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Computing support for advanced medical data analysis and imaging

  • Wojciech Wiślicki EMAIL logo , Tomasz Bednarski , Piotr Białas , Eryk Czerwiński , Łukasz Kapłon , Andrzej Kochanowski , Grzegorz Korcyl , Jakub Kowal , Paweł Kowalski , Tomasz Kozik , Wojciech Krzemień , Marcin Molenda , Paweł Moskal , Szymon Niedźwiecki , Marek Pałka , Monika Pawlik-Niedźwiecka , Lech Raczyński , Zbigniew Rudy , Piotr Salabura , Neha Gupta Sharma , Michał Silarski , Artur Słomski , Jerzy Smyrski , Adam Strzelecki , Anna Wieczorek , Marcin Zieliński and Natalia Zoń
Published/Copyright: April 11, 2014
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Bio-Algorithms and Med-Systems
From the journal Bio-Algorithms and Med-Systems Volume 10 Issue 2

Abstract

We discuss computing issues for data analysis and image reconstruction of positron emission tomography based on time-of-flight medical scanner or other medical scanning devices producing large volumes of data. Service architecture based on grid and cloud concepts for distributed processing is proposed and critically discussed.

Keywords: high-performance computing; medical image reconstruction; positron emission tomography
Corresponding author: Wojciech Wiślicki, National Centre for Nuclear Research, 05-400 Świerk-Otwock, Poland, E-mail:

Acknowledgments

This work was financially supported by the EU and MSHE grant no. POIG.02.03.00-161 00-013/09, the Polish National Center for Development and Research through grant no. INNOTECH-K1/IN1/64/159174/NCBR/12, the Foundation for Polish Science through MPD Programme, and Doctus – the Lesser Poland PhD Scholarship Fund.

Conflict of interest statement

Authors’ conflict of interest disclosure: The authors stated that there are no conflicts of interest regarding the publication of this article. Research funding played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.

Research funding: None declared.

Employment or leadership: None declared.

Honorarium: None declared.

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Based on talk given by the first author at the Symposium on Positron Emission Tomography, September 19–22, 2013, Jagiellonian University, Kraków, Poland.

Received: 2014-1-26
Accepted: 2014-3-23
Published Online: 2014-4-11
Published in Print: 2014-6-30

©2014 by Walter de Gruyter Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Mini Review
  3. Computing support for advanced medical data analysis and imaging
  4. Computer Science Towards Medicines
  5. Application of WLS strips for position determination in strip PET tomograph based on plastic scintillators
  6. Depth of interaction determination with temperature gradient function in continuous bismuth germanate oxide (BGO) crystal
  7. Simulations of γ quanta scattering in a single module of the J-PET detector
  8. Database and data structure for the novel TOF-PET detector developed for the J-PET project
  9. Determination of the map of efficiency of the Jagiellonian Positron Emission Tomograph (J-PET) detector with the GATE package
  10. Speaker identification based on artificial neural networks. Case study: the Polish vowel (pilot study)
https://doi.org/10.1515/bams-2014-0001
Keywords for this article
high-performance computing; medical image reconstruction; positron emission tomography

Articles in the same Issue

  1. Frontmatter
  2. Mini Review
  3. Computing support for advanced medical data analysis and imaging
  4. Computer Science Towards Medicines
  5. Application of WLS strips for position determination in strip PET tomograph based on plastic scintillators
  6. Depth of interaction determination with temperature gradient function in continuous bismuth germanate oxide (BGO) crystal
  7. Simulations of γ quanta scattering in a single module of the J-PET detector
  8. Database and data structure for the novel TOF-PET detector developed for the J-PET project
  9. Determination of the map of efficiency of the Jagiellonian Positron Emission Tomograph (J-PET) detector with the GATE package
  10. Speaker identification based on artificial neural networks. Case study: the Polish vowel (pilot study)
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