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2016 JRNL PP Rodrigo A. Lobos - 1.pdf
Terbatas  Irwan Sofiyan
» Gedung UPT Perpustakaan

Structured low-rank matrix models have previously been introduced to enable calibrationlessMR image reconstruction from sub-Nyquist data, and such ideas have recently been extended to enable navigator-free echoplanar 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 stateof- the-art methods across a range of different scenarios. Results are shown for both single-channel acquisition and highly accelerated multi-channel acquisition.