MRFlow: Flow-based neural network for MR image harmonization

We propose MRFlow, which is a normalizing flow-based neural network for the MRI harmonization framework. With the normalizing flow trained only with the target domain (e.g., 3T image) data, we harmonize the image from the source domain (e.g., 1.5T image) to the target domain by alternately reducing the norm of the latent variable and increasing similarity between the source domain and harmonized images. When MRFlow is applied to synthesized source domain images, the harmonized images showed lower errors than the source domain images. In the prospective study, the harmonized images became more similar to the target domain images after MRFlow.