Deep learning based three-dimensional reconstruction method for low-dose PET imaging

A three-dimensional low-dose PET reconstruction method based on deep learning, comprising the following steps:

• (1) performing a back projection method for low-dose PET raw data, which specifically comprises the following sub-steps:
• (1.1) performing attenuation correction processing on the low-dose PET raw data, and obtaining lpp_ac, a back projection of the low-dose PET data after attenuation correction through being subjected to the transposed system matrix of a PET scanner;
• (1.2) obtaining lrs, a back projection of random and scatter data by subjecting the random and scatter data of the low-dose PET raw data to a transposed system matrix of the PET scanner;
• (1.3) simulating an all-1 PET image, subjecting the all-1 PET image to a system matrix of the PET scanner to obtain a three-dimensional projection, and then back projecting a result of the three-dimensional projection to the image domain to obtain l1, a back projection of the all-1 PET image;
• (1.4) subtracting lrs_ac, the back projection of the random and scatter data obtained in step (1.2) from lpp_wc, the back projection of the low-dose PET data after attenuation correction obtained in step (1.1) to implement random and scatter correction, and then dividing by l1, the back projection of the all-1 PET image to obtain a corrected and regularized three-dimensional low-dose PET back projection lbp:
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• (2) taking lbp, the corrected and regularized three-dimensional low-dose PET back projection obtained in step (1.4), as an input of a deep neural network, taking a reconstructed standard-dose PET image as a label of the network, updating parameters of the deep neural network through Adam optimization algorithm, minimizing a target loss function, and completing training of the deep neural network, wherein the target loss function of training the deep neural network is as follows:
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• where Ny, Ny, and Nz represent total numbers of pixels of the low-dose PET back projection or the standard-dose PET image in horizontal, vertical and axial directions, respectively, C(·) represents mapping from the low-dose PET back projection lbp to the standard-dose PET reconstructed image ffull fitted by a three-dimensional deep neural network, and (i,j,k) represents pixels in the image;
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• (3) performing the back projection method in step 1 on newly acquired low-dose PET raw data, and inputting the data into the deep neural network trained in step 2 to obtain a corresponding low-dose PET reconstructed image.