  \textit{Objective:} Gadolinium-based contrast agents (GBCAs) have been widely
used to better visualize disease in brain magnetic resonance imaging (MRI).
However, gadolinium deposition within the brain and body has raised safety
concerns about the use of GBCAs. Therefore, the development of novel approaches
that can decrease or even eliminate GBCA exposure while providing similar
contrast information would be of significant use clinically. \textit{Methods:}
In this work, we present a deep learning based approach for contrast-enhanced
T1 synthesis on brain tumor patients. A 3D high-resolution fully convolutional
network (FCN), which maintains high resolution information through processing
and aggregates multi-scale information in parallel, is designed to map
pre-contrast MRI sequences to contrast-enhanced MRI sequences. Specifically,
three pre-contrast MRI sequences, T1, T2 and apparent diffusion coefficient map
(ADC), are utilized as inputs and the post-contrast T1 sequences are utilized
as target output. To alleviate the data imbalance problem between normal
tissues and the tumor regions, we introduce a local loss to improve the
contribution of the tumor regions, which leads to better enhancement results on
tumors. \textit{Results:} Extensive quantitative and visual assessments are
performed, with our proposed model achieving a PSNR of 28.24dB in the brain and
21.2dB in tumor regions. \textit{Conclusion and Significance:} Our results
suggest the potential of substituting GBCAs with synthetic contrast images
generated via deep learning. Code is available at
\url{https://github.com/chenchao666/Contrast-enhanced-MRI-Synthesis
