An improved approach in applying compressed sensing in parallel MR imaging
Both parallel MRI (pMRI, ) and compressed sensing (CS, ) allow image reconstruction from an under-sampled data set. The former exploits data redundancy in a sparse transform domain representation whereas the latter exploits the redundancy in multiple receiver data sets. Some success has already been reported in combining the two methods directly . We report a new approach in which conventional pMRI and CS are cascaded to better exploit the individual strengths of the two methods.