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