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Abstract #2462

Enhanced FRONSAC Encoding with Compressed Sensing

Haifeng Wang 1 , R. Todd Constable 1 , and Gigi Galiana 1

1 Yale University, New Haven, CT, United States

Nonlinear spatial encoding magnetic (SEM) fields have been studied to reduce the number of echoes needed to reconstruct a high quality image, but optimal schemes are still unknown. Previously, we showed that adding a rotating nonlinear field of modest amplitude, which we call the FRONSAC (Fast ROtary Nonlinear Spatial Acquisition) imaging, greatly improved the reconstructions obtained from highly undersampled conventional linear trajectories. However, since the ultimate goal is to acquire these highly undersampled trajectories in a single short TR, still lower amplitude FRONSAC gradients are desirable. FRONSAC creates undersampling artifacts that are relatively incoherent and well suited to CS reconstruction. Compressed sensing (CS) is a sparsity-promiting convex algorithm to reconstruct images from highly undersampled datasets. In this paper, we present a hybrid, CS-FRONSAC, which combines these two methods. The simulation results illustrate that the proposed method improves incoherence between the sensing and sparse domains, and it ultimately improves image quality compared with results recovered by the Kaczmarz algorithm. The resulting improvement allows us to consider FRONSAC gradients with lower amplitudes and frequencies, lowering hardware demands as well as dB/dt burden.

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