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Low-dose CT simulation with a generative adversarial network
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Paper 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.

Paper Details

Date Published: 10 September 2019
PDF: 10 pages
Proc. SPIE 11113, Developments in X-Ray Tomography XII, 111131F (10 September 2019); doi: 10.1117/12.2529698
Show Author Affiliations
Hongming Shan, Rensselaer Polytechnic Institute (United States)
Xun Jia, The Univ. of Texas Southwestern Medical Ctr. at Dallas (United States)
Klaus Mueller, Stony Brook Univ. (United States)
Uwe Kruger, Rensselaer Polytechnic Institute (United States)
Ge Wang, Rensselaer Polytechnic Institute (United States)


Published in SPIE Proceedings Vol. 11113:
Developments in X-Ray Tomography XII
Bert Müller; Ge Wang, Editor(s)

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