Abstract #1900

Application of GAN in optimizing compressed sensed MR imaging of brachxial plexus

Yan Fu¹, Tianjing Zhang², Haixia Li³, Yichen Tong³, Xiangchuang Kong⁴, and Dingxi Liu⁴

¹EPFL, Lausanne, Switzerland, ²Philips Healthcare, Guangzhou, China, ³Sun Yat-Sen University, Guangzhou, China, ⁴Radiology, Union Hospital,Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

In routine MRI clinical application of compressed sensing, the image quality is often not useful for diagnosis when its CS(Compressed SENSE) acceleration factor is beyond a certain level(e.g. 8 or 10). It is desirable to further accelerate MR sequences in multiple applications such as brachial plexus nerve, coronary artery, and so on. It is possible to use generative adversarial network(GAN) models to further optimize the imaging workflow by improving the image quality of data acquired with high CS factors.

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