A Kernel-Based Low-Rank (KLR) Model for Low-Dimensional Manifold Recovery in Highly Accelerated Dynamic MRI.
A Kernel-Based Low-Rank (KLR) Model for Low-Dimensional Manifold Recovery in Highly Accelerated Dynamic MRI.
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DOI：
10.1109/tmi.2017.2723871
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发表时间：
2017-11
影响因子：
10.6
通讯作者：
Ying L
中科院分区：
文献类型：
--
作者：
Nakarmi U;Wang Y;Lyu J;Liang D;Ying L
虽然已有许多基于低秩性和稀疏性的加速动态磁共振成像(DMRI)方法，但它们都是利用输入空间的低秩性或稀疏性，忽略了大多数dMRI数据固有的非线性相关性。在这篇文章中，我们提出了一个基于核的框架，允许从亚奈奎斯特数据重建非线性流形模型。在该框架下，许多已有的算法都可以扩展到具有非线性模型的核框架。特别是，我们开发了一种新的算法，该算法具有基于核的低秩模型(KLR)，推广了传统的低秩公式。该算法包括使用核的流形学习、特征空间的低阶强制和具有数据一致性的预成像。大量的仿真和实验结果表明，该方法优于传统的dMRI低阶建模方法。
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 (KLR) 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.