Mitsutaka Yamaguchi1, Chih-Chieh Liu2, Hsuan-Ming Huang3, Takuya Yabe4, Takashi Akagi5, Naoki Kawachi1, Seiichi Yamamoto4. 1. Takasaki Advanced Radiation Research Institute, Quantum Beam Science Research Directorate, National Institutes for Quantum and Radiological Science (QST), Takasaki, Japan. 2. Department of Biomedical Engineering, University of California, Davis, CA, USA. 3. Institute of Medical Device and Imaging, College of Medicine, National Taiwan University, Taipei City, Taiwan. 4. Radiological and Medical Laboratory Sciences, Nagoya University Graduate School of Medicine, Nagoya, Japan. 5. Hyogo Ion Beam Medical Center, Tatsuno, Japan.
Abstract
PURPOSE: Imaging of the secondary electron bremsstrahlung (SEB) x rays emitted during particle-ion irradiation is a promising method for beam range estimation. However, the SEB x-ray images are not directly correlated to the dose images. In addition, limited spatial resolution of the x-ray camera and low-count situation may impede correctly estimating the beam range and width in SEB x-ray images. To overcome these limitations of the SEB x-ray images measured by the x-ray camera, a deep learning (DL) approach was proposed in this work to predict the dose images for estimating the range and width of the carbon ion beam on the measured SEB x-ray images. METHODS: To prepare enough data for the DL training efficiently, 10,000 simulated SEB x-ray and dose image pairs were generated by our in-house developed model function for different carbon ion beam energies and doses. The proposed DL neural network consists of two U-nets for SEB x ray to dose image conversion and super resolution. After the network being trained with these simulated x-ray and dose image pairs, the dose images were predicted from simulated and measured SEB x-ray testing images for performance evaluation. RESULTS: For the 500 simulated testing images, the average mean squared error (MSE) was 2.5 × 10-5 and average structural similarity index (SSIM) was 0.997 while the error of both beam range and width was within 1 mm FWHM. For the three measured SEB x-ray images, the MSE was no worse than 5.5 × 10-3 and SSIM was no worse than 0.980 while the error of the beam range and width was 2 mm and 5 mm FWHM, respectively. CONCLUSIONS: We have demonstrated the advantages of predicting dose images from not only simulated data but also measured data using our deep learning approach.
PURPOSE: Imaging of the secondary electron bremsstrahlung (SEB) x rays emitted during particle-ion irradiation is a promising method for beam range estimation. However, the SEB x-ray images are not directly correlated to the dose images. In addition, limited spatial resolution of the x-ray camera and low-count situation may impede correctly estimating the beam range and width in SEB x-ray images. To overcome these limitations of the SEB x-ray images measured by the x-ray camera, a deep learning (DL) approach was proposed in this work to predict the dose images for estimating the range and width of the carbon ion beam on the measured SEB x-ray images. METHODS: To prepare enough data for the DL training efficiently, 10,000 simulated SEB x-ray and dose image pairs were generated by our in-house developed model function for different carbon ion beam energies and doses. The proposed DL neural network consists of two U-nets for SEB x ray to dose image conversion and super resolution. After the network being trained with these simulated x-ray and dose image pairs, the dose images were predicted from simulated and measured SEB x-ray testing images for performance evaluation. RESULTS: For the 500 simulated testing images, the average mean squared error (MSE) was 2.5 × 10-5 and average structural similarity index (SSIM) was 0.997 while the error of both beam range and width was within 1 mm FWHM. For the three measured SEB x-ray images, the MSE was no worse than 5.5 × 10-3 and SSIM was no worse than 0.980 while the error of the beam range and width was 2 mm and 5 mm FWHM, respectively. CONCLUSIONS: We have demonstrated the advantages of predicting dose images from not only simulated data but also measured data using our deep learning approach.