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Abstract #0229

Joint Data Driven Optimization of MRI Data Sampling and Reconstruction via Variational Information Maximization

Cagan Alkan1, Morteza Mardani1, Shreyas S. Vasanawala1, and John M. Pauly1
1Stanford University, Stanford, CA, United States

We propose a framework for learning the sampling pattern in MRI jointly with reconstruction in a data-driven manner using variational information maximization. We enable optimization of k-space samples via continuous parametrization of the sampling coordinates in the non-uniform FFT operator. Experiments with knee MRI shows improved reconstruction quality of our data-driven sampling over the prevailing variable-density sampling, highlighting possible benefits that can be obtained by learning data sampling patterns.

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