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international_conferences-abstracts.bib

@article{Vidal2011MedPhys,
  author = {F. P. Vidal and M. Folkerts and N. Freud and S. Jiang},
  title = {{GPU} Accelerated {DRR} Computation with Scatter},
  journal = {Medical Physics},
  year = 2011,
  volume = 38,
  pages = {3455-3456},
  number = 6,
  month = jul,
  address = {Vancouver, Canada},
  annotation = {AAPM Annual Meeting, Jul~31--Aug~4, 2011},
  abstract = {Purpose: We propose a fast software library implemented on 
    graphics processing unit (GPU) to compute digitally reconstructed 
    radiographs (DRRs). It takes into account first order Compton 
    scattering. 
    Methods: The simulation is based on the evaluation of 
    the Beer-Lambert law and of the Klein-Nishina equation. The 
    algorithm is fully determinist and has been fully implemented on 
    GPU to achieve clinically acceptable efficiency. A full resolution 
    simulation is performed for primary radiation. A much lower image 
    resolution is used for Compton scattering as it adds a low frequency 
    pattern over the projection image. Each voxel of the CT dataset is 
    considered as a secondary source. The number of photons that reach 
    each voxel is evaluated. Then, for each secondary source, a projection 
    image is computed and integrated in the final image. The photon energy 
    between each secondary source and each pixel is also computed. 
    An interlaced sampling mode is also proposed to further reduce 
    the computation time without sacrificing numerical accuracy. Finally, 
    the speed and accuracy are assessed.
    Results: We show that the computations can be fully implemented on 
    the GPU with an original under-sampling method to produce clinically 
    acceptable results. For example, a simulation can be achieved in less 
    than 7 seconds whilst the maximum relative error remains below 5\% and
    the average relative error below 1.4\%. At full resolution, a speed-up 
    by factor ~12X is achieved for the GPU implementation with our 
    interlaced-mode by comparison with our multi-threaded CPU implementation 
    using 8 threads in parallel. 
    Conclusions: DRR calculation with scatter is 
    computationally intensive. The use of GPU can achieve clinically 
    acceptable efficiency. A Compton fluence map can be computed in a few 
    seconds using under-sampling, whilst keeping numerical inaccuracies 
    relatively low. This work can be used for CBCT reconstruction to reduce 
    scatter artifacts.},
  doi = {10.1118/1.3611828},
  publisher = {American Association of Physicists in Medicine},
  pdf = {pdf/Vidal2011MedPhys.pdf}
}

@article{Vidal2010MedPhys-A,
  author = {F. P. Vidal and J. Louchet and {J.-M.} Rocchisani and \'E. Lutton},
  title = {Flies for {PET}: An Artificial Evolution Strategy for Image 
    Reconstruction in Nuclear Medicine},
  journal = {Medical Physics},
  year = 2010,
  volume = 37,
  pages = {3139},
  number = 6,
  month = jul,
  address = {Philadelphia, Pensilvania, USA},
  annotation = {AAPM Annual Meeting, Jul~18--22, 2010},
  abstract = {Purpose: We propose an evolutionary approach for image 
    reconstruction in nuclear medicine. Our method is based on 
    a cooperative coevolution strategy (also called Parisian evolution): 
    the ``fly algorithm''. 
    Method and Materials: Each individual, or fly, 
    corresponds to a 3D point that mimics a radioactive emitter, i.e. 
    a stochastic simulation of annihilation events is performed to compute 
    the fly's illumination pattern. For each annihilation, a photon is 
    emitted in a random direction, and a second photon is emitted in 
    the opposite direction. The line between two detected photons is 
    called line of response (LOR). If both photons are detected by 
    the scanner, the fly's illumination pattern is updated. 
    The LORs of every fly are aggregated to form the population total 
    illumination pattern. Using genetic operations to optimize the position 
    of positrons, the population of flies evolves so that the population 
    total pattern matches measured data. The final population of flies 
    approximates the radioactivity concentration. 
    Results: We have developed numerical phantom models to assess 
    the reconstruction algorithm. To date, no scattering and no tissue
    attenuation have been considered. Whilst this is not physically correct, it
    allows us to test and validate our approach in the simplest cases. 
    Preliminary results show the validity of this approach in both 2D and 
    fully-3D modes. In particular, the size of objects, and 
    their relative concentrations can be retrieved in the 2D mode. 
    In fully-3D, complex shapes can be reconstructed. 
    Conclusions: An evolutionary approach for PET reconstruction has been
    proposed and validated using simple test cases. Further work will
    therefore include the use of more realistic input data (including random
    events and scattering), which will finally lead to implement the
    correction of scattering within our algorithm. 
    A comparison study against ML-EM and/or OS-EM methods will also need 
    to be conducted.},
  doi = {10.1118/1.3468200},
  publisher = {American Association of Physicists in Medicine},
  pdf = {pdf/Vidal2010MedPhys-A.pdf}
}

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