Prior information-guided reconstruction network for positron emission tomography images.

ABSTRACT:

Background

Deep learning has recently shown great potential in medical image reconstruction tasks. For positron emission tomography (PET) images, the direct reconstruction from raw data to radioactivity images using deep learning without any constraint may lead to the production of nonexistent structures. The aim of this study was to specifically develop and test a flexibly deep learning-based reconstruction network guided by any form of prior knowledge to achieve high quality and high reliability reconstruction.

Methods

We developed a novel prior information-guided reconstruction network (PIGRN) with a dual-channel generator and a 2-scale discriminator based on a conditional generative adversarial network (cGAN). Besides the raw data channel, an additional channel is provid

SUBMITTER: Liu Z 

PROVIDER: S-EPMC10722030 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature