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A Kernel-based Low-rank (KLR) Model for Low-dimensional Manifold Recovery in Highly Accelerated Dynamic MRI
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...
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| 出版年: | IEEE Trans Med Imaging |
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| 主要な著者: | , , , , |
| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
2017
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| 主題: | |
| オンライン･アクセス: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6422674/ https://ncbi.nlm.nih.gov/pubmed/28692970 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1109/TMI.2017.2723871 |
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