Artificial Evolution for 3D PET Reconstruction

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

@InProceedings{Vidal:2009:EA,
  author =       "Franck P. Vidal and Delphine Lazaro-Ponthus and 
                 Samuel Legoupil and Jean Louchet and Evelyne Lutton and 
                 Jean-Marie Rocchisani",
  title =        "Artificial Evolution for {3D PET} Reconstruction",
  booktitle =    "Artificial Evolution, EA 2009",
  year =         "2009",
  editor =       "Pierre Collet and Nicolas Monmarche and 
                 Pierrick Legrand and Marc Schoenauer and Evelyne Lutton",
  volume =       "5975",
  series =       "Lecture Notes in Computer Science,",
  pages =        "37--48",
  address =      "Strasbourg, France",
  month =        "26-28 " # oct,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, Positron
                 Emission Tomography, Positron Emission Tomography
                 Imaging, Compton Scattering, Bright Area, Tomographic
                 Reconstruction",
  isbn13 =       "978-3-642-14155-3",
  DOI =          "doi:10.1007/978-3-642-14156-0_4",
  abstract =     "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.",
}

Genetic Programming entries for Franck P Vidal Delphine Lazaro-Ponthus Samuel Legoupil Jean Louchet Evelyne Lutton Jean-Marie Rocchisani

Citations