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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,...
High-dimensional signals, including dynamic magnetic resonance (dMR) images, often lie on low dimensional manifold. While many current dynamic magnetic resonance imaging (dMRI) reconstruction methods rely on priors which promote low-rank and sparsity, this paper proposes a novel manifold-based framework, we term M-MRI, for dMRI reconstruction from highly undersampled k-space data. Images in dMRI are...
Compressed Sensing (CS) is a new paradigm in signal processing and reconstruction from sub-nyquist sampled data. CS has shown promising results in accelerating dynamic Magnetic Resonance Imaging (dMRI). CS based approaches hugely rely on sparsifying transforms to reconstruct the dynamic MR images from its undersampled k-space data. Recent developments in dictionary learning and nonlinear kernel based...
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