Mazor Gal, Weizman Lior, Tal Assaf, Eldar Yonina C
Annu Int Conf IEEE Eng Med Biol Soc. 2016 Aug;2016:439-442. doi: 10.1109/EMBC.2016.7590734.
Magnetic Resonance Fingerprinting (MRF) is a relatively new approach that provides quantitative MRI using randomized acquisition. Extraction of physical quantitative tissue values is preformed off-line, based on acquisition with varying parameters and a dictionary generated according to the Bloch equations. MRF uses hundreds of radio frequency (RF) excitation pulses for acquisition, and therefore high under-sampling ratio in the sampling domain (k-space) is required. This under-sampling causes spatial artifacts that hamper the ability to accurately estimate the quantitative tissue values. In this work, we introduce a new approach for quantitative MRI using MRF, called Low Rank MRF. We exploit the low rank property of the temporal domain, on top of the well-known sparsity of the MRF signal in the generated dictionary domain. We present an iterative scheme that consists of a gradient step followed by a low rank projection using the singular value decomposition. Experiments on real MRI data demonstrate superior results compared to conventional implementation of compressed sensing for MRF at 15% sampling ratio.
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磁共振成像。物理定量组织值的提取是离线进行的，基于具有不同参数的采集以及根据布洛赫方程生成的字典。MRF使用数百个射频（RF）激发脉冲进行采集，因此在采样域（k空间）中需要高欠采样率。这种欠采样会导致空间伪影，从而妨碍准确估计定量组织值的能力。在这项工作中，我们引入了一种使用MRF进行定量磁共振成像的新方法，称为低秩MRF。我们利用时域的低秩特性，以及在生成字典域中MRF信号众所周知的稀疏性。我们提出了一种迭代方案，该方案由一个梯度步骤和随后使用奇异值分解的低秩投影组成。在真实磁共振成像数据上的实验表明，与在15%采样率下MRF的传统压缩感知实现相比，结果更优。
