Abstract：
:While many low rank and sparsity-based approaches have been developed for accelerated dynamic magnetic resonance imaging (dMRI), they all use low rankness or sparsity in input space, overlooking the intrinsic nonlinear correlation in most dMRI data. In this paper, we propose a kernel-based framework to allow nonlinear manifold models in reconstruction from sub-Nyquist data. Within this framework, many existing algorithms can be extended to kernel framework with nonlinear models. In particular, we have developed a novel algorithm with a kernel-based low-rank model generalizing the conventional low rank formulation. The algorithm consists of manifold learning using kernel, low rank enforcement in feature space, and preimaging with data consistency. Extensive simulation and experiment results show that the proposed method surpasses the conventional low-rank-modeled approaches for dMRI.
journal_name
IEEE Trans Med Imagingjournal_title
IEEE transactions on medical imagingauthors
Nakarmi U,Wang Y,Lyu J,Liang D,Ying Ldoi
10.1109/TMI.2017.2723871subject
Has Abstractpub_date
2017-11-01 00:00:00pages
2297-2307issue
11eissn
0278-0062issn
1558-254Xjournal_volume
36pub_type
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