Iterative PET image reconstruction using cascaded data consistency generative adversarial network

Abstract

This study proposed a GAN‐based reconstruction method‐cascaded data consistency generative adversarial network (CDCGAN) to recover high‐quality PET images from filtered back projection PET images with streaking artifacts and high noise. First, the authors embed defined data consistency layer (DC layer) in their generator network to constrain the reconstruction process and adjust accurately generated faked PET images. Second, to improve the accuracy of reconstruction on average, their generator network was built iteratively to achieve better performance with simple structures. They observed that the proposed CDCGAN allows the preservation of fine anomalous features while eliminating the streaking artifacts and noise. Experimental results show that the reconstructed PET images by their methods perform well comparably to other state‐of‐the‐art methods but at a faster speed. A clinical experiment was also performed to show the validity of the CDCGAN for artifacts reduction.

Keywords

• iterative PET image reconstruction
• GAN‐based reconstruction method‐cascaded data consistency generative adversarial network
• high‐quality PET images
• filtered back projection PET images
• streaking artifacts
• generator network