FBP‐CNN: A Direct PET Image Reconstruction Network for Flow Visualization

Abstract

Dynamic positron emission tomography (PET) imaging has the potential to address technical challenges that persist in the visualization of optically inaccessible flow fields in integrated systems. However, traditional reconstruction algorithms are unable to reconstruct high-quality images from dynamic scan data. In this paper, a neural network structure that can reconstruct high-quality images directly using sinograms as input by combining the filtered back-projection (FBP) algorithm and denoising convolutional neural network (CNN) is proposed, which is named FBP-CNN. Computational fluid dynamics (CFD) software and the Monte Carlo simulation platform are used jointly to generate a dataset for the flow around the bluff body problem. The dataset is then used to train, validate, and test the FBP-CNN network, and the network after completing training is used to reconstruct the real projection data. The results show that FBP-CNN can reconstruct high-quality images from both simulated datasets and real projection data.