Abstract #0082
Wave-CS: Combining wave encoding and compressed sensing
Andrew T Curtis 1 , Berkin Bilgic 2 , Kawin Setsompop 2 , Ravi S Menon 3 , and Christopher K Anand 1
1
Computing and Software, McMaster University,
Hamilton, Ontario, Canada,
2
Martinos
Center for Biomedical Imaging, Charlestown, MA, United
States,
3
Robarts Research Institute, London,
Ontario, Canada
The recently introduced WAVE encoding modulates the
phase/slice gradients such that the read trajectory
corkscrews through k-space, sampling additional spatial
frequencies. Here we investigate the combination of WAVE
encoding with compressed-sensing (CS) via random
phase/slice under-sampling patterns and sparsity
enforcing reconstruction, which we term Wave-CS. The
open source BART toolkit is leveraged for
reconstruction. The additional phase encoding and the
aliasing generated in the read direction from WAVE was
found to provide significant performance benefits in the
CS-framework as compared to regular Cartesian sampling,
with improved reconstruction quality and faster
iterative convergence for matched acceleration factors.
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