Attention 3D UNET for dose distribution prediction of high‐dose‐rate brachytherapy of cervical cancer: Intracavitary applicators
Attention-gated 3D UNET revealed a promising capability in predicting voxel-wise dose distributions compared to 3D UNET. This model could be deployed for clinical use to predict 3D dose distributions for near real-time decision-making before planning, quality assurance, and guiding future automated planning, making the current workflow more efficient.
Authors
- Binod Manandhar (ORCID: https://orcid.org/0000-0003-1522-0885)
- Sharmin Alam (ORCID: https://orcid.org/0000-0003-4521-8209)
- William Y. Song (ORCID: https://orcid.org/0009-0003-3196-2368)
- Alexander F. I. Osman (ORCID: https://orcid.org/0000-0002-1286-475X)
- Dylan Richeson
- Somayeh Gholami (ORCID: https://orcid.org/0000-0002-3014-1934)
- Shiva Gautam (ORCID: https://orcid.org/0009-0000-7160-7107)
Institutions
- Virginia Commonwealth University (US)
- University of Utah (US)
- Virginia Cancer Institute (US)
- Baylor Scott & White Medical Center - Temple (US)
- Baylor Scott & White Health (US)
Publication Details
- Journal
- Journal of Applied Clinical Medical Physics
- Published
- 2024-11-15
- DOI
- https://doi.org/10.1002/acm2.14568
- Citations
- 2
- Primary Topic
- Advanced Radiotherapy Techniques
- Type
- article
- Field-Weighted Citation Impact
- 1.22