Rahmim Lab

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Eight presentations at SNMMI Annual Meeting

Eight works by our group and collaborators have been accepted at the 2017 annual meeting of the Society of Nuclear Medicine & Molecular Imaging (SNMMI), taking place at Denver Colorado from June 10-14. We look forward to presenting these works (5 oral presentations and 3 posters) at this always excellent meeting:

  • P. Huang, N. Shenkov, S. Fotouhi, E. Davoodi-Bojd, L. Lu, Z. Mari, H. Soltanian-Zadeh, V. Sossi, and A. Rahmim
    Radiomics analysis of longitudinal DaTscan images for improved prediction of outcome in Parkinson’s disease
  • S. Ashrafinia, S. Rowe, M. Gorin, M. DiGianvittorio, L. Lu, M. Lodge, M. Pomper, and A. Rahmim
    Reproducibility and reliability of radiomic features in 18F-DCFPyL PET/CT imaging of prostate cancer
  • J. Tang, B. Yang, N. Shenkov, S. Fotouhi, E. Davoodi-Bojd, L. Lu, H. Soltanian-Zadeh, V. Sossi, and A. Rahmim
    Artificial neural network based outcome prediction in DAT SPECT imaging of Parkinson’s Disease
  • J. Leal, E. Turkbey, L. Solnes, S. Rowe, A. Rahmim, and M. Lodge
    A viewer for dynamic whole body PET/CT studies
  • N. Shenkov, I. Klyuzhin, S. Fotouhi, E. Davoodi-Bojd, H. Soltanian-Zadeh, A. Rahmim, and V. Sossi
    A metric to quantify DaTSCAN tracer uptake in subjects with Parkinson’s disease computed without MRI-based regions of interest
  • J. Kim, J. Miller-Ocuin, A. Rahmim, Matthew J. Oborski, C. M. Laymon, H. J. Zeh III, and J. M. Mountz
    Dynamic 18F-FDG PET response to preoperative neoadjuvant chemotherapy in potentially resectable pancreatic ductal adenocarcinoma may predict overall survival
  • W. Lv, Lijun Lu, J. Jiang, J. Ma, Q. Feng, A. Rahmim, and W. Chen
    Robustness of radiomic features in 18F-FDG PET/CT imaging of nasopharyngeal carcinoma: impact of parameter settings on different feature matrices
  • Y. Salimpour, E. Davoodi-Bojd, S. Fotouhi, R. Yan, S. Mirpour, H. Soltanian-Zadeh, V. Sossi, and A. Rahmim
    Regional correlation of subcortical structures against clinical phenotypes in Parkinson’s disease: DAT SPECT imaging approach

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