Reconstruction of dynamic positron emission tomographic images by exploiting low rank and sparse penalty.
- Author:
Xia-Ping WEI
1
,
2
,
3
;
Xue-Wen JIANG
;
Xiao-Mian MA
;
Li-Jun LU
Author Information
- Publication Type:Journal Article
- MeSH: Algorithms; Humans; Likelihood Functions; Positron-Emission Tomography; methods
- From: Journal of Southern Medical University 2015;35(10):1446-1450
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVETo propose a new method for dynamic positron emission tomographic (PET) image reconstruction using low rank and sparse penalty (L&S).
METHODSThe L&S reconstruction model was established and the split Bregman method was used to solve the optimal cost function. The one-tissue compartment model was used to simulate a set of PET 82Rb myocardial perfusion image. The L&S reconstruction method was compared with maximum likelihood expectation maximization (MLEM) method, low-rank penalty method and sparse penalty method.
RESULTSThe L&S reconstruction method had the smallest MSE and well maintained the feature information. The polar map created by L&S method was the most similar with the reference actual polar map.
CONCLUSIONL&S reconstruction method is better than the other three methods in both visual and quantitative analysis of the PET images.
