基于体模和反卷积算法的PET 呼吸运动模糊校正

doi:10.3969/j.issn.0258-8021. 2013. 06.03

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Abstract Motion blur in PET images induced by respiratory motion may influence the accuracy of tumor diagnosis and treatment. In this paper, a method of correcting the motion blur, which combined highfrequency sinusoidal vibration model and deconvolution technology, was proposed. The high-frequency sinusoidal vibration model is used to simulate the tumor motion due to respiration. The blur direction was identified by performing Radon transform on the quasicepstrum of motion blurred image. The blur extent was identified from bispectrum of motion blurred image rotated to make the motion direction vertical. Based on this, RichardsonLucy iterative algorithm is used to correct respiratory motion blurs. Experiments on phantom images show that both the tumor volume and mean standard uptake value (SUV) derived from restored images are closer to the true values. Compared to uncorrection motion blur images, the error of tumor volume estimation was decreased from 40% to 10%, and the error of SUV is decreased from 28% to 4%. The results demonstrate that the proposed correction approach can improve the accuracy of PET quantification by correcting the respiratory motion blurs.

Key words： respiratory motion blur correction point spread function PET

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	MENG Xiang Hong¹CHEN Yang Chun²ZHENG Dong Qin^1*

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MENG Xiang Hong¹CHEN Yang Chun²ZHENG Dong Qin^{1*
. Correcting Respiratory Motion Blur in PET Images Based on Phantom and Deconvolution Algorithm[J]. journal1, 2013, 32(6): 655-662.}

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021. 2013. 06.03 OR http://cjbme.csbme.org/EN/Y2013/V32/I6/655

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