  Today Gadolinium-based contrast agents (GBCA) are indispensable in Magnetic
Resonance Imaging (MRI) for diagnosing various diseases. However, GBCAs are
expensive and may accumulate in patients with potential side effects, thus
dose-reduction is recommended. Still, it is unclear to which extent the GBCA
dose can be reduced while preserving the diagnostic value -- especially in
pathological regions. To address this issue, we collected brain MRI scans at
numerous non-standard GBCA dosages and developed a conditional GAN model for
synthesizing corresponding images at fractional dose levels. Along with the
adversarial loss, we advocate a novel content loss function based on the
Wasserstein distance of locally paired patch statistics for the faithful
preservation of noise. Our numerical experiments show that conditional GANs are
suitable for generating images at different GBCA dose levels and can be used to
augment datasets for virtual contrast models. Moreover, our model can be
transferred to openly available datasets such as BraTS, where non-standard GBCA
dosage images do not exist.
