Artificial Evolution for 3D PET Reconstruction
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Vidal:2009:EA,
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author = "Franck P. Vidal and Delphine Lazaro-Ponthus and
Samuel Legoupil and Jean Louchet and Evelyne Lutton and
Jean-Marie Rocchisani",
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title = "Artificial Evolution for {3D PET} Reconstruction",
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booktitle = "Artificial Evolution, EA 2009",
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year = "2009",
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editor = "Pierre Collet and Nicolas Monmarche and
Pierrick Legrand and Marc Schoenauer and Evelyne Lutton",
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volume = "5975",
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series = "Lecture Notes in Computer Science,",
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pages = "37--48",
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address = "Strasbourg, France",
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month = "26-28 " # oct,
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming, Positron
Emission Tomography, Positron Emission Tomography
Imaging, Compton Scattering, Bright Area, Tomographic
Reconstruction",
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isbn13 = "978-3-642-14155-3",
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DOI = "doi:10.1007/978-3-642-14156-0_4",
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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