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Abstract #1185

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

Arda Atalik1,2,3, Sumit Chopra3,4, and Daniel K Sodickson2,3
1Center for Data Science, New York University, New York, NY, United States, 2Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States, 3Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States, 4Courant Institute of Mathematical Sciences, New York University, New York, NY, United States

Synopsis

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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Keywords