Pixel-based partial volume correction of small animal PET images using Point Spread Function system characterization: Evaluation of effects on cardiac output, perfusion and metabolic rate using parametric images

Quantitative analysis of positron emission tomography (PET) dynamic images allows to estimate physiological parameters such as glucose metabolic rate (GMR), perfusion, and cardiac output (CO). However, several physical effects such as photon attenuation, scatter and partial volume can reduce the accuracy of parameter estimation. The main goal of this work was to improve small animal PET image quality by introducing system point spread function (PSF) in the reconstruction scheme and to evaluate the effect of partial volume correction (PVC) on physiological parameter estimation. Images reconstructed respectively using constant and spatially variant (SV) PSFs and no PSF modeling was compared. The proposed algorithms were tested on simulated and real phantoms and mice images. Results show that the SV-PSF-based reconstruction method provides a significant contrast improvement of small animals PET cardiac images and, thus, the effects of PVC on physiological parameters were evaluated using such algorithm. Simulations show that the proposed PVC method reduces errors with respect to the true values for parametric images of GMR and perfusion. A reduction of CO percentage error with respect to the original value was also obtained using the SF-PSF approach. In conclusion, SV-PSF reconstruction method provides a more accurate estimation of several physiological parameters obtained from a dynamic PET scan.

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Assessment of myocardial metabolic rate of glucose by means of Bayesian ICA and Markov Chain Monte Carlo methods in small animal PET imaging

Polish Journal of Medical Physics and Engineering ◽

10.1515/pjmpe-2016-0012 ◽

2016 ◽

Vol 22 (3) ◽

pp. 69-75 ◽

Cited By ~ 2

Author(s):

Khadidja Berradja ◽

Nabil Boughanmi

Keyword(s):

Monte Carlo ◽

Markov Chain ◽

Markov Chain Monte Carlo ◽

Metabolic Rate ◽

Small Animal ◽

Region Of Interest ◽

Input Function ◽

Small Animal Pet ◽

Animal Pet ◽

Significant Difference

Abstract In dynamic cardiac PET FDG studies the assessment of myocardial metabolic rate of glucose (MMRG) requires the knowledge of the blood input function (IF). IF can be obtained by manual or automatic blood sampling and cross calibrated with PET. These procedures are cumbersome, invasive and generate uncertainties. The IF is contaminated by spillover of radioactivity from the adjacent myocardium and this could cause important error in the estimated MMRG. In this study, we show that the IF can be extracted from the images in a rat heart study with 18F-fluorodeoxyglucose (18F-FDG) by means of Independent Component Analysis (ICA) based on Bayesian theory and Markov Chain Monte Carlo (MCMC) sampling method (BICA). Images of the heart from rats were acquired with the Sherbrooke small animal PET scanner. A region of interest (ROI) was drawn around the rat image and decomposed into blood and tissue using BICA. The Statistical study showed that there is a significant difference (p < 0.05) between MMRG obtained with IF extracted by BICA with respect to IF extracted from measured images corrupted with spillover.

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