ImUnity is an original deep-learning model designed for efficient and flexible MRI harmonization. A VAE-GAN network is coupled with a confusion and a biological preservation module. It ‘corrects’ MR images that can be used for various multi-center studies. Using 3 open source databases, we show that ImUnity: outperforms state-of-the-art methods in terms of quality of images generated; removes sites/scanner bias while improving patients classification; harmonizes data coming from new sites/scanners and allows the selection of multiple MR reconstructed images according to the desired applications. Tested on T1w images, ImUnity could be generalized to other types of medical images.