文献类型：期刊文献

英文题名：Arterial spin labeling perfusion MRI signal denoising using robust principal component analysis

作者：Zhu, Hancan[1];Zhang, Jian[2];Wang, Ze[2,3]

机构：[1]Shaoxing Univ, Sch Math Phys & Informat, Shaoxing 312000, Peoples R China;[2]Hangzhou Normal Univ, Inst Psychol Sci, Ctr Cognit & Brain Disorders, Hangzhou 310010, Zhejiang, Peoples R China;[3]Temple Univ, Lewis Katz Sch Med, Dept Radiol, Philadelphia, PA 19140 USA

年份：2018

卷号：295

起止页码：10

外文期刊名：JOURNAL OF NEUROSCIENCE METHODS

收录：SCI-EXPANDED(收录号：WOS:000425556400002)、、Scopus(收录号：2-s2.0-85035750897)、WOS

基金：The work presented in this paper was supported by National Natural Science Foundation of China, under grant numbers: 61671198 and 61602307; Natural Science Foundation of Zhejiang Province, under grant number: LZ15H180001; the Youth 1000 Talent Program of China; Hangzhou Qianjiang Endowed Professor Program.

语种：英文

外文关键词：Arterial spin labeling; Temporal denoising; Robust principal component analysis; Functional connectivity

外文摘要：Background: 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. New method: 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). Results: The proposed method markedly increased temporal signal-to-noise-ratio (TSNR) and sensitivity of ASL CBF images for FC analysis and task activation detection. Conclusions: 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. (C) 2017 Elsevier B.V. All rights reserved.