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
One reply on “Presentations at 2018 IEEE Medical Imaging Conference”
Thank you very much for the news, it was very interesting and informative.