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Bio-Algorithms and Med-Systems

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3D PET image reconstruction based on the maximum likelihood estimation method (MLEM) algorithm

Artur Słomski
  • Corresponding author
  • Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Kraków, Poland
  • Email:
/ Zbigniew Rudy
  • Corresponding author
  • Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Kraków, Poland
  • Email:
/ Tomasz Bednarski
  • Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Kraków, Poland
/ Piotr Białas
  • Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Kraków, Poland
/ Eryk Czerwiński
  • Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Kraków, Poland
/ Łukasz Kapłon
  • Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Kraków, Poland
  • Faculty of Chemistry, Jagiellonian University, Kraków, Poland
/ Andrzej Kochanowski
  • Faculty of Chemistry, Jagiellonian University, Kraków, Poland
/ Grzegorz Korcyl
  • Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Kraków, Poland
/ Jakub Kowal
  • Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Kraków, Poland
/ Paweł Kowalski
  • Swierk Computing Centre, National Centre for Nuclear Research, Otwock-Swierk, Poland
/ Tomasz Kozik
  • Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Kraków, Poland
/ Wojciech Krzemień
  • Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Kraków, Poland
/ Marcin Molenda
  • Faculty of Chemistry, Jagiellonian University, Kraków, Poland
/ Paweł Moskal
  • Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Kraków, Poland
/ Szymon Niedźwiecki
  • Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Kraków, Poland
/ Marek Pałka
  • Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Kraków, Poland
/ Monika Pawlik
  • Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Kraków, Poland
/ Lech Raczyński
  • Swierk Computing Centre, National Centre for Nuclear Research, Otwock-Swierk, Poland
/ Piotr Salabura
  • Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Kraków, Poland
/ Neha Gupta-Sharma
  • Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Kraków, Poland
/ Michał Silarski
  • Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Kraków, Poland
/ Jerzy Smyrski
  • Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Kraków, Poland
/ Adam Strzelecki
  • Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Kraków, Poland
/ Wojciech Wiślicki
  • Swierk Computing Centre, National Centre for Nuclear Research, Otwock-Swierk, Poland
/ Marcin Zieliński
  • Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Kraków, Poland
/ Natalia Zoń
  • Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Kraków, Poland
Published Online: 2014-03-03 | DOI: https://doi.org/10.1515/bams-2013-0106

Abstract

A positron emission tomography (PET) scan does not measure an image directly. Instead, a PET scan measures a sinogram at the boundary of the field-of-view that consists of measurements of the sums of all the counts along the lines connecting the two detectors. Because there is a multitude of detectors built in a typical PET structure, there are many possible detector pairs that pertain to the measurement. The problem is how to turn this measurement into an image (this is called imaging). Significant improvement in PET image quality was achieved with the introduction of iterative reconstruction techniques. This was realized approximately 20 years ago (with the advent of new powerful computing processors). However, three-dimensional imaging still remains a challenge. The purpose of the image reconstruction algorithm is to process this imperfect count data for a large number (many millions) of lines of response and millions of detected photons to produce an image showing the distribution of the labeled molecules in space.

Keywords: image reconstruction; positron emission tomography

References

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About the article

Corresponding authors: Artur Słomski and Zbigniew Rudy, Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, 30-059 Kraków, Reymonta 4 Street, Poland, E-mail: ;


Received: 2013-10-28

Accepted: 2014-01-30

Published Online: 2014-03-03

Published in Print: 2014-03-31


Citation Information: Bio-Algorithms and Med-Systems, ISSN (Online) 1896-530X, ISSN (Print) 1895-9091, DOI: https://doi.org/10.1515/bams-2013-0106.

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[1]
A. Wieczorek, P. Moskal, S. Niedźwiecki, T. Bednarski, P. Białas, E. Czerwiński, A. Danel, A. Gajos, A. Gruntowski, D. Kamińska, Ł. Kapłon, A. Kochanowski, G. Korcyl, J. Kowal, P. Kowalski, T. Kozik, W. Krzemień, E. Kubicz, M. Molenda, M. Pałka, L. Raczyński, Z. Rudy, O. Rundel, P. Salabura, N.G. Sharma, M. Silarski, A. Słomski, J. Smyrski, A. Strzelecki, T. Uchacz, W. Wiślicki, M. Zieliński, and N. Zoń
Acta Physica Polonica A, 2015, Volume 127, Number 5, Page 1487

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