Keywords: Machine Learning/Artificial Intelligence, Data Acquisition, Data SamplingVariational information maximization allows joint optimization of MR data sampling and reconstruction and improves reconstruction quality upon the heuristically designed sampling patterns. Here, we analyze the learned sampling patterns with respect to changes in acceleration factor, measurement noise, anatomy, and coil sensitivities in order to provide some interpretation. We show that all of these factors contribute to the optimization result by impacting the sampling density, k-space coverage and point spread functions of the learned sampling patterns.
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