Low-rank magnetic resonance fingerprinting
Low-rank magnetic resonance fingerprinting
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DOI：
10.1002/mp.13078
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发表时间：
2018-09-01
期刊：
影响因子：
3.8
通讯作者：
Eldar, Yonina C.
中科院分区：
文献类型：
--
作者：
Mazor, Gal;Weizman, Lior;Eldar, Yonina C.
目的磁共振指纹（MRF）是一种相对较新的方法，通过随机采集提供定量MRI测量。物理定量组织参数的提取是离线进行的，不需要患者在场，基于不同参数的采集和根据Bloch方程模拟生成的字典。MRF使用数百个射频（RF）激励脉冲进行采集，因此，在采样域（k空间）需要较高的欠采样率以保证合理的扫描时间。这种采样不足导致空间伪影，妨碍了准确估计组织定量值的能力。在这项工作中，我们介绍了一种新的MRI定量方法，称为低秩磁共振指纹（FLOR）。方法利用串联时间成像对比的低秩特性，在生成的字典域中稀疏表示MRF信号。我们提出了一种利用奇异值分解的迭代恢复方案，该方案由梯度步骤和低秩投影组成。结果实验结果包括回顾性抽样和前瞻性抽样，前者允许与定义良好的参考进行比较，后者显示了FLOR在实际数据抽样场景中的性能。与其他压缩感知和基于低秩的MRF方法相比，这两个实验分别在5%和9%的回顾性和前瞻性实验采样率下证明了参数精度的提高。我们通过回顾性和前瞻性实验表明，通过利用MRF信号的低秩特性，FLOR可以恢复MRF时间欠采样图像，并提供比以前迭代方法更准确的参数图。
PurposeMagnetic resonance fingerprinting (MRF) is a relatively new approach that provides quantitative MRI measures using randomized acquisition. Extraction of physical quantitative tissue parameters is performed offline, without the need of patient presence, based on acquisition with varying parameters and a dictionary generated according to the Bloch equationsimulations. MRF uses hundreds of radio frequency (RF) excitation pulses for acquisition, and therefore, a high undersampling ratio in the sampling domain (k-space) is required for reasonable scanning time. This undersampling causes spatial artifacts that hamper the ability to accurately estimate the tissue's quantitative values. In this work, we introduce a new approach for quantitative MRI using MRF, called magnetic resonance fingerprinting with low rank (FLOR).MethodsWe exploit the low-rank property of the concatenated temporal imaging contrasts, on top of the fact that the MRF signal is sparsely represented in the generated dictionary domain. We present an iterative recovery scheme that consists of a gradient step followed by a low-rank projection using the singular value decomposition.ResultsExperimental results consist of retrospective sampling that allows comparison to a well defined reference, and prospective sampling that shows the performance of FLOR for a real-data sampling scenario. Both experiments demonstrate improved parameter accuracy compared to other compressed-sensing and low-rank based methods for MRF at 5% and 9% sampling ratios for the retrospective and prospective experiments, respectively.ConclusionsWe have shown through retrospective and prospective experiments that by exploiting the low-rank nature of the MRF signal, FLOR recovers the MRF temporal undersampled images and provides more accurate parameter maps compared to previous iterative approaches.