Fly4PET - Fly Algorithm in PET Reconstruction for Radiotherapy Treatment Planning


This project is focused on developing new software technologies for lung cancer treatment and it is based on accurate physical models implemented using high performance computing. Four research themes have been identified: improvement of our original reconstruction algorithm for Positron Emission Tomography (PET) imaging, fast respiration simulation, tumour segmentation and extraction, and interactive multi-modal visualisation. The clinical outputs that are expected will be improving the quantitative results in PET, assisting doctors to elaborate their treatment plans using both anatomical (CT) and biological (PET) information, helping to assess the response of tumours to the treatment, and improving of the validation of treatment plans by radiation oncologists.

This multi-disciplinary project is carried out by an alliance of external partners in computer science, medicine and medical physics from France, Belgium, California and Wales.

More information can be found at Fly4PET.

  1. F. P. Vidal, P.-F. Villard, and É. Lutton, “Tuning of Patient Specific Deformable Models using an Adaptive Evolutionary Optimization Strategy,” IEEE Transactions on Biomedical Engineering, vol. 59, no. 10, pp. 2942–2949, Oct. 2012. [bib]
  2. Z. Ali Abbood, J. Lavauzelle, É. Lutton, J.-M. Rocchisani, J. Louchet, and F. P. Vidal, “Voxelisation in the 3-D Fly Algorithm for PET,” Swarm and Evolutionary Computation, vol. x, pp. xx-xx, 2017, (To be published). [bib]
  3. F. P. Vidal, D. Lazaro-Ponthus, S. Legoupil, J. Louchet, É. Lutton, and J.-M. Rocchisani, “Artificial Evolution for 3D PET Reconstruction,” in Proceedings of the 9th international conference on Artificial Evolution (EA’09), Strasbourg, France, 2009, vol. 5975, pp. 37–48. [bib]
  4. F. P. Vidal, J. Louchet, J.-M. Rocchisani, and É. Lutton, “New genetic operators in the Fly algorithm: application to medical PET image reconstruction,” in Applications of Evolutionary Computation, Istanbul, Turkey, 2010, vol. 6024, pp. 292–301.(missing reference)
  5. F. P. Vidal, É. Lutton, J. Louchet, and J.-M. Rocchisani, “Threshold selection, mitosis and dual mutation in cooperative coevolution: application to medical 3D tomography,” in International Conference on Parallel Problem Solving From Nature (PPSN’10), Krakow, Poland, 2010, vol. 6238, pp. 414–423. [bib]
  6. P.-F. Villard, F. P. Vidal, F. Bello, and N. W. John, “A Method to Compute Respiration Parameters for Patient-based Simulators,” in Proceeding of Medicine Meets Virtual Reality 19 - NextMed (MMVR19), Newport Beach, California, 2012, vol. 173, pp. 529–533. [bib]
  7. F. P. Vidal, P.-F. Villard, and É. Lutton, “Automatic tuning of respiratory model for patient-based simulation,” in Proceeding of the International Conference on Medical Imaging Using Bio-inspired and Soft Computing (MIBISOC2013), Brussels, Belgium, 2013. [bib]
  8. F. P. Vidal, Y. L. Pavia, J.-M. Rocchisani, J. Louchet, and É. Lutton, “Artificial Evolution Strategy for PET Reconstruction,” in Proceeding of the International Conference on Medical Imaging Using Bio-inspired and Soft Computing (MIBISOC2013), Brussels, Belgium, 2013. [bib]