Dose image prediction for range and width verifications from carbon ion-induced secondary electron bremsstrahlung x-rays using deep learning workflow

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.