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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