SVD compression for magnetic resonance fingerprinting in the time domain.
SVD compression for magnetic resonance fingerprinting in the time domain.
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DOI：
10.1109/tmi.2014.2337321
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发表时间：
2014-12
影响因子：
10.6
通讯作者：
Griswold MA
中科院分区：
文献类型：
--
作者：
McGivney DF;Pierre E;Ma D;Jiang Y;Saybasili H;Gulani V;Griswold MA
磁共振指纹识别是一种用于采集和处理MR数据的技术，其通过模式识别算法同时提供不同组织参数的定量图。预定义的字典对使用具有各种MR参数的不同组合的Bloch方程模拟的可能的信号演变进行建模，并且通过计算所观察到的信号与字典内的每个预测信号之间的内积来完成模式识别。虽然这种匹配算法已经被证明可以准确地预测感兴趣的MR参数，但人们希望有一种更有效的方法来获得定量图像。我们建议使用奇异值分解（SVD），这将提供一个低秩近似压缩字典。通过压缩的字典在时域中的大小，我们能够加快模式识别算法，由3.4-4.8之间的一个因素，而不牺牲高的信号-噪声比的原始方案先前提出的。
Magnetic resonance fingerprinting is a technique for acquiring and processing MR data that simultaneously provides quantitative maps of different tissue parameters through a pattern recognition algorithm. A predefined dictionary models the possible signal evolutions simulated using the Bloch equations with different combinations of various MR parameters and pattern recognition is completed by computing the inner product between the observed signal and each of the predicted signals within the dictionary. Though this matching algorithm has been shown to accurately predict the MR parameters of interest, one desires a more efficient method to obtain the quantitative images. We propose to compress the dictionary using the singular value decomposition (SVD), which will provide a low-rank approximation. By compressing the size of the dictionary in the time domain, we are able to speed up the pattern recognition algorithm, by a factor of between 3.4-4.8, without sacrificing the high signal-to-noise ratio of the original scheme presented previously.