PET image reconstruction with parallax correction based on a distance-driven deep neural network

Positron emission tomography (PET) is a widely used molecular imaging technology. However, the inability of conventional PET systems without depth of interaction (DOI) information to precisely locate gamma rays leads to parallax error, which further results in the non-uniform resolution in reconstructed images. The existing methods enable PET systems to acquire DOI information by adding more hardwares, which are generally at the cost of higher prices and degradation of other performances. To overcome these shortcomings, we proposed a novel distance-driven cascade framework containing a bi-directional long short-term memory (Bi-LSTM) module and an encoder-decoder module. Especially, the distance-driven preprocessing was realized by splitting the sinogram into one-dimensional vectors according to radial distance and inputting them sequentially. In this approach, the bins in the same sinogram row had related features,thus were processed at the same time. Furthermore, sinogram rows used the mutual implicit information extracted by Bi-LSTM to achieve better transformation before processed by the encoder-decoder module. To test the proposed method, we conducted the network training and testing on a dataset simulated using the open-source Geant4 toolkit GATE. Compared to the DeepPET, which is a typical PET reconstruction method based on deep learning, our method acquired an obvious promotion in structural similarity (SSIM) and peak signal-to-noise ratio (PSNR) on the test dataset. It proves that our method is superior in perceptual performance and efficiency.