Gadolinium-based contrast agents (GBCAs) create unique image contrast to facilitate identification of various clinical findings. However, recent discovery of gadolinium deposition after contrast-enhanced MRI raises new safety concerns of GBCAs. Deep learning (DL) has recently been used to predict the contrast-enhanced images using only a fraction of the standard dose. However, challenges remain in generalizing the DL methods across different protocols/vendors/institutions. In this work, we propose comprehensive technical solutions to improve DL model robustness and obtain high quality low-dose contrast-enhanced MRI across multiple scanners and institutions.