Respiratory and cardiac motion may cause artifacts in body trunk imaging if patients cannot hold their breath or triggered acquisitions are not practical. Retrospective correction strategies cope with motion by fast sequences under free-movement conditions. In the acquisition, a random subsampling with global variable-density scaling is usually applied. Improved sampling efficiency in terms of acquired SNR per sample can be achieved if the underlying energy distribution is considered. An image upscaling via dictionary learning from the localizer with extraction of the energy distribution and local shaping of the hybrid Cartesian subsampling improves the overall image quality and SNR by 45%.