Abstract #3523

Streaking artifact reduction of free-breathing undersampled stack-of-radial MRI using a 3D generative adversarial network

Chang Gao^1,2, Vahid Ghodrati^1,2, Dylan Nguyen³, Marcel Dominik Nickel⁴, Thomas Vahle⁴, Brian Dale⁵, 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

Undersampling in free-breathing stack-of-radial MRI is desirable to shorten the scan time but introduces streaking artifacts. Deep learning has shown an excellent performance in removing image artifacts. We developed a 3D residual generative adversarial network (3D-GAN) to remove streaking artifacts caused by radial undersampling. We trained and tested the network using paired images that were undersampled with acceleration factors of 3.1x to 6.3x and fully-sampled from single echo and multi-echo acquisitions. We demonstrate the feasibility of the network with 3.1x to 6.3x acceleration factors and 6 different echo times.

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