Joint recovery of variably accelerated multi-contrast MRI acquisitions via generative adversarial networks

Two frameworks to recover missing data in accelerated MRI are reconstruction of undersampled acquisitions and synthesis of missing acquisitions. In reconstruction, performance diminishes towards higher acceleration factors and in synthesis, lack of evidence regarding the target contrast can lead to artefactual sensitivity or insensitivity to image features. To address these issues, we propose an approach that synergistically performs reconstruction and synthesis of multi-contrast MRI using generative adversarial networks. Demonstrations on brain MRI datasets from healthy subjects and patients indicate that the proposed method preserves intermediate spatial frequency details and prevents artefactual feature synthesis or feature loss as compared to previous state-of-the-art methods.