- National Key Research and Development Program of China