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Abstract #3877

OEDIPUS: Towards optimal deterministic k-space sampling for sparsity-constrained MRI

Justin P. Haldar1 and Daeun Kim1

1Electrical Engineering, University of Southern California, Los Angeles, CA, United States

We propose a novel approach to designing optimal k-space sampling patterns for sparsity-constrained MRI. The new approach, called OEDIPUS (Oracle-based Experiment Design for Imaging Parsimoniously Under Sparsity constraints), is inspired by insights and methods from estimation theory and the statistical design of experiments. Specifically, OEDIPUS combines the oracle-based Cramér-Rao bound for sparsity-constrained reconstruction with sequential greedy algorithms for observation selection. We demonstrate that OEDIPUS can be used to deterministically and automatically generate k-space sampling patterns that are tailored to specific hardware and application contexts, and which lead to better reconstruction performance relative to conventional sampling approaches for sparse MRI.

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