Abstract #3173

Trajectory design of optimized repeating linear and nonlinear gradient encoding using a k-space point spread function metric

Nadine Luedicke Dispenza¹, Hemant Tagare^1,2, Gigi Galiana², and Robert Todd Constable¹

¹Biomedical Engineering, Yale University, New Haven, CT, United States, ²Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States

Accelerated imaging with nonlinear gradients can result in undersampling artifacts. A computationally efficient k-space point spread function metric that reflects the qualitative features of interest in the object is used to design a repeating nonlinear gradient trajectory that can be added to the linear trajectory. The nonlinear gradient solution is found through optimization of the metric calculated for only a few time points in the linear trajectory over a subregion of k-space containing the linear encoding. Images reconstructed from data simulated with the optimized nonlinear trajectories result in less undersampling artifacts compared to linear trajectories.

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