Super resolution imaging from low-field strength scanners using generative adversarial networks

High-field MRI provides superior imaging for diverse clinical applications, but cost and other factors limit availability in various healthcare and lower resource settings. Lower-field strength units promise to expand access but involve tradeoffs including reduced signal, longer scan times, and lower resolution. Here we develop super-resolution methods that can generate high-field quality images from low-field scanner inputs, thus increasing signal and resolution. We use generative adversarial networks to demonstrate image enhancement in T1, T2 and FLAIR sequences.