In magnetic resonance fingerprinting (MRF), tissue parameters are determined by finding the best-match to the acquired MR signal from a predefined signal dictionary. This dictionary searching (DS) process is generally performed in an exhaustive manner, which requires a large predefined dictionary and long searching time. A fast MRF DS algorithm, MRF-ZOOM, was recently proposed based on DS objective function optimization. As a proof-of-concept study, MRF-ZOOM was only tested with one of the earliest MRF sequences but not with the recently more popular unbalanced steady state free precession MRF sequence (MRF-ubSSFP, or MRF-FISP). Meanwhile noise effects on MRF and MRF-ZOOM have not been examined. The purpose of this study was to address these open questions and to verify whether MRF-ZOOM can be combined with a dictionary-compression based method to gain further speed. Numerical simulations were performed to evaluate the DS objective function properties, noise effects on MRF, and to compare MRF-ZOOM with other methods in terms of speed and accuracy. In-vivo experiments were performed as well. Evaluation results showed that premises of MRF-ZOOM held for MRF-FISP; noise did not affect MRF-ZOOM more than the conventional MRF method; when SNR ≥ 1, MRF quantification yielded accurate results. Dictionary compression introduced quantification errors more to T2 quantification. MRF-ZOOM was thousands of times faster than the conventional MRF method. Combining MRF-ZOOM with dictionary compression showed no benefit in terms of fitting speed. In conclusion, MRF-ZOOM is valid for MRF- FISP, and can remarkably save MRF dictionary generation and searching time without sacrificing matching accuracy.

中文翻译：

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MRF-ZOOM用于不平衡的稳态自由进动（ubSSFP）磁共振指纹图谱。

在磁共振指纹（MRF）中，通过从预定义的信号字典中找到与所采集的MR信号的最佳匹配来确定组织参数。通常以穷尽的方式执行该字典搜索（DS）过程，这需要较大的预定义字典和较长的搜索时间。最近，在DS目标函数优化的基础上，提出了一种快速的MRF DS算法MRF-ZOOM。作为概念验证研究，仅使用最早的MRF序列之一测试了MRF-ZOOM，但没有使用最近更流行的不平衡稳态无进动MRF序列（MRF-ubSSFP或MRF-FISP）进行测试。同时，尚未研究对MRF和MRF-ZOOM的噪声影响。这项研究的目的是解决这些悬而未决的问题，并验证MRF-ZOOM是否可以与基于字典压缩的方法结合使用以提高速度。进行了数值模拟，以评估DS目标函数特性，噪声对MRF的影响，并在速度和准确性方面将MRF-ZOOM与其他方法进行比较。还进行了体内实验。评估结果表明，MRF-ZOOM的前提适用于MRF-FISP；噪声对MRF-ZOOM的影响不比常规MRF方法大。当SNR≥1时，MRF定量得到准确的结果。字典压缩将量化误差引入了T2量化。MRF-ZOOM比常规MRF方法快数千倍。将MRF-ZOOM与字典压缩结合使用，在拟合速度方面没有任何好处。