- •The Generative Adversarial Network to reduce motion artifacts on liver MRI.
- •Training with the perceptual loss to preserve anatomical structure details.
- •Multiple types and degrees of motion artifacts simulation in image and k-space.
- •Good generalization of the model evaluated on liver MRI images with real motion.
Abbreviations:DCE-MRI (dynamic contrast enhanced magnetic resonance imaging), DRN-DCMB (deep residual network with densely connected multi-resolution block), CS (compressed sensing), CNNs (convolutional neural networks), DL (deep learning), 3D (three-dimensional), FFT (Fast Fourier Transform), BN (batch normalization), LeakyReLU (Leaky rectified linear unit), MSE (mean square error), SSIM (structural similarity index), TR (repetition time), TE (echo time), FA (flip angle), MARC (motion artifact reduction with convolution), GAN (generative adversarial network)
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