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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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