MAXIMUM A POSTERIORI RECONSTRUCTION OF PATLAK PARAMETRIC IMAGE FROM SINOGRAMS IN DYNAMIC PET

Parametric imaging using Patlak graphical method has been widely used to analyze dynamic PET data. The conventional way to generate Patlak parametric image is to reconstruct dynamic images first and then perform Patlak graphical analysis on the time activity curves pixel-by-pixel. In this paper we present a Bayesian method for reconstructing Patlak parametric images directly from raw sinogram data by combining the Patlak plot model with image reconstruction. A preconditioned conjugate gradient algorithm is used to find the maximum a posteriori solution. We conduct computer simulations to validate the proposed method. The comparison with conventional indirect approaches shows that the proposed method results in more accurate estimate of the parametric image

[1]  C S Patlak,et al.  Graphical Evaluation of Blood-to-Brain Transfer Constants from Multiple-Time Uptake Data , 1983, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[2]  Donald L. Snyder,et al.  Parameter Estimation for Dynamic Studies in Emission-Tomography Systems Having List-Mode Data , 1984, IEEE Transactions on Nuclear Science.

[3]  Donald L. Snyder,et al.  A Preliminary Evaluation of the Use of the EM Algorithm for Estimating Parameters in Dynamic Tracer-Studies , 1985, IEEE Transactions on Nuclear Science.

[4]  Richard E. Carson,et al.  Comment: The EM Parametric Image Reconstruction Algorithm , 1985 .

[5]  Richard E. Carson,et al.  The EM parametric image reconstruction algorithm , 1985 .

[6]  C. Patlak,et al.  Graphical Evaluation of Blood-to-Brain Transfer Constants from Multiple-Time Uptake Data. Generalizations , 1985, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[7]  M. Phelps,et al.  Simple noninvasive quantification method for measuring myocardial glucose utilization in humans employing positron emission tomography and fluorine-18 deoxyglucose. , 1989, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[8]  M. Phelps,et al.  Parametric images of myocardial metabolic rate of glucose generated from dynamic cardiac PET and 2-[18F]fluoro-2-deoxy-d-glucose studies. , 1991, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[9]  M. Phelps,et al.  A simplified method for quantification of myocardial blood flow using nitrogen-13-ammonia and dynamic PET. , 1993, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[10]  Alfred O. Hero,et al.  Model-based estimation for dynamic cardiac studies using ECT , 1994, IEEE Trans. Medical Imaging.

[11]  J. Borwein,et al.  Direct reconstruction of functional parameters for dynamic SPECT , 1995 .

[12]  G. Zeng,et al.  Using linear time-invariant system theory to estimate kinetic parameters directly from projection measurements , 1995 .

[13]  David Dagan Feng,et al.  An evaluation of the algorithms for determining local cerebral metabolic rates of glucose using positron emission tomography dynamic data , 1995, IEEE Trans. Medical Imaging.

[14]  Wan-Chi Siu,et al.  A technique for extracting physiological parameters and the required input function simultaneously from PET image measurements: theory and simulation study , 1997, IEEE Transactions on Information Technology in Biomedicine.

[15]  D. Bailey,et al.  The direct calculation of parametric images from dynamic PET data using maximum-likelihood iterative reconstruction. , 1997 .

[16]  G.T. Gullberg,et al.  Kinetic parameter estimation from attenuated SPECT projection measurements , 1997, 1997 IEEE Nuclear Science Symposium Conference Record.

[17]  R. P. Maguire,et al.  An investigation of multiple time/graphical analysis applied to projection data: theory and validation. , 1997, Journal of computer assisted tomography.

[18]  R. Leahy,et al.  High-resolution 3D Bayesian image reconstruction using the microPET small-animal scanner. , 1998, Physics in medicine and biology.

[19]  G A Ojemann,et al.  Glucose metabolism in human malignant gliomas measured quantitatively with PET, 1-[C-11]glucose and FDG: analysis of the FDG lumped constant. , 1998, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[20]  Jeffrey A. Fessler,et al.  Statistical image reconstruction methods for randoms-precorrected PET scans , 1998, Medical Image Anal..

[21]  T. Jones,et al.  Parametric image reconstruction using spectral analysis of PET projection data. , 1998, Physics in medicine and biology.

[22]  J. Bloch,et al.  Neurodegeneration prevented by lentiviral vector delivery of GDNF in primate models of Parkinson's disease. , 2000, Science.

[23]  F. Turkheimer,et al.  Kinetic modeling in positron emission tomography. , 2002, The quarterly journal of nuclear medicine : official publication of the Italian Association of Nuclear Medicine (AIMN) [and] the International Association of Radiopharmacology.

[24]  Richard M. Leahy,et al.  Spatiotemporal reconstruction of list-mode PET data , 2002, IEEE Transactions on Medical Imaging.

[25]  D. Brooks,et al.  Direct brain infusion of glial cell line–derived neurotrophic factor in Parkinson disease , 2003, Nature Medicine.

[26]  Andreas Robert Formiconi,et al.  Kinetic parameter estimation from renal measurements with a three-headed SPECT system: a Simulation study , 2004, IEEE Transactions on Medical Imaging.

[27]  Ken D. Sauer,et al.  Direct reconstruction of kinetic parameter images from dynamic PET data , 2005, IEEE Transactions on Medical Imaging.

[28]  I. Buvat,et al.  Iterative Kinetic Parameter Estimation within Fully 4D PET Image Reconstruction , 2006, 2006 IEEE Nuclear Science Symposium Conference Record.

[29]  Guobao Wang,et al.  Spatially Penalized Methods for Linear Parametric Imaging in Dynamic PET , 2006, 2006 IEEE Nuclear Science Symposium Conference Record.