k-t sparse GROWL: Sequential combination of partially parallel imaging and compressed sensing in k-t space using flexible virtual coil

Because dynamic MR images are often sparse in x‐f domain, k‐t space compressed sensing (k‐t CS) has been proposed for highly accelerated dynamic MRI. When a multichannel coil is used for acquisition, the combination of partially parallel imaging and k‐t CS can improve the accuracy of reconstruction. In this work, an efficient combination method is presented, which is called k‐t sparse Generalized GRAPPA fOr Wider readout Line. One fundamental aspect of this work is to apply partially parallel imaging and k‐t CS sequentially. A partially parallel imaging technique using a Generalized GRAPPA fOr Wider readout Line operator is adopted before k‐t CS reconstruction to decrease the reduction factor in a computationally efficient way while preserving temporal resolution. Channel combination and relative sensitivity maps are used in the flexible virtual coil scheme to alleviate the k‐t CS computational load with increasing number of channels. Using k‐t FOCUSS as a specific example of k‐t CS, the experiments with Cartesian and radial data sets demonstrate that k‐t sparse Generalized GRAPPA fOr Wider readout Line can produce results with two times lower root‐mean‐square error than conventional channel‐by‐channel k‐t CS while consuming up to seven times less computational cost. Magn Reson Med, 2012. © 2011 Wiley Periodicals, Inc.