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New Genetic Operators in the Fly Algorithm: Application to Medical PET Image Reconstruction

  • Franck Patrick Vidal
  • Jean Louchet
  • Jean-Marie Rocchisani
  • Évelyne Lutton
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6024)

Abstract

This paper presents an evolutionary approach for image reconstruction in positron emission tomography (PET). Our reconstruction method is based on a cooperative coevolution strategy (also called Parisian evolution): the “fly algorithm”. Each fly is a 3D point that mimics a positron emitter. The flies’ position is progressively optimised using evolutionary computing to closely match the data measured by the imaging system. The performance of each fly is assessed using a “marginal evaluation” based on the positive or negative contribution of this fly to the performance of the population. Using this property, we propose a “thresholded-selection” method to replace the classical tournament method. A mitosis operator is also proposed. It is triggered to automatically increase the population size when the number of flies with negative fitness becomes too low.

Keywords

Positron Emission Tomography Genetic Operator Radioactivity Concentration Phantom Model Positron Emission Tomography System 
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 Patrick Vidal
    • 1
  • Jean Louchet
    • 2
  • Jean-Marie Rocchisani
    • 1
    • 3
  • Évelyne Lutton
    • 1
  1. 1.INRIA Saclay - Île-de-France/APISOrsay CedexFrance
  2. 2.ArteniaChâtillonFrance
  3. 3.Paris XIII University, UFR SMBH & Avicenne hospitalBobignyFrance

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