# 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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