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- W2347565362 abstract "The least-squares algorithm is a traditional one for PET reconstruction,but the reconstructed images contain obvious noise and the convergence is slow.We started with the least squares objective function and introduced a penalty function to eliminate noise,and the optimal solution was obtained with modified SOR iterative method.Moreover,we combined modified ordered subsets method to accelerate the convergence.Simulations using Sheep-Logan model were utilized to estimate the quality of reconstructed images by some parameters,such as SNR,root mean squared error and correlation coefficient,to test and compare the effectiveness with other algorithms.Results showed that the proposed algorithm performed better in noise suppression compared with the penalized least-squares algorithm based on ordered subset(OS-LS).The quality indices,including signal to noise ratio,correlation coefficient and root mean squared error,were better than those of OS-LS.The convergence rate of the proposed algorithm was faster than that of OS-LS with less iterations(2 /3 of that of OS-LS)." @default.
- W2347565362 created "2016-06-24" @default.
- W2347565362 creator A5042109685 @default.
- W2347565362 date "2011-01-01" @default.
- W2347565362 modified "2023-09-28" @default.
- W2347565362 title "Least-squares Algorithm with Relaxation Factor and Penalty for PET Image Reonstruction" @default.
- W2347565362 hasPublicationYear "2011" @default.
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