Presentations at 2018 IEEE Medical Imaging Conference

Eight works by our team and collaborators (2 oral and 6 posters) were recently presented at the 2018 IEEE Medical Imaging Conference in Sydney, November 14-17:

• M. R. Salmanpour, M. Shamsaee, A. Saberi Manesh, S. Setayeshi, E. Taherinezhad, I. S. Klyuzhin, J. Tang, V. Sossi, and A. Rahmim
  Machine learning methods for optimal prediction of outcome in Parkinson’s disease
• K. H. Leung, M. R. Salmanpour, A. S. Manesh, I. S. Klyuzhin, V. Sossi, A. K. Jha, M. G. Pomper, Y. Du, and A. Rahmim
  Using deep-learning to predict outcome of patients with Parkinson’s disease
• Y. Gao, H. Zhang, Y. Zhu, M. Bilgel, O. Rousset, S. Resnick, D. F. Wong, L. Lu, and A. Rahmim
  Voxel-based partial volume correction of amyloid PET images incorporating non-local means regularization
• I. Shiri, H. Maleki, G. Hajianfar, H. Abdollahi, S. Ashrafinia, M. Ghelich Oghli, M. Oveisi, and A. Rahmim
  PET/CT radiomic sequencer for prediction of EGFR and KRAS mutation status in NSCLC patients
• M. P. Adams, B. Yang, A. Rahmim, and J. Tang
  Prediction of outcome in Parkinson’s disease patients from DAT SPECT images using a convolutional neural network
• J. -C. Cheng, C. W. J. Bevington, A. Rahmim, I. S. Klyuzhin, J. Matthews, R. Boellaard, V. and Sossi
  Dynamic PET reconstruction utilizing a spatiotemporal 4D de-noising kernel
• H. Li, L. Lu, S. Cao, J. Gong, Q. Feng, A. Rahmim, and W. Chen
  Dual-modality joint reconstruction of PET-MRI incorporating a cross-guided prior
• M. A. Lodge, J. Sunderland, and A. Rahmim
  About measurement of PET spatial resolution