Dose distribution correction for the influence of magnetic field using a deep convolutional neural network for online MR-guided adaptive radiotherapy. (December 2020)

• Record Type: Journal Article
• Authors: Kajikawa, Tomohiro; Kadoya, Noriyuki; Tanaka, Shohei; Nemoto, Hikaru; Takahashi, Noriyoshi; Chiba, Takahito; Ito, Kengo; Katsuta, Yoshiyuki; Dobashi, Suguru; Takeda, Ken; Yamada, Kei; Jingu, Keiichi
• Is Part Of: Physica medica. Volume 80(2021)
• Page Start: 186
• Page End: 192
• Publication Date: 2020-12
• Subjects: Radiotherapy -- Dose correction -- Convolutional neural network -- Magnetic field -- Deep learning; Medical physics -- Periodicals; Biophysics -- Periodicals; Biophysics -- Periodicals; Imagerie médicale -- Périodiques; Radiothérapie -- Périodiques; Rayons X -- Sécurité -- Mesures -- Périodiques; Physique -- Périodiques; Médecine -- Périodiques; 610.153
• DOI: 10.1016/j.ejmp.2020.11.002
• Languages: English
• ISSNs: 1120-1797

Abstract

Highlights: A CNN model to correct the influence of a magnetic field for doses was proposed. CNN-corrected doses had relatively good agreement with doses in a magnetic field. The gamma indexes improved in most cases compared with uncorrected doses. Abstract: 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.