Further Development of Subspace Imaging to Magnetic Resonance Fingerprinting: A Low-rank Tensor Approach.
Further Development of Subspace Imaging to Magnetic Resonance Fingerprinting: A Low-rank Tensor Approach.
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DOI：
10.1109/embc44109.2020.9175853
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发表时间：
2020-07
期刊：
影响因子：
--
通讯作者：
Wald LL
中科院分区：
文献类型：
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作者：
Zhao B;Setsompop K;Salat D;Wald LL
磁共振指纹识别是一种近期出现的定量磁共振成像技术，它能在单次成像实验中同时获取多个组织参数图（例如，T1、T2和自旋密度）。在我们早期的工作中，我们证明了低秩/子空间重建相较于利用简单模式匹配的传统磁共振指纹识别重建，显著提高了组织参数图的准确性。在本文中，我们通过引入一个多线性低维图像模型（即低秩张量模型）对低秩/子空间重建进行了推广。利用这个模型，我们进一步估计了与磁化演化相关的子空间，以简化图像重建问题。所提出的公式导致了一个非凸优化问题，我们通过交替最小化算法来解决它。我们通过数值实验评估了所提方法的性能，并证明所提方法改进了传统重建方法以及最先进的低秩重建方法。
Magnetic resonance fingerprinting is a recent quantitative MRI technique that simultaneously acquires multiple tissue parameter maps (e.g., T1, T2, and spin density) in a single imaging experiment. In our early work, we demonstrated that the low-rank/subspace reconstruction significantly improves the accuracy of tissue parameter maps over the conventional MR fingerprinting reconstruction that utilizes simple pattern matching. In this paper, we generalize the low-rank/subspace reconstruction by introducing a multilinear low-dimensional image model (i.e., a low-rank tensor model). With this model, we further estimate the subspace associated with magnetization evolutions to simplify the image reconstruction problem. The proposed formulation results in a nonconvex optimization problem which we solve by an alternating minimization algorithm. We evaluate the performance of the proposed method with numerical experiments, and demonstrate that the proposed method improves the conventional reconstruction method and the state-of-the-art low-rank reconstruction method.