A PET image reconstruction method and apparatus, and a computing device, relating to the field of image processing, the method comprising: comparing a desired PET image and a standard PET image (S101); if the comparison reaches a comparison termination condition, then outputting the desired PET image as a target PET image (S102); and, if the comparison does not reach the comparison termination condition, then processing the desired PET image using a mapping network to obtain a new desired PET image (S103). In the present solution, as the mapping network is a deep convolutional neural network based on dictionary learning and obtained after training with magnetic resonance images and PET images as training samples, when processing desired PET images via such a mapping network to obtain new desired PET images, the difference with the standard PET image gets smaller and smaller after fewer iterative calculations, enabling the algorithm to rapidly converge, thereby increasing the PET image reconstruction speed and reducing the time required for reconstruction.
PET IMAGE RECONSTRUCTION METHOD AND APPARATUS, AND COMPUTING DEVICE