Open AccessTCGAN: a transformer-enhanced GAN for PET synthetic CT
Author(s) -
jitao li,
Yang Yue,
Fuchun Zhang,
Meng Li,
Zongjin Qu,
Shunbo Hu
Publication year - 2022
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.467683
Subject(s) - computer science , image synthesis , convolutional neural network , generative adversarial network , transformer , artificial intelligence , medical imaging , positron emission tomography , deep learning , pattern recognition (psychology) , image (mathematics) , nuclear medicine , medicine , physics , quantum mechanics , voltage
Multimodal medical images can be used in a multifaceted approach to resolve a wide range of medical diagnostic problems. However, these images are generally difficult to obtain due to various limitations, such as cost of capture and patient safety. Medical image synthesis is used in various tasks to obtain better results. Recently, various studies have attempted to use generative adversarial networks for missing modality image synthesis, making good progress. In this study, we propose a generator based on a combination of transformer network and a convolutional neural network (CNN). The proposed method can combine the advantages of transformers and CNNs to promote a better detail effect. The network is designed for positron emission tomography (PET) to computer tomography synthesis, which can be used for PET attenuation correction. We also experimented on two datasets for magnetic resonance T1- to T2-weighted image synthesis. Based on qualitative and quantitative analyses, our proposed method outperforms the existing methods.