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Abstract #4377

Optimized k-t Sampling for Combined Parallel Imaging and Compressed Sensing Reconstruction

Johannes F.M. Schmidt *1 , Claudio Santelli *1,2 , and Sebastian Kozerke 1,2

1 Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland, 2 Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom

Combining parallel imaging and compressed sensing (CS) has shown improved reconstruction performance as compared to applying either of the two methods alone. Sampling patterns are mostly designed to fully sample the k-space center while randomly undersample higher phase encodes. Trajectories combining regular and random undersampling have been shown to improve reconstruction accuracy. In dynamic imaging, time-interleaved k-t sampling may be used to reduce aliasing in the spatial temporal Fourier domain. We propose a k-t sampling scheme combining time-interleaved regular and random undersampling. Using cardiac short-axis data, it is demonstrated that this approach improves image reconstruction relative to standard CS trajectories.

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