This abstract explores non-Cartesian sampling trajectories that are optimized specifically for various reconstruction methods (CG-SENSE, penalized least-squares, compressed sensing, and unrolled neural networks). The learned sampling trajectories vary in the k-space coverage strategy and may reflect underlying characteristics of the corresponding reconstruction method. The reconstruction-specific sampling trajectory optimization leads to the most reconstruction quality improvement. This work demonstrates the potential benefit of jointly optimizing imaging protocols and downstream tasks (i.e., image reconstruction).
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