A Kernel-Based Low-Rank (KLR) Model for Low-Dimensional Manifold Recovery in Highly Accelerated Dynamic MRI

@article{Nakarmi2017AKL,
  title={A Kernel-Based Low-Rank (KLR) Model for Low-Dimensional Manifold Recovery in Highly Accelerated Dynamic MRI},
  author={Ukash Nakarmi and Yanhua Wang and Jingyuan Lyu and Dong Liang and Leslie Ying},
  journal={IEEE Transactions on Medical Imaging},
  year={2017},
  volume={36},
  pages={2297-2307}
}
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… CONTINUE READING

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