Deep-Learning-Based PET Parallax Error Correction: A 2-D Simulation and Phantom Study

The parallax error (PE) significantly deteriorates the spatial resolution and imaging quality of positron emission tomography (PET) scanners. Existing PE correction methods either rely on depth decoding detectors in hardware which increases development costs, or optimize the system response matrix (SRM) in software providing limited compensation for PE. This work proposed a novel PE correction method in projection space based on deep learning (DL), consisting of two steps. First, the sinogram affected by PE was processed by a neural network (PEC-Net). The corrected sinogram output from the PEC-Net was then reconstructed to an improved image. To generate ideal PE-corrected labels, we synthesized training data using Monte Carlo (MC) simulation-based SRMs as forward projectors. The proposed method was validated using simulation data and real data. Experimental results show that the proposed method effectively eliminated artifacts caused by PE, and the reconstructed images of simulation data outperformed those obtained at 4 mm depth of interaction (DOI) resolution in terms of structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR). The PEC-Net may provide a low-cost, high-performance, software-based PE correction method for PET scanners without DOI measurement.