High-quality PET image synthesis from ultra-low-dose PET/MRI using bi-task deep learning.

ABSTRACT:

Background

Lowering the dose for positron emission tomography (PET) imaging reduces patients' radiation burden but decreases the image quality by increasing noise and reducing imaging detail and quantifications. This paper introduces a method for acquiring high-quality PET images from an ultra-low-dose state to achieve both high-quality images and a low radiation burden.

Methods

We developed a two-task-based end-to-end generative adversarial network, named bi-c-GAN, that incorporated the advantages of PET and magnetic resonance imaging (MRI) modalities to synthesize high-quality PET images from an ultra-low-dose input. Moreover, a combined loss, including the mean absolute error, structural loss, and bias loss, was created to improve the trained model's performance. Real int

SUBMITTER: Sun H 

PROVIDER: S-EPMC9703111 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature