  Multi-contrast MRI acquisitions of an anatomy enrich the magnitude of
information available for diagnosis. Yet, excessive scan times associated with
additional contrasts may be a limiting factor. Two mainstream approaches for
enhanced scan efficiency are reconstruction of undersampled acquisitions and
synthesis of missing acquisitions. In reconstruction, performance decreases
towards higher acceleration factors with diminished sampling density
particularly at high-spatial-frequencies. In synthesis, the absence of data
samples from the target contrast can lead to artefactual sensitivity or
insensitivity to image features. Here we propose a new approach for synergistic
reconstruction-synthesis of multi-contrast MRI based on conditional generative
adversarial networks. The proposed method preserves high-frequency details of
the target contrast by relying on the shared high-frequency information
available from the source contrast, and prevents feature leakage or loss by
relying on the undersampled acquisitions of the target contrast. Demonstrations
on brain MRI datasets from healthy subjects and patients indicate the superior
performance of the proposed method compared to previous state-of-the-art. The
proposed method can help improve the quality and scan efficiency of
multi-contrast MRI exams.
