Abstract #4312

Accelerated VCC-Wave MR Imaging Using Deep Generative Models

Congcong Liu^1,2, Zhuoxu Cui¹, Zhilang Qiu¹, Haoxiang Li^1,2, Yifan Guo¹, Chentao Cao^1,2, Xin Liu^1,2, Hairong Zheng^1,2, Dong Liang^1,2,3, and Haifeng Wang^1,2

¹Shenzhen Institutes of Advanced Technology,Chinese Academy of Sciences, Shenzhen, China, ²Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China, ³Research Centre for Medical AI, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

Synopsis

CSM or kernel needs to be estimated in conventional parallel image, which is very time-consuming and the estimation process may be inaccurate. Here, wave coding model based on virtual conjugate coil using deep generative modes (WV-DGM) is proposed for the virtual conjugate coil (VCC) extended model based on wave coding. WV-DGM combined with deep DGM without training can realize advantages of wave encoding and introduce extra phase to reduce the ill-condition of the model via VCC. The results in vivo demonstrated that WV-DGM can achieve better quality compared with conventional SENSE while 10x times used to estimate CSM is reduced.

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