Navigator-Free EPI Ghost Correction With Structured Low-Rank Matrix Models: New Theory and Methods.

ABSTRACT: Structured low-rank matrix models have previously been introduced to enable calibrationless MR image reconstruction from sub-Nyquist data, and such ideas have recently been extended to enable navigator-free echo-planar imaging (EPI) ghost correction. This paper presents a novel theoretical analysis which shows that, because of uniform subsampling, the structured low-rank matrix optimization problems for EPI data will always have either undesirable or non-unique solutions in the absence of additional constraints. This theory leads us to recommend and investigate problem formulations for navigator-free EPI that incorporate side information from either image-domain or k-space domain parallel imaging methods. The importance of using nonconvex low-rank matrix regularization is also identified.

SUBMITTER: Lobos RA 

PROVIDER: S-EPMC6309699 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature