School of Mathematics Physics and Information, Shaoxing University, Shaoxing, 312000, China.
Center for Cognition and Brain Disorders, Institutes of Psychological Science, Hangzhou Normal University, Hangzhou, 310010, China.
J Neurosci Methods. 2018 Feb 1;295:10-19. doi: 10.1016/j.jneumeth.2017.11.017. Epub 2017 Nov 28.
Arterial spin labeling (ASL) perfusion MRI provides a non-invasive way to quantify regional cerebral blood flow (CBF) and has been increasingly used to characterize brain state changes due to disease or functional alterations. Its use in dynamic brain activity study, however, is still hampered by the relatively low signal-to-noise-ratio (SNR) of ASL data.
The aim of this study was to validate a new temporal denoising strategy for ASL MRI. Robust principal component analysis (rPCA) was used to decompose the ASL CBF image series into a low-rank component and a sparse component. The former captures the slowly fluctuating perfusion patterns while the latter represents spatially incoherent spiky variations and was discarded as noise. While there still lacks a way to determine the parameter for controlling the balance between the low-rankness and sparsity of the decomposition, we designed a method to solve this problem based on the unique data structures of ASL MRI. Method evaluations were performed with ASL CBF-based functional connectivity (FC) analysis and a sensorimotor functional ASL MRI study.
COMPARISON WITH EXISTING METHOD(S): The proposed method was compared with the component based noise correction method (CompCor).
The proposed method markedly increased temporal signal-to-noise-ratio (TSNR) and sensitivity of ASL CBF images for FC analysis and task activation detection.
We proposed a new temporal ASL CBF image denoising method, and showed its benefit for the CBF time series-based FC analysis and task activation detection.
动脉自旋标记（ASL）灌注 MRI 提供了一种非侵入性的方法来量化局部脑血流（CBF），并已越来越多地用于描述由于疾病或功能改变导致的脑状态变化。然而，其在动态脑活动研究中的应用仍然受到 ASL 数据相对较低的信噪比（SNR）的限制。
本研究旨在验证一种新的 ASL MRI 时间去噪策略。稳健主成分分析（rPCA）用于将 ASL CBF 图像序列分解为低秩分量和稀疏分量。前者捕获缓慢波动的灌注模式，而后者代表空间上不一致的尖峰变化，并作为噪声丢弃。虽然仍然缺乏一种方法来确定控制分解的低秩和稀疏之间平衡的参数，但我们设计了一种基于 ASL MRI 独特数据结构的方法来解决这个问题。方法评估是通过基于 ASL CBF 的功能连接（FC）分析和感觉运动功能 ASL MRI 研究进行的。
所提出的方法与基于分量的噪声校正方法（CompCor）进行了比较。
该方法显著提高了 FC 分析和任务激活检测的 ASL CBF 图像的时间信噪比（TSNR）和灵敏度。
我们提出了一种新的 ASL CBF 图像时间去噪方法，并证明了其在基于 CBF 时间序列的 FC 分析和任务激活检测中的益处。
