Presentations at 2018 SNMMI Annual Meeting

Thirteen accepted works by our group and collaborators (8 oral and 5 posters) are being presented at the 2018 Annual Meeting of the Society of Nuclear Medicine & Molecular Imaging (SNMMI) in Philadelphia June 23-26:

• A. Rahmim, K. P. Bak-Fredslund, S. Ashrafinia, C. R. Schmidtlein, R. M. Subramaniam, A. Morsing, S. Keiding, J. Horsager, and O. L. Munk
  Quantification of colorectal liver metastases using FDG PET volumetric and heterogeneity features for improved prediction of clinical outcome
• A. Rahmim, S. Ashrafinia, S. Rowe, C. R. Schmidtlein, M. H. Vendelbo, T. El-Galaly, L. C. Gormsen, and O. L. Munk
  Quantification of lymphoma using FDG PET heterogeneity features for improved prediction of clinical outcome
• S. Ashrafinia, P. Dalaie, R. Yan, P. Ghazi, C. Marcus, M. Taghipour, P. Huang, M. G. Pomper, T. Schindler, and A. Rahmim
  Radiomics analysis of clinical myocardial perfusion SPECT to predict coronary artery calcification
• S. Ashrafinia, P. Dalaie, R. Yan, P. Huang, Martin G. Pomper, T. Schindler, and A. Rahmim
  Application of texture and radiomics analysis to clinical myocardial perfusion SPECT imaging
• H. Leung, W. Marashdeh, S. Ashrafinia, A. Rahmim, M. G. Pomper, and A. K. Jha
  A deep-learning-based fully automated segmentation approach to delineate tumors in FDG PET images of lung cancer patients
• S. Klyuzhin, N. Shenkov, A. Rahmim, and V. Sossi
  Use of deep convolutional neural networks to predict Parkinson’s disease progression from DaTscan SPECT images
• D. Du, W. Lv, Q. Yuan, Q. Wang, Q. Feng, W. Chen, A. Rahmim, and L. Lu
  Machine learning methods for optimal differentiation of recurrence versus inflammation from post-therapy nasopharyngeal 18F-FDG PET/CT images
• X. Hong, W. Lv, Q. Yuan, Q. Wang, Q. Feng, W. Chen, A. Rahmim, and L. Lu
  Prediction of local recurrence and distant metastasis using radiomics analysis of pretreatment nasopharyngeal 18F-FDG PET/CT images
• Y. Gao, M. Bilgel, S. Ashrafinia, Lijun Lu, Olivier Rousset, Susan Resnick, Dean F. Wong, Arman Rahmim
  Evaluation of non-local methods with and without anatomy information for improved quantitative amyloid PET imaging
• A. Rahmim, M. A. Lodge, N. A. Karakatsanis, V. Y. Panin, Y. Zhou, A. McMillan, S. Cho, H. Zaidi, M. E. Casey, R. L. Wahl
  Dynamic whole-body PET imaging: principles, potentials and applications
• W. Lv, Q. Yuan, Q. Wang, J. Ma, Q. Feng, W. Chen, A. Rahmim, and L. Lu
  Prognostic potentials of radiomics analysis on the PET and CT components of PET/CT complementary to clinical parameters in patients with nasopharyngeal carcinoma
• L. Lu, P. Wang, J. Ma, Q. Feng, A. Rahmim, and W. Chen
  Generalized factor analysis incorporating alpha-divergence and kinetics-based clustering: application to dynamic myocardial perfusion PET imaging
• Y. Li, A. Rahmim, and L. Lu
  Direct Bayesian parametric image reconstruction from dynamic myocardial perfusion PET data