Mazor Gal, Weizman Lior, Tal Assaf, Eldar Yonina C
Department of Electrical Engineering, Technion - Israel Institute of Technology, Haifa, Israel.
Department of Chemical Physics, Weizmann Institute of Science, Rehovot, Israel.
Med Phys. 2018 Jul 4. doi: 10.1002/mp.13078.
Magnetic 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 equation simulations. 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).
We 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.
Experimental 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.
We 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.
磁共振指纹识别（MRF）是一种相对较新的方法，它通过随机采集来提供定量MRI测量。基于具有不同参数的采集以及根据布洛赫方程模拟生成的字典，在无需患者在场的情况下离线执行物理定量组织参数的提取。MRF使用数百个射频（RF）激发脉冲进行采集，因此，为了获得合理的扫描时间，在采样域（k空间）需要较高的欠采样率。这种欠采样会导致空间伪影，从而妨碍准确估计组织定量值的能力。在这项工作中，我们引入了一种使用MRF进行定量MRI的新方法，称为低秩磁共振指纹识别（FLOR）。
我们利用串联时间成像对比的低秩特性，此外MRF信号在生成的字典域中是稀疏表示的。我们提出了一种迭代恢复方案，该方案包括一个梯度步骤，随后是使用奇异值分解的低秩投影。
实验结果包括回顾性采样，可与明确的参考进行比较，以及前瞻性采样，展示了FLOR在实际数据采样场景中的性能。两个实验均表明，与其他基于压缩感知和低秩的MRF方法相比，在回顾性和前瞻性实验中，分别以5%和9%的采样率时，参数准确性得到了提高。
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能够恢复MRF时间欠采样图像，并且与之前的迭代方法相比，能够提供更准确的参数图。
