Low-dose CT simulation with a generative adversarial network

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

doi:10.1117/12.2529698

10 September 2019 Low-dose CT simulation with a generative adversarial network

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

Proceedings Volume 11113, Developments in X-Ray Tomography XII; 111131F (2019) https://doi.org/10.1117/12.2529698
Event: SPIE Optical Engineering + Applications, 2019, San Diego, California, United States

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.

Citation Download Citation

Hongming Shan, Xun Jia, Klaus Mueller, Uwe Kruger, and Ge Wang "Low-dose CT simulation with a generative adversarial network", Proc. SPIE 11113, Developments in X-Ray Tomography XII, 111131F (10 September 2019); https://doi.org/10.1117/12.2529698

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available