Paper Info
Title
3D Transformer-GAN for High-Quality PET Reconstruction
Abstract
To obtain high-quality positron emission tomography (PET) image at low dose, this study proposes an end-to-end 3D generative adversarial network embedded with transformer, namely Transformer-GAN, to reconstruct the standard-dose PET (SPET) image from the corresponding low-dose PET (LPET) image. Specifically, considering the convolutional neural network (CNN) can well describe the local spatial features, while the transformer is good at capturing the long-range semantic information due to its global information extraction ability, our generator network takes advantages of both CNN and transformer, and is designed as an architecture of EncoderCNN-Transformer-DecoderCNN. Particularly, the EncoderCNN aims to extract compact feature representations with rich spatial information by using CNN, while the Transformer targets at capturing the long-range dependencies between the features learned by the EncoderCNN. Finally, the DecoderCNN is responsible for restoring the reconstructed PET image. Moreover, to ensure the similarity of voxel-level intensities as well as the data distributions between the reconstructed image and the real image, we harness both the voxel-wise estimation error and the adversarial loss to train the generator network. Validations on the clinical PET data show that our proposed method outperforms the state-of-the-art methods in both qualitative and quantitative measures.
Year
DOI
Venue
2021
10.1007/978-3-030-87231-1_27
MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2021, PT VI
Keywords
DocType
Volume
Positron Emission Tomography (PET), Generative Adversarial Network (GAN), Transformer, Image reconstruction
Conference
12906
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
0
8
Name
Order
Citations
PageRank
Yanmei Luo100.34
Yan Wang212.07
Chen Zu3254.99
Bo Zhan414.44
Xi Wu56118.90
Jiliu Zhou645058.21
Dinggang Shen77837611.27
Luping Zhou849843.89