Compressed sensing reconstruction with higher-order off-resonance correction using the cross-sampling and the time-segmented method

To design incoherent trajectories is important to achieve an efficient compressed sensing (CS) reconstruction. However, in practice, CS reconstruction using non-Cartesian sampling trajectories suffer from the off-resonance effect, which makes image distortion and artifacts. To overcome this problem, we developed a new CS reconstruction strategy using a cross-sampling, and a time-segmented off-resonance correction method. A B ₀ distribution map used for the off-resonance correction was estimated using an image registration based method. And a wavelet regularized split Bregman method was used for the reconstruction. Imaging experiments of a chemically fixed mouse demonstrated usefulness of our method.

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