Denoising PET images for proton therapy using a residual U-net

The use of proton therapy has the advantage of high dose concentration as it is possible to concentrate the dose on the tumor while suppressing damage to the surrounding normal organs. However, the range uncertainty significantly affects the actual dose distribution in the vicinity of the proton range, limiting the benefit of proton therapy for reducing the dose to normal organs. By measuring the annihilation gamma rays from the produced positron emitters, it is possible to obtain a proton induced positron emission tomography (pPET) image according to the irradiation region of the proton beam. Smoothing with a Gaussian filter is generally used to denoise PET images; however, this approach lowers the spatial resolution. Furthermore, other conventional smoothing processing methods may deteriorate the steep region of the pPET images. In this study, we proposed a denoising method based on a Residual U-Net for pPET images. We conducted the Monte Carlo simulation and irradiation experiment on a human phantom to obtain pPET data. The accuracy of the range estimation and the image similarity were evaluated for pPET images using the Residual U-Net, a Gaussian filter, a median filter, the block-matching and 3D-filtering (BM3D), and a total variation (TV) filter. Usage of the Residual U-Net yielded effective results corresponding to the range estimation; however, the results of peak-signal-to-noise ratio were identical to those for the Gaussian filter, median filter, BM3D, and TV filter. The proposed method can contribute to improving the accuracy of treatment verification and shortening the PET measurement time.