A PET image reconstruction algorithm combining a filtered back-projection algorithm and a neural network. The PET image reconstruction algorithm comprises the following steps: step 1, injecting a PET radioactive tracer into a biological tissue, performing scanning using a PET device, and detecting coincidence photons and counting same, so as to obtain an original sinogram matrix Y; step 2, establishing a measurement equation model according to a PET imaging principle; step 3, splitting a reconstruction problem into a reconstruction sub-problem and a de-noising sub-problem; step 4, solving the reconstruction sub-problem by using a filtered back-projection (FBP) layer, and solving the de-noising sub-problem by using an improved de-noising convolutional neural network (DnCNN); step 5, at a training stage, adjusting a parameter of an FBP-Net, and reducing an error between an output of the FBP-Net and a tag; and step 6, at an estimation stage, inputting a sinogram to be reconstructed into a trained FBP-Net, so as to directly obtain a required reconstructed image. By means of the method, the problem of it being difficult to interpret when deep learning is used for image reconstruction is solved, and a clear PET image may still be reconstructed where the count rate is low.
PET IMAGE RECONSTRUCTION ALGORITHM COMBINING FILTERED BACK-PROJECTION ALGORITHM AND NEURAL NETWORK