Abstract
The reconstruction of positron emission tomography data is a difficult task, particularly at low count rates because Poisson noise has a significant influence on the statistical uncertainty of positron emission tomography (PET) measurements. Prior information is frequently used to improve image quality. In this paper, we propose the use of a field of experts to model a priori structure and capture anatomical spatial dependencies of the PET images to address the problems of noise and low count data, which make the reconstruction of the image difficult. We reconstruct PET images by using a modified MXE algorithm, which minimizes a objective function with the cross-entropy as a fidelity term, while the field of expert model is incorporated as a regularizing term. Comparisons with the expectation maximization algorithm and a iterative method with a prior penalizing relative differences showed that the proposed method can lead to accurate estimation of the image, especially with acquisitions at low count rate.
Highlights
Positron emission tomography is an imaging technology that provides quantitative studies to detect, diagnose, and monitor treatment of different diseases such as hypernated myocardium, cancer, and many others [1,2].The scan process begins by administering a radioactive substance, the radiotracer, to the patient.The substance is absorbed mainly by the target organs or tissues
A novel reconstruction method for positron emission tomography (PET) images is presented based on a cross-entropy fidelity term
We propose to regularize the ill-posed problem by using field of experts priors
Summary
Positron emission tomography is an imaging technology that provides quantitative studies to detect, diagnose, and monitor treatment of different diseases such as hypernated myocardium, cancer, and many others [1,2]. The positron emission tomography (PET) data are obtained by detecting the radiotracer distribution within the body. These acquired data are processed by reconstruction algorithms to obtain the final image, which is presented to the medical research personnel [2,3]. Iterative methods are popular in PET due to their robustness and ability to incorporate prior data and noise statistics. Iterative methods are mainly based on the maximum likelihood expectation maximization estimator or the least squares model. In this paper a novel approach to reconstruct PET images is presented. We expect that the FoE prior helps to recover the anatomical structure and capture anatomical spatial dependencies of the PET images during the reconstruction process.
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