Abstract #3519

Undersampling artifact reduction for free-breathing 3D stack-of-radial MRI based on a deep adversarial learning network

Chang Gao^1,2, Vahid Ghodrati^1,2, Shu-Fu Shih^1,3, Holden H. Wu^1,2,3, Yongkai Liu^1,2, Marcel Dominik Nickel⁴, Thomas Vahle⁴, Brian Dale⁵, Victor Sai¹, Ely Felker¹, Qi Miao¹, Xiaodong Zhong⁶, and Peng Hu^1,2

¹Department of Radiological Sciences, University of California Los Angeles, Los Angeles, CA, United States, ²Department of Physics and Biology in Medicine, University of California Los Angeles, Los Angeles, CA, United States, ³Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, United States, ⁴MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany, ⁵MR R&D Collaborations, Siemens Medical Solutions USA, Inc., Cary, NC, United States, ⁶MR R&D Collaborations, Siemens Medical Solutions USA, Inc., Los Angeles, CA, United States

Synopsis

Undersampling is desired to reduce scan time but can cause streaking artifacts in stack-of-radial imaging. State-of-the-art deep neural networks such as the U-Net can be trained in a supervised manner to remove streaking artifacts but produce blurred images and loss of image details. Therefore, we developed and trained a 3D generative adversarial network to preserve perceptual image sharpness while removing streaking artifacts. The network used a combination of adversarial loss, L2 loss and structural similarity index loss. We demonstrated the feasibility of the proposed network for removing streaking artifacts and preserving perceptual image sharpness.

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