Quantification method in [18F]fluorodeoxyglucose brain positron emission tomography using independent component analysis

2005 ◽  
Vol 26 (11) ◽  
pp. 995-1004 ◽  
Author(s):  
Kuan-Hao Su ◽  
Liang-Chih Wu ◽  
Ren-Shian Liu ◽  
Shih-Jen Wang ◽  
Jyh-Cheng Chen
Keyword(s):  
Positron Emission Tomography ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Emission Tomography ◽  
Quantification Method ◽  
18F Fluorodeoxyglucose ◽  
Positron Emission ◽  
Brain Positron Emission Tomography

Denoise PET Images Based on a Combining Method of EMD and ICA

2014 ◽  
Vol 981 ◽  
pp. 340-343
Author(s):  
Qi Wei ◽  
Qi Liu
Keyword(s):  
Positron Emission Tomography ◽  
Independent Component Analysis ◽  
Empirical Mode Decomposition ◽  
Independent Component ◽  
Emission Tomography ◽  
Intrinsic Mode Functions ◽  
Positron Emission ◽  
Mode Decomposition ◽  
Novel Method ◽  
Simulation Results

The incidental component in addition to the measured target signals is considered as noise of Positron Emission Tomography (PET) images. A novel method to denoise the PET images based on Empirical Mode Decomposition (EMD) and Independent Component Analysis (ICA) associated with Sparse Code Shrinkage (SCS) technique is proposed in this paper. EMD is executed to decompose a PET image into a number of Intrinsic Mode Functions (IMFs), which are used to reconstruct a new PET image after chosen by means of an inverse EMD procedure. By applying ICA to the new PET image, an orthogonal dataset can be obtained and the signal-noise separation can be realized. Then a clearer PET image can be reconstructed by SCS. The simulation results indicate that the proposed method is effective to denoise PET images.

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