Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction, Side information
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.
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