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Today Gadolinium-based contrast agents (GBCA) are indispensable in MagneticResonance Imaging (MRI) for diagnosing various diseases. However, GBCAs areexpensive and may accumulate in patients with potential side effects, thusdose-reduction is recommended. Still, it is unclear to which extent the GBCAdose can be reduced while preserving the diagnostic value -- especially inpathological regions. To address this issue, we collected brain MRI scans atnumerous non-standard GBCA dosages and developed a conditional GAN model forsynthesizing corresponding images at fractional dose levels. Along with theadversarial loss, we advocate a novel content loss function based on theWasserstein distance of locally paired patch statistics for the faithfulpreservation of noise. Our numerical experiments show that conditional GANs aresuitable for generating images at different GBCA dose levels and can be used toaugment datasets for virtual contrast models. Moreover, our model can betransferred to openly available datasets such as BraTS, where non-standard GBCAdosage images do not exist.

Faithful Synthesis of Low-dose Contrast-enhanced Brain MRI Scans using Noise-preserving Conditional GANs