Abstract #2874

Rapid De-aliasing of Undersampled Real-Time Phase-Contrast MRI Images using Generative Adversarial Network with Optimal Loss Terms

Huili Yang^1,2, Amanda Lynn DiCarlo^1,2, Daming Shen^1,2, Hassan Haji-Valizadeh^1,2, Michael Markl^1,2, and Daniel Kim^1,2

¹Department of Biomedical Engineering, Northwestern University, Evanston, IL, United States, ²Department of Radiology, Northwestern University, Chicago, IL, United States

While compressed sensing is a proven method for highly accelerating cardiovascular MRI, its lengthy reconstruction time hinders clinical translation. Deep learning is a promising method to accelerate reconstruction processing. We propose a generative adversarial network (GAN) with optimal loss terms for rapid reconstruction of 28.8-fold accelerated real-time phase-contrast MRI. Our results show that GAN reconstructs images 613 times faster than compressed sensing without significant loss in peak and mean velocity measurements and image sharpness.

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