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Abstract #3173

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

Nadine Luedicke Dispenza1, Hemant Tagare1,2, 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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