Modular GAN: positron emission tomography image reconstruction using two generative adversarial networks.

ABSTRACT:

Introduction

The reconstruction of PET images involves converting sinograms, which represent the measured counts of radioactive emissions using detector rings encircling the patient, into meaningful images. However, the quality of PET data acquisition is impacted by physical factors, photon count statistics and detector characteristics, which affect the signal-to-noise ratio, resolution and quantitative accuracy of the resulting images. To address these influences, correction methods have been developed to mitigate each of these issues separately. Recently, generative adversarial networks (GANs) based on machine learning have shown promise in learning the complex mapping between acquired PET data and reconstructed tomographic images. This study aims to investigate the properties of

SUBMITTER: Vashistha R 

PROVIDER: S-EPMC11425657 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature