Brain functional network reconstruction based on compressed sensing and fast iterative shrinkage-thresholding algorithm.
10.7507/1001-5515.201908024
- Author:
Qing GUO
1
,
2
;
Yueyang TENG
1
,
2
;
Can TONG
1
,
2
;
Disen LI
3
;
Xuefei WANG
3
Author Information
1. College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, P.R.China
2. Key Laboratory for Medical Imaging Intelligent Computing of Ministry of Education, Shenyang 110169, P.R.China.
3. College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, P.R.China.
- Publication Type:Journal Article
- Keywords:
brain functional network;
compressed sensing;
fast iterative shrinkage-thresholding algorithm;
least absolute shrinkage and selection operator
- MeSH:
Algorithms;
Brain/diagnostic imaging*;
Humans;
Image Processing, Computer-Assisted;
Magnetic Resonance Imaging
- From:
Journal of Biomedical Engineering
2020;37(5):855-862
- CountryChina
- Language:Chinese
-
Abstract:
The construction of brain functional network based on resting-state functional magnetic resonance imaging (fMRI) is an effective method to reveal the mechanism of human brain operation, but the common brain functional network generally contains a lot of noise, which leads to wrong analysis results. In this paper, the least absolute shrinkage and selection operator (LASSO) model in compressed sensing is used to reconstruct the brain functional network. This model uses the sparsity of
