Although the luminescence images of water during proton-beam irradiation using a cooled charge-coupled device camera showed almost the same ranges of proton beams as those measured by an ionization chamber, the depth profiles showed lower Bragg peak intensities than those measured by an ionization chamber. In addition, a broad optical baseline signal was observed in depths that exceed the depth of the Bragg peak. We hypothesize that this broad baseline signal originates from the interaction of proton-induced prompt gamma photons with water. These prompt gamma photons interact with water to form high-energy Compton electrons, which may cause luminescence or Cherenkov emission from depths exceeding the location of the Bragg peak. To clarify this idea, we measured the luminescence images of water during the irradiations of protons in water with minimized parallax errors, and also simulated the produced light by the interactions of prompt gamma photons with water. We corrected the measured depth profiles of the luminescence images by subtracting the simulated distributions of the produced light by the interactions of prompt gamma photons in water. Corrections were also conducted using the estimated depth profiles of the light of the prompt gamma photons, as obtained from the off-beam areas of the luminescence images of water. With these corrections, we successfully obtained depth profiles that have almost identical distributions as the simulated dose distributions for protons. The percentage relative height of the Bragg peak with corrections to that of the simulation data increased to 94% from 80% without correction. Also, the percentage relative offset heights of the deeper part of the Bragg peak with corrections decreased to 0.2%-0.4% from 4% without correction. These results indicate that the luminescence imaging of water has potential for the dose distribution measurements for proton therapy dosimetry.

Although luminescence of water lower in energy than the Cerenkov-light threshold during proton and carbon-ion irradiation has been found, the phenomenon has not yet been implemented for Monte Carlo simulations. The results provided by the simulations lead to misunderstandings of the physical phenomenon in optical imaging of water during proton and carbon-ion irradiation. To solve the problems, as well as to clarify the light production of the luminescence of water, we modified a Monte Carlo simulation code to include the light production from the luminescence of water and compared them with the experimental results of luminescence imaging of water. We used GEANT4 for the simulation of emitted light from water during proton and carbon-ion irradiation. We used the light production from the luminescence of water using the scintillation process in GEANT4 while those of Cerenkov light from the secondary electrons and prompt gamma photons in water were also included in the simulation. The modified simulation results showed similar depth profiles to those of the measured data for both proton and carbon-ion. When the light production of 0.1 photons/MeV was used for the luminescence of water in the simulation, the simulated depth profiles showed the best match to those of the measured results for both the proton and carbon-ion compared with those used for smaller and larger numbers of photons/MeV. We could successively obtain the simulated depth profiles that were basically the same as the experimental data by using GEANT4 when we assumed the light production by the luminescence of water. Our results confirmed that the inclusion of the luminescence of water in Monte Carlo simulation is indispensable to calculate the precise light distribution in water during irradiation of proton and carbon-ion.

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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.

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