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Abstract #3611

Parallel Imaging with a Combination of SENSE and Generative Adversarial Networks (GAN)

Jun Lyu1, Peng Wang1, and Chengyan Wang2
1Yantai University, Yantai, China, 2Fudan University, Shanghai, China

This study aims to use GAN architecture to remove the g-factor artifacts in SENSE reconstruction. The proposed method outperforms SENSE and ZF+GAN in terms of the measured quality metrics (decreases of NMSE and increases of PSNR and SSIM). Besides, our method performs well in preserving images details with under-sampling factor of up to 6-fold, which is promising to be applied in clinical applications.

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