3D Auto-Context-Based Locality Adaptive Multi-Modality GANs for PET Synthesis

Yan Wang, Luping Zhou, Biting Yu, Lei Wang, Chen Zu, David S. Lalush, Weili Lin, Xi Wu, Jiliu Zhou, Dinggang Shen

Research output: Contribution to journalArticlepeer-review

132 Citations (Scopus)


Positron emission tomography (PET) has been substantially used recently. To minimize the potential health risk caused by the tracer radiation inherent to PET scans, it is of great interest to synthesize the high-quality PET image from the low-dose one to reduce the radiation exposure. In this paper, we propose a 3D auto-context-based locality adaptive multi-modality generative adversarial networks model (LA-GANs) to synthesize the high-quality FDG PET image from the low-dose one with the accompanying MRI images that provide anatomical information. Our work has four contributions. First, different from the traditional methods that treat each image modality as an input channel and apply the same kernel to convolve the whole image, we argue that the contributions of different modalities could vary at different image locations, and therefore a unified kernel for a whole image is not optimal. To address this issue, we propose a locality adaptive strategy for multi-modality fusion. Second, we utilize 1 × 1 × 1 kernel to learn this locality adaptive fusion so that the number of additional parameters incurred by our method is kept minimum. Third, the proposed locality adaptive fusion mechanism is learned jointly with the PET image synthesis in a 3D conditional GANs model, which generates high-quality PET images by employing large-sized image patches and hierarchical features. Fourth, we apply the auto-context strategy to our scheme and propose an auto-context LA-GANs model to further refine the quality of synthesized images. Experimental results show that our method outperforms the traditional multi-modality fusion methods used in deep networks, as well as the state-of-the-art PET estimation approaches.

Original languageEnglish
Article number8552676
Pages (from-to)1328-1339
Number of pages12
JournalIEEE Transactions on Medical Imaging
Issue number6
Publication statusPublished - 2019 Jun

Bibliographical note

Funding Information:
This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 61701324, in part by the Australian Research Council (ARC) under Grant DE160100241, and in part by NIH under Grant EB006733.

Publisher Copyright:
© 1982-2012 IEEE.


  • Image synthesis
  • generative adversarial networks (GANs)
  • locality adaptive fusion
  • multi-modality
  • positron emission topography (PET)

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering


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