Deep learning-based in vivo dose verification from proton-induced secondary-electron-bremsstrahlung images with various count level. (July 2022)

• Record Type: Journal Article
• Authors: Yabe, Takuya; Yamaguchi, Mitsutaka; Liu, Chih-Chieh; Toshito, Toshiyuki; Kawachi, Naoki; Yamamoto, Seiichi
• Is Part Of: Physica medica. Volume 99(2022)
• Page Start: 130
• Page End: 139
• Publication Date: 2022-07
• Subjects: secondary-electron-bremsstrahlung -- deep learning -- dose prediction -- proton therapy; 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.2022.05.013
• Languages: English
• ISSNs: 1120-1797

Abstract

Highlights: Bremsstrahlung imaging is a promising method for range verification of proton beam. A deep learning model was applied to overcome problems for bremsstrahlung imaging. U-Net model was trained to predict proton dose images from bremsstrahlung images. The trained model can predict dose images within 2.1 mm range/width error. Abstract: Purpose: Proton-induced secondary-electron-bremsstrahlung (SEB) imaging is a promising method for estimating the ranges of particle beam. However, SEB images do not directly represent dose distributions of particle beams. In addition, the ranges estimated from measured images were deviated because of limited spatial resolutions of the developed x-ray camera as well as statistical noise in the images. To solve these problems, we proposed a method for predicting high-resolution dose images from SEB images with various count level using a deep learning (DL) approach for range and width verification. Methods: In this study, we adopted the double U-Net model, which is a previously proposed deep convolutional network model. The first U-Net model in the double U-Net model was used to denoise the SEB images with various count level. The first U-Net model for denoising was trained on 8000 pairs of SEB images with various count level and noise-free images which were created by a sophisticated in-house developed model function. The second U-Net model for dose prediction was trained using 8000 pairs of denoised SEB images from the first U-Net model and high-resolution dose images generated by Monte Carlo simulation. Results: For both simulation and measurement data, the trained DL model could successfully predict high-resolution dose images which showed a clear Bragg peak and no statistical noise. The difference of the range and width was less than 2.1 mm, even from the SEB images measured with a decrease in the number of irradiated protons to less than 11% of 3.2 × 10 11 protons. Conclusions: High-resolution dose images from measured and simulated SEB images were successfully predicted by using the trained DL model for protons. Our proposed DL model was feasible to predict dose images accurately even with smaller number of irradiated protons.