Will a Convolutional Neural Network Trained for Non-contrast Water-Fat Separation Generalize to Post-Contrast Acquisitions?

A deep learning CNN trained using precontrast images generalizes to post-contrast images, providing equivalent image quality with fewer swap artifacts. For wide-spread adoption of deep learning methods, it is important that they have the capability to generalize beyond training data for flexible usage. This work provides important evidence that magnetic resonance deep learning water-fat separation can be used in a variety of settings.