To achieve high spatial resolution of reconstructed images in positron emission tomography (PET), the size of the scintillation crystal element is set small in current PET systems, which greatly increases the inter-crystal scattering (ICS) frequency. The ICS is a type of Compton scattering of the gamma photons from one crystal element to its neighborhood element, which obscures the determination of the first interaction position. In this study, we propose a 1D U-Net convolutional neural network to predict the first interaction position, which provides a universal way to efficiently solve the ICS recovery problem. The network is trained using the dataset collected from the GATE Monte Carlo simulation. The 1D U-Net structure is applied due to its capability of synthesizing both low-level and high-level information, which shows superiority in solving the ICS recovery problem. After being well trained, the 1D U-Net can generate a prediction accuracy of 78.1%. Compared to the coincidence events only composed from two photoelectric gamma photons, the sensitivity is improved by 149%. The contrast-to-noise ratio of the reconstructed contrast phantom increases from 6.973 to 10.795 for the 16 mm hot sphere. Compared to the take-energy-centroid method, the spatial resolution of the reconstructed resolution phantom can obtain the best improvement of 33.46%. Compared with the previous deep learning method based on the fully connected network, the proposed 1D U-Net can work more stably with considerably fewer network parameters. The 1D U-Net network model shows good universality when predicting different phantoms, and the computation speed is fast.
Keywords: PET; U-Net; deep learning; inter-crystal scatter.
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