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

• Autorzy: Chen Zhuo , An Lingling , Kao Chien-Min , Huang Hsin-Hsiung

Identyfikatory

DOI

10.5604/01.3001.0054.1807

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

error bounds; imaging reconstruction; positronium lifetime decay

• Wydawca: Uniwersytet Jagielloński - Collegium Medicum ; Index Copernicus Sp. z o.o.
• Czasopismo: Bio-Algorithms and Med-Systems
• Rocznik: 2023
• Tom: Vol. 19, no. 1
• Strony: 1--8
• Opis fizyczny: Bibliogr. 17 poz., rys.

Twórcy

autor

Chen Zhuo

• Department of Mathematics, University of Arizona, Tucson, USA

• https://orcid.org/0009-0003-8847-1244

autor

An Lingling

• Department of Agricultural and Biosystems Engineering, University of Arizona, Tucson, USA

• https://orcid.org/0000-0001-8273-0776

autor

Kao Chien-Min

• Department of Radiology, University of Chicago, Chicago, USA

• https://orcid.org/0000-0002-8785-5225

autor

Huang Hsin-Hsiung

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

• https://orcid.org/0000-0001-7150-7229

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).