Dataset Information

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. We demonstrate using phantom and in vivo data that the proposed methods are able to eliminate ghost artifacts for several navigator-free EPI acquisition schemes, obtaining better performance in comparison with the state-of-the-art methods across a range of different scenarios. Results are shown for both single-channel acquisition and highly accelerated multi-channel acquisition.

SUBMITTER: Lobos RA

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

REPOSITORIES: biostudies-literature

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

Lobos Rodrigo A RA Kim Tae Hyung TH Hoge W Scott WS Haldar Justin P JP

IEEE transactions on medical imaging 20180402 11

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 additi ...[more]

PMID: 29993978

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Dataset Information

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

Publications

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

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