Priors-guided slice-wise adaptive outlier cleaning for arterial spin labeling perfusion MRI
Priors-guided slice-wise adaptive outlier cleaning for arterial spin labeling perfusion MRI
复制标题
先验引导的切片自适应异常值清理用于动脉自旋标记灌注 MRI
DOI：
10.1016/j.jneumeth.2018.06.007
复制
发表时间：
2018-09-01
影响因子：
3
通讯作者：
Wang, Ze
中科院分区：
文献类型：
--
作者：
Li, Yiran;Dolui, Sudipto;Wang, Ze
背景资料：由于低信噪比（SNR）和不可避免的头部运动，在动脉自旋标记（ASL）灌注MRI中，两两相减的灌注信号提取过程会产生极端的离群值。虽然它甚至对于临床ASL数据也表现良好，但仍然存在两个问题。一个是，如果参考已经被噪声主导，则使用与均值的低相关性作为拒绝标准的离群值清除实际上将拒绝噪声较小的样本，但保留噪声较大的样本。另一个是在不考虑每个组成切片的质量的情况下拒绝整个离群值体积是次优的。为了解决这两个问题，本研究提出了一种先验引导的分层AOC算法。新方法：AOC的参考是基于先验知识的伪脑血流量（CBF）图，并在每个切片上进行离群值剔除。来自ADNI数据库（www.adni-info.org）的ASL数据用于验证该方法。图像预处理进行ASLtbx.Results：所提出的方法优于原始AOC和SCORE在更高的信噪比和重测稳定性的CBF maps.Conclusion：ASL CBF可以大大提高使用事先指导和切片的离群拒绝。所提出的方法将有利于不断增加的ASL用户社区的临床和科学的大脑研究。
Background: 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.Comparison with existing methods: 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.New Methods: 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.Results: The proposed method outperformed the original AOC and SCORE in terms of higher SNR and test-retest stability of the resultant CBF maps.Conclusion: 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.