Smoothed Random-like Trajectory (SRT) is a promising MR imaging method, but the data acquisitions have some hardware limitations of the gradient amplitude and slew rate. In order to realize the SRT in k-space, we proposed a new multiple-leaf SRT in k-space to reduce hardware requirements for applying the Compressed Sensing (CS) theory. To guarantee the constrains of the gradient amplitude and slew rate and reduce readout, the proposed multiple-leaf gradient waveforms were optimized by the time-optimal method for arbitrary k-space trajectories. The simulations have showed that the proposed method could greatly improve the reconstruction image quality, comparing to spiral trajectories.
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