Generalizable synthetic MRI with physics-informed convolutional networks

1 code implementation • 21 May 2023 • Luuk Jacobs, Stefano Mandija, Hongyan Liu, Cornelis A. T. van den Berg, Alessandro Sbrizzi, Matteo Maspero

In this study, we develop a physics-informed deep learning-based method to synthesize multiple brain magnetic resonance imaging (MRI) contrasts from a single five-minute acquisition and investigate its ability to generalize to arbitrary contrasts to accelerate neuroimaging protocols.

Generative Adversarial Network