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J4  2012, Vol. 46 Issue (6): 961-966    DOI: 10.3785/j.issn.1008-973X.2012.06.001
    
Dynamic PET image reconstruction with Geometrical structure
prior constraints
ZHANG Jun-chao1, YUE Mao-xiong2, LIU Hua-feng1
1. State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou 310027, China;
2. The Fourth Research Institute,China Aerodynamics Research and Development Center, Mianyang 62100, China
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Abstract  

In order to improve the image quality,an improved algorithm for dynamic positron emission tomography(PET) image reconstruction was proposed by using segmented anatomical template that provided by other high quality imaging technology. Based on state space theory, a dynamic PET image reconstruction framework for low count rate and high noise environment was formulated with the observation equation of detectors and a modified evolution equation incorporating structural constraint which was generated to guide the reconstruction process, and H∞ filtering principle was employed to solve the above two equations. Compared with other algorithms, experiments conducted by Monte Carlo simulations indicate a persuasive assessment that the proposed strategy was particularly applicable for real-world situations with the uncertainties of system and statistical properties, suppresses noise well, while the boundary information and other details remain clear.



Published: 24 July 2012
CLC:  TP 301.6  
Cite this article:

ZHANG Jun-chao, YUE Mao-xiong, LIU Hua-feng. Dynamic PET image reconstruction with Geometrical structure
prior constraints. J4, 2012, 46(6): 961-966.

URL:

http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2012.06.001     OR     http://www.zjujournals.com/eng/Y2012/V46/I6/961


结构先验约束的动态PET图像重建

为提高图像质量,提出一种利用其他高质量解剖模板作为先验,引导动态正电子发射断层 (PET) 成像重建的方法.该方法基于状态空间理论体系,将与生理特性相关的解剖模板耦合于状态方程中,并与成像模型相结合构成完善的状态空间方程组,运用具有鲁棒性的H∞滤波法求解,从而构建一种适合于符合计数率低、噪声影响显著的动态PET图像的重建框架.蒙特卡洛模拟实验结果表明,与其他传统方法相比,本方法在能够适应实际动态PET成像中统计特性和系统特性不确定的基础上,进一步抑制了噪声,并保持了图像边缘和细节信息.

[1] LEAHY R M, QI J. Statistical approaches in quantitative positron emission tomography [J]. Statistics and Computing. 2000, 102): 147-165.
[2] ZHOU Z, LEAHY R M, MUMCUOGLU E U. A comparative study of the effects of using anatomical priors in PET reconstruction[C]∥Proccedings of The 1993 IEEE Nuclear Science Symposium and Medical Imaging Conference. [S. l.]:IEEE, 1993: 1749-1753.
[3] BOWSHER J E, JOHNSON V E, TURKINGTON T G, et al. Bayesian reconstruction and use of anatomical a priori information for emission tomography[J]. IEEE Transactions on Medical Imaging, 1996, 15(5): 673-686.
[4] RANGARAJAN A, HSIAO I T, GINDI G. A Bayesian joint mixture framework for the integration of anatomical information in functional image reconstruction[J]. Journal of Mathematical Imaging and Vision, 2000, 12(3):  199-217.
[5] JING T, ARMAN R. Bayesian PET image reconstruction incorporating anatofunctional joint entropy[J]. Physics in Medicine and Biology, 2009,54(23): 7063-7075.
[6] AMEYA A, KATHLEEN V, KRISTOF B,et al. Evaluation of different MRIbased anatomical priors for PET brain imaging[C]∥ Nuclear Science Symposium Conference Record NSS/MIC), 2009 IEEE. Orlando, FL:IEEE, 2009: 2774-2780.
[7] COBELLI C, FOSTER D, TOFFOLO G. Tracer kinetics in biomedical research [M]. New York :Kluwer Academic/Plenum Publishers, 2000.
[8] LIU H, TIAN Y, SHI P. PET image reconstruction: a robust state space approach [C]∥ Information Processing in Medical Imaging IPMI’05). [S. l.]: Springer, 2005: 197-209.
[9] HOETJES N J, VAN VELDEN P H P, HOEKSTRA O S, et al. Partial volume correction strategies for quantitative FDG PET in oncology[J]. European Journal of Nuclear Medicine and Molecular Imaging, 2010, 37(9): 1679-1687.
[10] VAN LEEMPUT K, MAES F, VANDERMEULEN D, et al. Automated modelbased tissue classication of MR images of the brain[J]. IEEE Transactions on Medical Imaging, 1999, 18(10): 1162-1175.
[11] TAI Y C, LIN K P, HOH C K, et al. Utilization of 3D elastic transformation in the registration of chest xray CT and whole body PET [J]. IEEE Transactions on Nuclear Science, 1997, 44(4): 1606-1612.
[12] TONG S, SHI P. Tracer kinetics guided dynamic PET reconstruction [C]∥ Information Processing in Medical Imaging IPMI’07. [S. l.]: Springer, 2007, 20: 421-33.
[13] SIMON D. Optimal state estimation: kalman, and nonlinear approaches [M]. Hoboken, New Jersey: Wiley, 2006.
[14] SUN W, NAGPAL K M, KHARGONEKAR P P. control and ltering for sampleddata systems [J]. IEEE Transactions on Automatic Control, 1993, 38(8): 1162-1175.
[15] MUZIC R F, CORNELIUS S, COMKAT: Compartment model kinetic analysis tool [J]. The Journal of Nuclear Medicine, 2001, 42(4): 636-645.
[16] WONG K P, FENG D, MEIKLE S, et al. Simultaneous estimation of physiological parameters and the input function in vivo PET data [J]. IEEE Transactions on Information Technology in Biomedicine, 2001, 5(1): 67-76.

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