Artificial Evolution for 3D PET Reconstruction

  • Franck P. Vidal
  • Delphine Lazaro-Ponthus
  • Samuel Legoupil
  • Jean Louchet
  • Évelyne Lutton
  • Jean-Marie Rocchisani
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5975)


This paper presents a method to take advantage of artificial evolution in positron emission tomography reconstruction. This imaging technique produces datasets that correspond to the concentration of positron emitters through the patient. Fully 3D tomographic reconstruction requires high computing power and leads to many challenges. Our aim is to reduce the computing cost and produce datasets while retaining the required quality. Our method is based on a coevolution strategy (also called Parisian evolution) named “fly algorithm”. Each fly represents a point of the space and acts as a positron emitter. The final population of flies corresponds to the reconstructed data. Using “marginal evaluation”, the fly’s fitness is the positive or negative contribution of this fly to the performance of the population. This is also used to skip the relatively costly step of selection and simplify the evolutionary algorithm.


Positron Emission Tomography Positron Emission Tomography Imaging Compton Scattering Bright Area Tomographic Reconstruction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Franck P. Vidal
    • 1
    • 2
  • Delphine Lazaro-Ponthus
    • 2
  • Samuel Legoupil
    • 2
  • Jean Louchet
    • 1
    • 3
  • Évelyne Lutton
    • 1
  • Jean-Marie Rocchisani
    • 1
    • 4
  1. 1.INRIA Saclay - Île-de-France/APISParc Orsay UniversitéOrsay CedexFrance
  2. 2.CEALIST, SaclayGif-sur-YvetteFrance
  3. 3.ArteniaChâtillonFrance
  4. 4.UFR SMBH & Avicenne hospitalParis XIII UniversityBobignyFrance

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