Maximum a posteriori reconstruction of the Patlak parametric image from sinograms in dynamic PET

Guobao Wang, Lin Fu, Jinyi Qi

Research output: Contribution to journalArticle

65 Citations (Scopus)

Abstract

Parametric imaging using the Patlak graphical method has been widely used to analyze dynamic PET data. Conventionally a Patlak parametric image is generated by reconstructing a sequence of dynamic images first and then performing Patlak graphical analysis on the time-activity curves pixel-by-pixel. However, because it is rather difficult to model the noise distribution in reconstructed images, the spatially variant noise correlation is simply ignored in the Patlak analysis, which leads to sub-optimal results. In this paper we present a Bayesian method for reconstructing Patlak parametric images directly from raw sinogram data by incorporating the Patlak plot model into the image reconstruction procedure. A preconditioned conjugate gradient algorithm is used to find the maximum a posteriori solution. The proposed direct method is statistically more efficient than the conventional indirect approach because the Poisson noise distribution in PET data can be accurately modeled in the direct reconstruction. The computation cost of the direct method is similar to reconstruction time of two dynamic frames. Therefore, when more than two dynamic frames are used in the Patlak analysis, the direct method is faster than the conventional indirect approach. We conduct computer simulations to validate the proposed direct method. Comparisons with the conventional indirect approach show that the proposed method results in a more accurate estimate of the parametric image. The proposed method has been applied to dynamic fully 3D PET data from a microPET scanner

Original languageEnglish (US)
Pages (from-to)593-604
Number of pages12
JournalPhysics in Medicine and Biology
Volume53
Issue number3
DOIs
StatePublished - Feb 7 2008

Fingerprint

Noise
Pixels
pixels
image reconstruction
Image reconstruction
scanners
Poisson Distribution
computerized simulation
plots
Bayes Theorem
Computer-Assisted Image Processing
costs
Imaging techniques
gradients
Computer Simulation
Computer simulation
curves
estimates
Costs
Costs and Cost Analysis

ASJC Scopus subject areas

  • Biomedical Engineering
  • Physics and Astronomy (miscellaneous)
  • Radiology Nuclear Medicine and imaging
  • Radiological and Ultrasound Technology

Cite this

Maximum a posteriori reconstruction of the Patlak parametric image from sinograms in dynamic PET. / Wang, Guobao; Fu, Lin; Qi, Jinyi.

In: Physics in Medicine and Biology, Vol. 53, No. 3, 07.02.2008, p. 593-604.

Research output: Contribution to journalArticle

@article{54a808965b984ff59cae83ee02e98576,
title = "Maximum a posteriori reconstruction of the Patlak parametric image from sinograms in dynamic PET",
abstract = "Parametric imaging using the Patlak graphical method has been widely used to analyze dynamic PET data. Conventionally a Patlak parametric image is generated by reconstructing a sequence of dynamic images first and then performing Patlak graphical analysis on the time-activity curves pixel-by-pixel. However, because it is rather difficult to model the noise distribution in reconstructed images, the spatially variant noise correlation is simply ignored in the Patlak analysis, which leads to sub-optimal results. In this paper we present a Bayesian method for reconstructing Patlak parametric images directly from raw sinogram data by incorporating the Patlak plot model into the image reconstruction procedure. A preconditioned conjugate gradient algorithm is used to find the maximum a posteriori solution. The proposed direct method is statistically more efficient than the conventional indirect approach because the Poisson noise distribution in PET data can be accurately modeled in the direct reconstruction. The computation cost of the direct method is similar to reconstruction time of two dynamic frames. Therefore, when more than two dynamic frames are used in the Patlak analysis, the direct method is faster than the conventional indirect approach. We conduct computer simulations to validate the proposed direct method. Comparisons with the conventional indirect approach show that the proposed method results in a more accurate estimate of the parametric image. The proposed method has been applied to dynamic fully 3D PET data from a microPET scanner",
author = "Guobao Wang and Lin Fu and Jinyi Qi",
year = "2008",
month = "2",
day = "7",
doi = "10.1088/0031-9155/53/3/006",
language = "English (US)",
volume = "53",
pages = "593--604",
journal = "Physics in Medicine and Biology",
issn = "0031-9155",
publisher = "IOP Publishing Ltd.",
number = "3",

}

TY - JOUR

T1 - Maximum a posteriori reconstruction of the Patlak parametric image from sinograms in dynamic PET

AU - Wang, Guobao

AU - Fu, Lin

AU - Qi, Jinyi

PY - 2008/2/7

Y1 - 2008/2/7

N2 - Parametric imaging using the Patlak graphical method has been widely used to analyze dynamic PET data. Conventionally a Patlak parametric image is generated by reconstructing a sequence of dynamic images first and then performing Patlak graphical analysis on the time-activity curves pixel-by-pixel. However, because it is rather difficult to model the noise distribution in reconstructed images, the spatially variant noise correlation is simply ignored in the Patlak analysis, which leads to sub-optimal results. In this paper we present a Bayesian method for reconstructing Patlak parametric images directly from raw sinogram data by incorporating the Patlak plot model into the image reconstruction procedure. A preconditioned conjugate gradient algorithm is used to find the maximum a posteriori solution. The proposed direct method is statistically more efficient than the conventional indirect approach because the Poisson noise distribution in PET data can be accurately modeled in the direct reconstruction. The computation cost of the direct method is similar to reconstruction time of two dynamic frames. Therefore, when more than two dynamic frames are used in the Patlak analysis, the direct method is faster than the conventional indirect approach. We conduct computer simulations to validate the proposed direct method. Comparisons with the conventional indirect approach show that the proposed method results in a more accurate estimate of the parametric image. The proposed method has been applied to dynamic fully 3D PET data from a microPET scanner

AB - Parametric imaging using the Patlak graphical method has been widely used to analyze dynamic PET data. Conventionally a Patlak parametric image is generated by reconstructing a sequence of dynamic images first and then performing Patlak graphical analysis on the time-activity curves pixel-by-pixel. However, because it is rather difficult to model the noise distribution in reconstructed images, the spatially variant noise correlation is simply ignored in the Patlak analysis, which leads to sub-optimal results. In this paper we present a Bayesian method for reconstructing Patlak parametric images directly from raw sinogram data by incorporating the Patlak plot model into the image reconstruction procedure. A preconditioned conjugate gradient algorithm is used to find the maximum a posteriori solution. The proposed direct method is statistically more efficient than the conventional indirect approach because the Poisson noise distribution in PET data can be accurately modeled in the direct reconstruction. The computation cost of the direct method is similar to reconstruction time of two dynamic frames. Therefore, when more than two dynamic frames are used in the Patlak analysis, the direct method is faster than the conventional indirect approach. We conduct computer simulations to validate the proposed direct method. Comparisons with the conventional indirect approach show that the proposed method results in a more accurate estimate of the parametric image. The proposed method has been applied to dynamic fully 3D PET data from a microPET scanner

UR - http://www.scopus.com/inward/record.url?scp=41449101512&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=41449101512&partnerID=8YFLogxK

U2 - 10.1088/0031-9155/53/3/006

DO - 10.1088/0031-9155/53/3/006

M3 - Article

C2 - 18199904

AN - SCOPUS:41449101512

VL - 53

SP - 593

EP - 604

JO - Physics in Medicine and Biology

JF - Physics in Medicine and Biology

SN - 0031-9155

IS - 3

ER -