2020年12月
Dose distribution correction for the influence of magnetic field using a deep convolutional neural network for online MR-guided adaptive radiotherapy.
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
- 巻
- 80
- 号
- 開始ページ
- 186
- 終了ページ
- 192
- 記述言語
- 英語
- 掲載種別
- 研究論文(学術雑誌)
- DOI
- 10.1016/j.ejmp.2020.11.002
PURPOSE: This study aimed to develop a deep convolutional neural network (CNN)-based dose distribution conversion approach for the correction of the influence of a magnetic field for online MR-guided adaptive radiotherapy. METHODS: Our model is based on DenseNet and consists of two 2D input channels and one 2D output channel. These three types of data comprise dose distributions without a magnetic field (uncorrected), electron density (ED) maps, and dose distributions with a magnetic field. These data were generated as follows: both types of dose distributions were created using 15-field IMRT in the same conditions except for the presence or absence of a magnetic field with the GPU Monte Carlo dose in Monaco version 5.4; ED maps were acquired with planning CT images using a clinical CT-to-ED table at our institution. Data for 50 prostate cancer patients were used; 30 patients were allocated for training, 10 for validation, and 10 for testing using 4-fold cross-validation based on rectum gas volume. The accuracy of the model was evaluated by comparing 2D gamma-indexes against the dose distributions in each irradiation field with a magnetic field (true). RESULTS: The gamma indexes in the body for CNN-corrected uncorrected dose against the true dose were 94.95% ± 4.69% and 63.19% ± 3.63%, respectively. The gamma indexes with 2%/2-mm criteria were improved by 10% in most test cases (99.36%). CONCLUSIONS: Our results suggest that the CNN-based approach can be used to correct the dose-distribution influences with a magnetic field in prostate cancer treatment.
- リンク情報
- ID情報
-
- DOI : 10.1016/j.ejmp.2020.11.002
- PubMed ID : 33189049