文献类型：期刊文献

英文题名：Patch tensor decomposition and non-local means filter-based hybrid ASL image denoising

作者：He, Guanghua[1];Lu, Tianzhe[1];Li, Hongjuan[1];Lu, Jue[1];Zhu, Hancan[1]

机构：[1]Shaoxing Univ, Sch Math Phys & Informat, Chengnan Rd 900, Shaoxing 312000, Peoples R China

年份：2022

卷号：370

外文期刊名：JOURNAL OF NEUROSCIENCE METHODS

收录：SCI-EXPANDED(收录号：WOS:000788139600008)、、Scopus(收录号：2-s2.0-85124150866)、WOS

基金：Acknowledgments This work was supported by Zhejiang Provincial Natural Science Foundation of China [LY19F020013, LY20F020011] , National Natural Science Foundation of China [61602307, 61877039] and Scientific Research Fund of Zhejiang Provincial Education Department [Y201839295] . The authors would like to thank Dr. Ze Wang (University of Maryland) for providing the ASL images to validate the proposed method.

语种：英文

外文关键词：Hybrid ASL denoising; Tensor decomposition; Non-local means filter; FC analysis; Task activation detection

外文摘要：Background: Arterial spin labeling magnetic resonance imaging (ASL MRI) is a noninvasive technique to measure cerebral blood flow (CBF). It is widely used in the study of neurodegenerative diseases. Image denoising is an important step in ASL image processing because the signal-to-noise ratio (SNR) of an ASL CBF perfusion image is very small. New method: We propose a new ASL image denoising method that exploits patch-based low-rank and sparse tensor decomposition and a non-local means filter. Comparison with existing methods: The proposed method was compared with two existing ASL denoising methods: component-based noise correction method (CompCor) and low-rank and sparse matrix decomposition-based ASL image denoising method (LS-ASLd). Results: Various image quality measures, namely SNR, tSNR and ASL CBF variance, show that the proposed method is more effective than existing ASL denoising methods. The proposed method was used to denoise images from a resting state ASL dataset to compute brain functional connectivity (FC) and images from a task-related ASL dataset to identify brain activation. The results show that the proposed denoising method is more effective to enhance the sensitivity of ASL CBF series when undertaking CBF time series-based FC analysis and task activation detection. Conclusions: Assessment of the performance of the proposed hybrid ASL CBF image denoising method confirms that it is especially well-suited to FC analysis and sensorimotor task analysis.