Semi-Supervised Learning of Mutually Accelerated MRI Synthesis without Fully-Sampled Ground Truths

Understand

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.