Low-dose CT simulation with a generative adversarial network

Hongming Shan, Xun Jia, Klaus Mueller, Uwe Kruger, Ge Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

This paper introduces a generative adversarial network (GAN) for low-dose CT (LDCT) simulation, which is an inverse process for network-based low-dose CT denoising. Within our GAN framework, the generator is an encoder-decoder network with a shortcut connection to produce realistic noisy LDCT images. To ensure satisfactory results, a conditional batch normalization layer is incorporated into the bottleneck between the encoder and the decoder. After the model is trained, a Gaussian noise generator serves as the latent variable controlling the noise in generated CT images. With the Mayo Low-dose CT Challenge dataset, the proposed network was trained on image patches, and then produced full-size low-dose CT images of different noise distributions at various noise levels. The network-generated low-dose CT images can be used to test the robustness of the current low-dose CT denoising models and also help perform other imaging tasks such as optimization of radiation dose to patients and evaluation of model observers.

Original languageEnglish (US)
Title of host publicationDevelopments in X-Ray Tomography XII
EditorsBert Muller, Ge Wang
PublisherSPIE
ISBN (Electronic)9781510629196
DOIs
StatePublished - 2019
Event12th SPIE Conference on Developments in X-Ray Tomography 2019 - San Diego, United States
Duration: Aug 13 2019Aug 15 2019

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11113
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Conference12th SPIE Conference on Developments in X-Ray Tomography 2019
CountryUnited States
CitySan Diego
Period8/13/198/15/19

Keywords

  • Batch normalization
  • Generative adversarial network
  • Image simulation
  • Low-dose CT (LDCT)

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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  • Cite this

    Shan, H., Jia, X., Mueller, K., Kruger, U., & Wang, G. (2019). Low-dose CT simulation with a generative adversarial network. In B. Muller, & G. Wang (Eds.), Developments in X-Ray Tomography XII [111131F] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 11113). SPIE. https://doi.org/10.1117/12.2529698