MR images from fewer data

There is a strong motivation to reduce the amount of acquired data necessary to reconstruct clinically useful MR images, since less data means faster acquisition sequences, less time for the patient to remain motionless in the scanner and better time resolution for observing temporal changes within the body. We recently introduced an improvement in image quality for reconstructing parallel MR images by incorporating a data ordering step with compressed sensing (CS) in an algorithm named ‘PECS’. That method requires a prior estimate of the image to be available. We are extending the algorithm to explore ways of utilising the data ordering step without requiring a prior estimate. The method presented here first reconstructs an initial image x1 by compressed sensing (with sparsity enhanced by SVD), then derives a data ordering from x1, R ′ 1, which ranks the voxels of x1 according to their value. A second reconstruction is then performed which incorporates minimisation of the first norm of the estimate after ordering by R′ 1, resulting in a new reconstruction x2. Preliminary results are encouraging.