The properties of the positronium lifetime image reconstruction based on maximum likelihood estimation

Author:

Chen Zhuo1ORCID,An Lingling2ORCID,Kao Chien-Min3ORCID,Huang Hsin-Hsiung4ORCID

Affiliation:

1. Department of Mathematics, University of Arizona, Tucson, USA

2. Department of Agricultural and Biosystems Engineering, University of Arizona, Tucson, USA

3. Department of Radiology, University of Chicago, Chicago, USA

4. Department of Statistics and Data Science, University of Central Florida, Orlando, USA

Abstract

The positronium lifetime imaging (PLI) reconstruction is a technique used in time- -of-flight (TOF) positron emission tomography (PET) imaging that involves measuring the lifespan of positronium, which is a metastable electron-positron pair that arises when a PET molecule releases a positron, prior to its annihilation. We have previously developed a maximum likelihood (ML) algorithm for PLI reconstruction and demonstrated that it can generate quantitatively accurate lifetime images for a 570 ps (pico-seconds) TOF PET system. In this study, we conducted further investigations into the statistical properties of the algorithm, including the variability of the reconstruction results, the sensitivity of the algorithm to the number of acquired PLI events and its robustness to hyperparameter choices. Our findings indicate that the proposed ML method produces sufficiently stable lifetime images to enable reliable distinction of regions of interest. Moreover, the number of PLI events required to produce quantitatively accurate lifetime images is computationally plausible. These results demonstrate the potential of our ML algorithm for advancing the capabilities of TOF PET imaging.reliable distinction of regions of interest. Moreover, the number of PLI events required to produce quantitatively accurate lifetime images is computationally plausible. These results demonstrate the potential of our ML algorithm for advancing the capabilities of TOF PET imaging.

Publisher

Index Copernicus

Subject

Health Informatics,Biochemistry, Genetics and Molecular Biology (miscellaneous),Medicine (miscellaneous),General Computer Science

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