Advanced prediction of multi‐leaf collimator leaf position using artificial neural network
Three models were evaluated for predicting the delivered MLC positions using data from an Elekta accelerator. The PSOBPNN model exhibited superior performance by achieving markedly lower MAE and MSE values while also demonstrating robust generalizability in predicting positions across various leaf indices, outperforming the conventional BPNN and RBFNN models.
Authors
- Zhiqiang Zhu (ORCID: https://orcid.org/0000-0002-1277-5917)
- Zetian Shen
- Liuli Chen
- Jun Lv (ORCID: https://orcid.org/0009-0000-6083-1314)
- Pengcheng Long (ORCID: https://orcid.org/0009-0004-7092-2438)
- Han Zhou (ORCID: https://orcid.org/0000-0002-8067-4878)
- Liqin Hu (ORCID: https://orcid.org/0000-0002-9115-9978)
Institutions
- University of Science and Technology of China (CN)
- Chinese Academy of Sciences (CN)
- Hefei Institutes of Physical Science (CN)
- Second Affiliated Hospital of Nanjing Medical University (CN)
Publication Details
- Journal
- Medical Physics
- Published
- 2025-02-18
- DOI
- https://doi.org/10.1002/mp.17690
- Citations
- 1
- Primary Topic
- Advanced Radiotherapy Techniques
- Type
- article
- Field-Weighted Citation Impact
- 1.88