Image segmentation and activity estimation for microPET 11C-raclopride images using an expectation-maximum algorithm with a mixture of Poisson distributions

The objective of this study was to use a mixture of Poisson (MOP) model expectation maximum (EM) algorithm for segmenting microPET images. Simulated rat phantoms with partial volume effect and different noise levels were generated to evaluate the performance of the method. The partial volume correction was performed using an EM deblurring method before the segmentation. The EM-MOP outperforms the EM-MOP in terms of the estimated spatial accuracy, quantitative accuracy, robustness and computing efficiency. To conclude, the proposed EM-MOP method is a reliable and accurate approach for estimating uptake levels and spatial distributions across target tissues in microPET (11)C-raclopride imaging studies.

[1]  Adriaan A. Lammertsma,et al.  Effects of ROI definition and reconstruction method on quantitative outcome and applicability in a response monitoring trial , 2005, European Journal of Nuclear Medicine and Molecular Imaging.

[2]  D Visvikis,et al.  Fuzzy hidden Markov chains segmentation for volume determination and quantitation in PET. , 2007, Physics in medicine and biology.

[3]  Robert J. Elliott,et al.  Robust EM algorithms for Markov modulated Poisson processes , 2000, Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187).

[4]  H. Akaike.  A Bayesian analysis of the minimum AIC procedure , 1978 .

[5]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[6]  William H. Richardson,et al.  Bayesian-Based Iterative Method of Image Restoration , 1972 .

[7]  Finbarr O'Sullivan,et al.  An Analysis of the Role of Positivity and Mixture Model Constraints in Poisson Deconvolution Problems , 2001 .

[8]  D. Karlis.  An EM algorithm for multivariate Poisson distribution and related models , 2003 .

[9]  I. Buvat,et al.  Comparative Assessment of Methods for Estimating Tumor Volume and Standardized Uptake Value in 18F-FDG PET , 2010, Journal of Nuclear Medicine.

[10]  John A. D. Aston,et al.  MR Image Segmentation Using a Power Transformation Approach , 2009, IEEE Transactions on Medical Imaging.

[11]  Zhengrong Liang,et al.  Parameter estimation and tissue segmentation from multispectral MR images , 1994, IEEE Trans. Medical Imaging.

[12]  Habib Zaidi,et al.  Comparative assessment of statistical brain MR image segmentation algorithms and their impact on partial volume correction in PET , 2006, NeuroImage.

[13]  T. Rydén.  An EM algorithm for estimation in Markov-modulated Poisson processes , 1996 .

[14]  Miao Qi,et al.  A Modified FCM Algorithm for MRI Brain Image Segmentation , 2008, 2008 International Seminar on Future BioMedical Information Engineering.

[15]  U. Ruotsalainen,et al.  Automatic Extraction of Caudate and Putamen in [$^11$C] Raclopride PET Using Deformable Surface Models and Normalized Cuts , 2006, IEEE Transactions on Nuclear Science.

[16]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[17]  B. C. Penney,et al.  A Gaussian mixture model for definition of lung tumor volumes in positron emission tomography. , 2007, Medical physics.

[18]  L. Lucy.  An iterative technique for the rectification of observed distributions , 1974 .

[19]  S M Larson,et al.  Segmentation of lung lesion volume by adaptive positron emission tomography image thresholding , 1997, Cancer.

[20]  R. Jaszczak,et al.  Parameter estimation of finite mixtures using the EM algorithm and information criteria with application to medical image processing , 1992 .

[21]  C. Honey,et al.  PET imaging of implanted human retinal pigment epithelial cells in the MPTP-induced primate model of Parkinson's disease , 2004, Experimental Neurology.

[22]  Stephen M. Smith,et al.  Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm , 2001, IEEE Transactions on Medical Imaging.

[23]  Yu-Te Wu,et al.  Classification of spatiotemporal hemodynamics from brain perfusion MR images using expectation‐maximization estimation with finite mixture of multivariate gaussian distributions , 2007, Magnetic resonance in medicine.

[24]  Finbarr O'Sullivan,et al.  Locally constrained mixture representation of dynamic imaging data from PET and MR studies. , 2006, Biostatistics.

[25]  Zhengrong Liang,et al.  An EM Approach to MAP Solution of Segmenting Tissue Mixtures: A Numerical Analysis , 2009, IEEE Transactions on Medical Imaging.

[26]  Michael V. Green,et al.  Persistent Dopamine Functions of Neurons Derived from Embryonic Stem Cells in a Rodent Model of Parkinson Disease , 2007, Stem cells.

[27]  Inderjit S. Dhillon,et al.  Weighted Graph Cuts without Eigenvectors A Multilevel Approach , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  Hongbin Wang,et al.  Automated MAP-MRF EM labelling for volume determination in PET , 2008, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[29]  G. Schwarz.  Estimating the Dimension of a Model , 1978 .

[30]  Jinming Zhang,et al.  In-vivo PET imaging of implanted human retinal pigment epithelium cells in a Parkinson's disease rat model , 2008, Nuclear medicine communications.

[31]  V Kaasinen,et al.  Upregulation of putaminal dopamine D2 receptors in early Parkinson's disease: a comparative PET study with [11C] raclopride and [11C]N-methylspiperone. , 2000, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[32]  Christian Roux,et al.  A Fuzzy Locally Adaptive Bayesian Segmentation Approach for Volume Determination in PET , 2009, IEEE Transactions on Medical Imaging.

[33]  Rafael Castro-Linares,et al.  Trajectory tracking for non-holonomic cars: A linear approach to controlled leader-follower formation , 2010, 49th IEEE Conference on Decision and Control (CDC).

[34]  Natasa Kovacevic,et al.  A Robust Method for Extraction and Automatic Segmentation of Brain Images , 2002, NeuroImage.