We benchmark the current existing methods to jointly learn non-Cartesian k-space trajectory and reconstruction: PILOT1, BJORK2 and compare them with those obtained from recently developed generalized hybrid learning (HybLearn) framework3. We present the advantages of using projected gradient descent to enforce MR scanner hardware constraints as compared to using added penalties in the cost function. Further, we use the novel HybLearn scheme to jointly learn and compare our results through retrospective study on fastMRI validation dataset.
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