Resolving ambiguous space: Leveraging side information with deep learning to extend the limits of MR image reconstruction

Motivation: Reconstruction quality sharply declines beyond certain acceleration levels, resulting in non-diagnostic images. Leveraging diverse sources of readily available side information offers a promising solution to this challenge, improving disambiguation during reconstruction and enabling higher acceleration rates while preserving diagnostic image quality. Goal(s): To reliably incorporate additional contextual information (relevant side information) into the MR image reconstruction. Approach: Eliminate undesirable solutions from the ambiguous space of the forward operator, while remaining faithful to the acquired data. Results: Compared to a set of baselines that also use side information, our method reconstructs high-quality knee MR images in the presence of heretofore challenging levels of under-sampling. Impact: By leveraging readily available sources of information which may not generally be used for image reconstruction, our approach reduces ambiguities, enabling more accurate solutions even with highly-sparse measurements.