A Clinically Applicable Scheme of MRI Trajectory Optimization for 3D Cartesian Acquisition
Enhao Gong 1 , Feng Huang 2 , and John M Pauly 1
Electrical Engineering, Stanford University,
Stanford, CA, United States,
Healthcare, Gainesville, FL, United States
Random undersampling is an important component used with
Parallel Imaging (PI) and Compressed Sensing (CS) and
their combination (PI-CS) for fast acquisition.
Optimized pseudo-random trajectory results in better
reconstruction yet the optimization is computational
costly. Lately, we proposed an efficient scheme for 1D
random undersampling optimization using stochastic
method and reference k-space. Here we extended and
improved the scheme to optimize the 2D Cartesian
undersampling for both PI and CS using Nonlinear Grappa
Operator and Coherence based objective function. In-vivo
experiments demonstrated greater performance improvement
for reconstruction using PI-CS. The scheme is also
applicable for non-Cartesian undersampling.
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