Fast regularized reconstruction of non-uniformly subsampled parallel MRI data.

ABSTRACT Parallel MR imaging is an effective approach to reduce MR image acquisition time. Non-uniform subsampling allows one to tailor the subsampling,scheme,for improved,image quality at high acceleration factors. However, non-uniform subsam- pling precludes fast reconstruction schemes such as SENSE, and is more likely to require a regularized solution than recon- struction of uniformly subsampled,data demands. This means that one needs to choose a good regularization parameter, typ- ically requiring multiple expensive system solves. Here, we present an efficient LSQR-Hybrid algorithm which simulta- neously addresses the need for rapid regularization parameter selection and fast reconstruction. This algorithm can recon- struct non-uniformly subsampled parallel MRI data, with au- tomatic regularization and good image quality, in a time com- petitive with Cartesian SENSE.