Journal
IEEE TRANSACTIONS ON MEDICAL IMAGING
Volume 36, Issue 11, Pages 2297-2307Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMI.2017.2723871
Keywords
Low rank models; compressed sensing; kernel method; preimaging; manifold recovery
Categories
Funding
- National Science Foundation (NSF) [CBET-1265612]
- NSF [CCF-1514403]
- National Institute of Health [R21EB020861]
- Direct For Computer & Info Scie & Enginr
- Division of Computing and Communication Foundations [1632599] Funding Source: National Science Foundation
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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.
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