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

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.