  Learning-based synthetic multi-contrast MRI commonly involves deep models
trained using high-quality images of source and target contrasts, regardless of
whether source and target domain samples are paired or unpaired. This results
in undesirable reliance on fully-sampled acquisitions of all MRI contrasts,
which might prove impractical due to limitations on scan costs and time. Here,
we propose a novel semi-supervised deep generative model that instead learns to
recover high-quality target images directly from accelerated acquisitions of
source and target contrasts. To achieve this, the proposed model introduces
novel multi-coil tensor losses in image, k-space and adversarial domains. These
selective losses are based only on acquired k-space samples, and randomized
sampling masks are used across subjects to capture relationships among acquired
and non-acquired k-space regions. Comprehensive experiments on multi-contrast
neuroimaging datasets demonstrate that our semi-supervised approach yields
equivalent performance to gold-standard fully-supervised models, while
outperforming a cascaded approach that learns to synthesize based on
reconstructions of undersampled data. Therefore, the proposed approach holds
great promise to improve the feasibility and utility of accelerated MRI
acquisitions mutually undersampled across both contrast sets and k-space.
