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Abstract #0573

Deep Generative Adversarial Neural Networks for Compressed Sensing (GANCS) Automates MRI

Morteza Mardani1, Enhao Gong2, Joseph Cheng3, Shreyas Vasanawala4, Greg Zaharchuk4, Lei Xing1,5, and John Pauly6

1Electrical Engineering, Stanford University, Stanford, CA, United States, 2Electrical Engineering, Stanford University, stanford, CA, United States, 3Stanford University, stanford, CA, United States, 4Radiology, Stanford University, Stanford, CA, United States, 5Radiation Oncology, Stanford University, Stanford, CA, United States, 6Stanford University, Stanford, CA, United States

MRI suffers from aliasing artifacts when undersampled for real-time imaging. Conventional compressed sensing (CS) is not however cognizant of image diagnostic quality, and substantially trade-off accuracy for speed in real-time imaging. To cope with these challenges we put forth a novel CS framework that permeates benefits from generative adversarial networks (GAN) to modeling a manifold of MR images from historical patients. Evaluations on a large abdominal MRI dataset of pediatric patients by expert radiologists corroborate that GANCS retrieves improved images with finer details relative to CS-MRI and deep learning schemes with pixel-wise costs, at 100 times faster speed than CS-MRI.

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