Magnetic resonance image (MRI) reconstruction is a severely ill-posed linear inverse task demanding time and resource intensive computations that can substantially trade off accuracy for speed in real-time imaging. In addition, state-of-the-art compressed sensing (CS) analytics are not cognizant of the image diagnostic quality. To cope with these challenges we put forth a novel CS framework that permeates benefits from generative adversarial networks (GAN) to train a (low-dimensional) manifold of diagnostic-quality MR images from historical patients. Leveraging a mixture of least-squares (LS) GANs and pixel-wise 1 cost, a deep residual network with skip connections is trained as the generator that learns to remove the aliasing artifacts by projecting onto the manifold. LSGAN learns the texture details, while 1 controls the high-frequency noise. A multilayer convolutional neural network is then jointly trained based on diagnostic quality images to discriminate the projection quality. The test phase performs feed-forward propagation over the generator network that demands a very low computational overhead. Extensive evaluations are performed on a large contrast-enhanced MR dataset of pediatric patients. In particular, images rated based on expert radiologists corroborate that GANCS retrieves high contrast images with detailed texture relative to conventional CS, and pixel-wise schemes. In addition, it offers reconstruction under a few milliseconds, two orders of magnitude faster than state-of-the-art CS-MRI schemes.
M Mardani et al. REF puts forward a novel compressed sensing framework that uses GAN to train a low-dimensional manifold of diagnostic-quality images.
Deep Generative Adversarial Networks for Compressed Sensing Automates MRI
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