Fast group matching for MR fingerprinting reconstruction.
Fast group matching for MR fingerprinting reconstruction.
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DOI：
10.1002/mrm.25439
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发表时间：
2015-08
影响因子：
3.3
通讯作者：
Wald LL
中科院分区：
文献类型：
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作者：
Cauley SF;Setsompop K;Ma D;Jiang Y;Ye H;Adalsteinsson E;Griswold MA;Wald LL
MR 指纹识别 (MRF) 是一种使用伪随机测量进行定量组织绘图的技术。为了估计 T1、T2、质子密度和 B0 等组织特性，将快速获取的数据与 Bloch 模拟的大型字典进行比较。该匹配过程可能是 MRF 重建中计算要求非常高的部分。我们引入了一种快速组匹配算法 (GRM)，该算法利用 MRF 字典中的固有相关性来创建元素的高度聚类分组。在匹配过程中，首先使用组特定签名来消除不良匹配的可能性。组主成分分析 (PCA) 用于评估所有剩余的组织类型。 In vivo 3 Tesla 大脑数据用于验证我们方法的准确性。对于具有超过 196,000 个字典元素、1000 个 MRF 样本和 128 × 128 图像矩阵的 trueFISP 序列，GRM 能够使用标准供应商计算资源在 2 秒内映射 MR 参数。这比全局 PCA 快一个数量级，比直接匹配快近两个数量级，且精度相当（1-2% 相对误差）。所提出的 GRM 方法是一种用于 MRF 匹配的高效模型简化技术，并且应该能够在标准供应商计算资源上实现临床相关的重建精度和时间。
MR fingerprinting (MRF) is a technique for quantitative tissue mapping using pseudorandom measurements. To estimate tissue properties such as T1, T2, proton density, and B0, the rapidly acquired data are compared against a large dictionary of Bloch simulations. This matching process can be a very computationally demanding portion of MRF reconstruction. We introduce a fast group matching algorithm (GRM) that exploits inherent correlation within MRF dictionaries to create highly clustered groupings of the elements. During matching, a group specific signature is first used to remove poor matching possibilities. Group principal component analysis (PCA) is used to evaluate all remaining tissue types. In vivo 3 Tesla brain data were used to validate the accuracy of our approach. For a trueFISP sequence with over 196,000 dictionary elements, 1000 MRF samples, and image matrix of 128 × 128, GRM was able to map MR parameters within 2s using standard vendor computational resources. This is an order of magnitude faster than global PCA and nearly two orders of magnitude faster than direct matching, with comparable accuracy (1–2% relative error). The proposed GRM method is a highly efficient model reduction technique for MRF matching and should enable clinically relevant reconstruction accuracy and time on standard vendor computational resources.