Method for mixed tracers dynamic PET concentration image reconstruction based on stacked autoencoder

A method for reconstructing mixed tracers dynamic position emission tomography (PET) concentration distribution images based on stacked autoencoder comprising the following steps:

• (1) injecting a first tracer and a second tracer into a biological tissue, detecting the biological tissue by using a pair of detectors, and collecting coincidence counting vectors corresponding to different collection time to construct a coincidence counting matrix Y for the first tracer and the second tracer, a coincidence counting matrix Y1 for the first tracer and a coincidence counting matrix Y2 for the second tracer;
• (2) according to the coincidence counting matrices Y, Y1 and Y2, obtaining dynamic PET concentration distribution images X for the first tracer and the second tracer, dynamic PET concentration distribution images X1 for the first tracer and dynamic PET concentration distribution images X2 for the second tracer by maximum likelihood-expectation maximization (ML-EM) algorithm using a principle of PET imaging;
• then arranging the PET concentration distribution images of different frames in pixel order to obtain a dynamic concentration truth vector set x for the first tracer and the second tracer PET images X, and a dynamic concentration truth vector set o for PET images X1 and X2 after the first tracer and the second tracer are combined, x and o being taken as a training set;
• wherein each vector in x is used as an input of a neural network and each vector in o is used as a truth label;
• (3) training multiple autoencoders with the training set, cascading trained autoencoders to the stacked autoencoder, and then fine-tuning the stacked autoencoder with the training set to obtain PET concentration distribution image reconstruction model, wherein the stacked autoencoder consists of an input layer, a hidden layer and an output layer, the input layer to the hidden layer is called an encoder, and the hidden layer to the output layer is called a decoder, the hidden layer of a previous autoencoder is the input layer of a latter autoencoder;
• for any self-encoder, the number of neurons in the hidden layer is less than the number of neurons in the input layer;
• (4) repeating the method of the step (1) and using new mixed tracers to acquire a new coincidence counting matrix, then obtaining the dynamic mixed PET concentration distribution images of the new mixed tracers according to the step (2) and arranging the obtained PET concentration distribution images in different frames in pixel order as a test set, inputting the test set into the PET concentration distribution image reconstruction model to reconstruct the dynamic PET concentration distribution images of the first tracer and the second tracer, respectively.