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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