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Abstract #3473

A domain-agnostic MR reconstruction framework using a randomly weighted neural network

Arghya Pal1 and Yogesh Rathi1
1Department of Psychiatry, Harvard Medical School, Boston, MA, United States


Can a random weighted deep network structure encode informative cues to solve the MR reconstruction problem from highly under-sampled k-space measurements? Trained networks update the weights at training time, while untrained networks optimize the weights at inference time. In contrast, our proposed methodology selects an optimal subnetwork from a randomly weighted dense network to perform MR reconstruction without updating the weights - neither at training time nor at inference time. The methodology does not require ground truth data and shows excellent performance across domains in T1-weighted (head, knee) images from highly under-sampled multi-coil k-space measurements.

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