The integration of compressed sensing and
parallel imaging (CS-PI) has shown an increased popularity
in recent years to accelerate magnetic resonance (MR)
imaging. Among them, calibration-free techniques have
presented encouraging performances due to its capability
in robustly handling the sensitivity information. Unfortunately,
existing calibration-freemethods have only explored
joint-sparsity with direct analysis transform projections.
To further exploit joint-sparsity and improve reconstruction
accuracy, this paper proposes to Learn joINt-sparse coDes
for caliBration-free parallEl mR imaGing (LINDBERG) by
modeling the parallel MR imaging problem as an 2âFâ2,1
minimization objective with an 2 norm constraining data
fidelity, Frobenius norm enforcing sparse representation
error and the 2,1 mixed norm triggering joint sparsity
acrossmultichannels. A corresponding algorithm has been
developed to alternatively update the sparse representation,
sensitivity encoded images and K-space data. Then,
the final image is produced as the square root of sum
of squares of all channel images. Experimental results on
both physical phantom and in vivo data sets show that the
proposedmethod is comparable and even superior to stateof-
the-art CS-PI reconstruction approaches. Specifically,
LINDBERG has presented strong capability in suppressing
noise and artifacts while reconstructing MR images from
highly undersampled multichannel measurements.