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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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| Опубликовано в: : | IEEE Trans Med Imaging |
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| Главные авторы: | , , , , |
| Формат: | Artigo |
| Язык: | Inglês |
| Опубликовано: |
2017
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| Предметы: | |
| Online-ссылка: | 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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