Learning-based methods offer improved reconstruction accuracy for compressed Sensing MRI. However, most modern methods assume the sampling trajectory to be predefined. In order to further increase reconstruction quality, we present a method for adaptive design of Cartesian undersampling masks. The proposed method delivers sampling trajectories that allow to improve reconstruction accuracy by 26% and 6% compared to the random and state-of-the-art interleaved variable density patterns, respectively.
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