international_conferences-other_presentations.bib

@inproceedings{AliAbbood2015VCBM,
  booktitle = {Eurographics Workshop on Visual Computing for Biology and Medicine},
  editor = {Katja B\"uhler and Lars Linsen and Nigel W. John},
  title = {{Visualisation of PET data in the Fly Algorithm}},
  author = {{Ali Abbood}, Zainab  and Rocchisani, Jean-Marie and Vidal, Franck P.},
  year = {2015},
  pages = {211--212},
  publisher = {The Eurographics Association},
  issn = {2070-5786},
  isbn = {978-3-905674-82-8},
  doi = {10.2312/vcbm.20151227},
  abstract = {We use the Fly algorithm, an artificial evolution strategy, 
        to reconstruct positron emission tomography (PET) images. The algorithm iteratively 
        optimises the position of 3D points. It eventually produces a point cloud, which needs 
        to be voxelised to produce volume data that can be used with conventional medical image 
        software. However, resulting voxel data is noisy. In our test case with 6,400 points 
        the normalised cross-correlation (NCC) between the reference and the reconstruction is 
        85.53\%; with 25,600 points it is 93.60\%. This paper introduces a more robust 
        3D voxelisation method based on implicit modelling using metaballs to overcome 
        this limitation. With metaballs, the NCC with 6,400 points increases up to 92.21\%; 
        and up to 96.26\% with 25,600 points.},
  pdf = {pdf/AliAbbood2015VCBM.pdf}
}

@inproceedings{Vidal2009MIC,
  author = {F. P. Vidal and J. Louchet and \'E. Lutton and {J.-M.} Rocchisani},
  title = {{PET} Reconstruction Using a Cooperative Coevolution Strategy in 
    {LOR} Space},
  booktitle = {IEEE Nuclear Science Symposium Conference Record},
  year = 2009,
  pages = {3363-3366},
  month = oct,
  address = {Orlando, Florida},
  annotation = {Oct~25--31, 2012},
  abstract = {This paper presents preliminary results of a novel method that
    takes advantage of artificial evolution for positron emission tomography
    (PET) reconstruction. Fully 3D tomographic reconstruction in PET requires
    high computing power and leads to many challenges. To date, the use of such
    methods is still restricted due to the heavy computing power needed.
    Evolutionary algorithms have proven to be efficient optimisation techniques
    in various domains. However the use of evolutionary computation in
    tomographic reconstruction has been largely overlooked. We propose a
    computer-based algorithm for fully 3D reconstruction in PET based on
    artificial evolution and evaluate its relevance.},
  keywords = {Positron emission tomography, genetic algorithms, 
    optimization methods},
  doi = {10.1109/NSSMIC.2009.5401758},
  publisher = {IEEE}
}

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