Deep Generative Adversarial Networks for Compressed Sensing Automates MRI
Magnetic resonance image (MRI) reconstruction is a severely ill posed linear inverse task demanding time and resource intensive computations that can substantially trade off \it accuracy} for \it speed} in real time imaging. In addition, state of the art compressed sensing (CS) analytics are not cognizant of the image \it 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 \ell_1$ cost, a deep residual network with skip connections is trained as the generator that learns to remove the \it aliasing} artifacts by projecting onto the manifold. LSGAN learns the texture details, while \ell_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.
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