Magnetic resonance (MR) images have exquisite soft tissue contrast and are critical to modern clinical imaging and medical science research. Automatic processing of MR images has always been hampered, however, by the lack of standardized tissue contrasts with standardized intensity scales. MR image harmonization or intensity normalization has long been investigated and used as part of neuroimaging pipelines to try to make quantitative measures compatible between MR scanners and across sites, but this has been a difficult task. In this talk, I will give an overview of past work and then describe three new harmonization approaches, each facilitated by a different style of deep network, that have recently been developed in my lab. The first approach is based on image synthesis, the second on domain adaptivity, and the third on a disentangled latent space. I will present brief overviews and results for each method and then discuss their limitations as well as needs and opportunities for future research on MR image harmonization.