Interesting links/news Workshop/conference

On Effective Work Habits

Here is video of grand rounds lecture by Dr. Arman Rahmim on “11 Practical Steps for Effective Time Management in a Distracted World”:

Some of these (listed below), may at first not be intuitive but can be very important.


Workshop on “Important Trends in Medical Imaging: Artificial Intelligence, Quantitation and Theranostics”

Interesting links/news Workshop/conference

Lecture on Use of Dynamic PET Imaging in the Clinic

Lecture at the University of Groningen on May 8, 2019 , entitled: “Does Dynamic PET Imaging have a Future in Clinical Oncologic Practice?”

Interesting links/news Workshop/conference

An Interview on Radiomics

Here’s a podcast interview at the annual meeting of SNMMI on radiomics:


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

Conference works Workshop/conference

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



Medical Imaging Workshop in Guangzhou, China

We conducted a workshop on “State-of-the-Art Medical Imaging and Analysis Towards Personalized Medicine” at the Southern Medical University, Guangzhou, China on Monday July 24th, 2017. There were a number of interesting discussions, including ideas for expanded collaborative efforts between the Southern Medical University and Johns Hopkins University.