Department of Electrical and Computer Engineering, Temple University, Philadelphia, PA, USA.
Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.
J Neurosci Methods. 2018 Sep 1;307:248-253. doi: 10.1016/j.jneumeth.2018.06.007. Epub 2018 Jun 27.
Due to the low signal-to-noise-ratio (SNR) and unavoidable head motions, the pairwise subtraction perfusion signal extraction process in arterial spin labeling (ASL) perfusion MRI can produce extreme outliers.
We previously proposed an adaptive outlier cleaning (AOC) algorithm for ASL MRI. While it performed well even for clinical ASL data, two issues still exist. One is that if the reference is already dominated by noise, outlier cleaning using low correlation with the mean as a rejection criterion will actually reject the less noisy samples but keep the more noisy ones. The other is that it is sub-optimal to reject the entire outlier volumes without considering the quality of each constituent slices. To address both problems, a prior-guided and slice-wise AOC algorithm was proposed in this study.
The reference of AOC was initiated to be a pseudo cerebral blood flow (CBF) map based on prior knowledge and outlier rejection was performed at each slice. ASL data from the ADNI database (www.adni-info.org) were used to validate the method. Image preprocessing was performed using ASLtbx.
The proposed method outperformed the original AOC and SCORE in terms of higher SNR and test-retest stability of the resultant CBF maps.
ASL CBF can be substantially improved using prior-guided and slice-wise outlier rejection. The proposed method will benefit the ever since increasing ASL user community for both clinical and scientific brain research.
由于信噪比（SNR）低和不可避免的头部运动，动脉自旋标记（ASL）灌注 MRI 中的成对减法灌注信号提取过程可能会产生极端异常值。
我们之前提出了一种用于 ASL MRI 的自适应异常值清理（AOC）算法。虽然它即使在临床 ASL 数据上也能很好地工作，但仍存在两个问题。一个是如果参考已经被噪声主导，那么使用与平均值低相关作为拒绝标准的异常值清理实际上会拒绝噪声较小的样本，但保留噪声较大的样本。另一个是，如果不考虑每个组成切片的质量，就拒绝整个异常值体积是次优的。为了解决这两个问题，本研究提出了一种基于先验知识的引导和切片式 AOC 算法。
AOC 的参考最初是基于先验知识的伪脑血流（CBF）图，并且在每个切片上执行异常值拒绝。使用 ADNI 数据库（www.adni-info.org）中的 ASL 数据来验证该方法。图像预处理使用 ASLtbx 进行。
与原始 AOC 和 SCORE 相比，该方法在产生的 CBF 图的 SNR 和测试-重测稳定性方面表现更好。
使用基于先验知识的引导和切片式异常值拒绝可以大大提高 ASL CBF。该方法将使越来越多的 ASL 用户受益，无论是用于临床还是科学脑研究。
