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

Interesting links/news Team Members

Prospective Students

Graduate Students

We actively recruit talented MSc and PhD students from the University of British Columbia Physics, Interdisciplinary Oncology Program, Biomedical Engineering, and Electrical & Computer Engineering. If joining via the Physics or Medical Physics Program, please see Graduate Program Financial Information page, including information on salary. However, please note that our student RA salaries are substantially higher, set at minimum of $24,000 annually, in addition to standard TA pay for both MSc and PhD as well as tuition remission for PhD students.

Undergraduate Students

We also actively recruit undergraduate students. This includes co-op students which have been an important part of our team, with a variety of projects that are hands-on/experimental as well as computational. It also includes students pursuing honours theses projects, e.g. UBC’s Phys 449.

Interesting links/news Team Members

Last day with Wenbing Lyu

Today we bid farewell to the incredible Wenbing Lyu, visiting PhD student for the past year from Southern Medical University, Guangzhu, China. Wenbing has led significant efforts towards robust radiomics analyses, including PET-CT “fusion radiomics”, for prediction of outcome in head & neck cancer patients.

Awards/Grants Interesting links/news

NSERC Postgraduate Doctoral Scholarship for Cassandra Miller

It is a pleasure to announce our very own Cassandra Miller to have won an NSERC Postgraduate Scholarships-Doctoral (PDG D) award, to pursue her PhD studies in “Monte Carlo Simulations of SPECT Imaging in Peptide Receptor Radionuclide Therapy with Lu177 and Y90”.

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

Grand Round Talk on Radiomics and its Relationship to Machine Learning

Here’s a grand round talk delivered to UBC Department of Radiology, entitled: “What is Radiomics? What is Radiogenomics? And What is Their Relationship to Machine Learning and Deep Learning?” (Oct 17, 2018).

Conference works

11 Presentations at 2019 EANM Annual Meeting

Eleven accepted works by our team and collaborators (5 oral; 6 posters) are being presented at the 2019 Annual Congress of the European Association of Nuclear Medicine (EANM), taking place in Barcelona on October 12-16:

  • X. Hou, W. Lv, J-M. Buregaurd, A. Celler, and A. Rahmim
    Dose distribution radiomics: a new paradigm for assessment of radioligand therapy
  • W. Lv, S. Ashrafinia, J. Ma, L. Lu, and A. Rahmim
    Multi-level multi-modality fusion radiomics: application to PET and CT imaging for improved prognostication of head and neck cancer
  • S. Ashrafinia, P. Dalaie, M. S. Sadaghiani, T. H. Schindler, M. G. Pomper, and A. Rahmim
    Standardized radiomics of clinical myocardial perfusion stress SPECT images to determine coronary artery calcification score
  • I. Shiri, P. Ghafarian, P. Geramifar, K. H. Leung, M. Oveisi, A. Rahmim, and M. R. Ay
    Deep direct attenuation correction of brain PET images using emission data and deep convolutional encoder-decoder for application to PET/MR and dedicated brain PET scanners
  • I. Shiri, G. Hajianfar, S. Ashrafinia, E. Jenabi, M. Oveisi, and A. Rahmim
    Radiogenomics analysis of PET/CT images in lung cancer patients: Conventional radiomics versus deep learning
  • R. Ataya, C. F. Uribe, R. Coope, A. Rahmim, F. Bénard
    Variable density 3D-grids for non-uniform activity distributions in PET and SPECT phantoms: a proof of concept
  • Y. Zhu and A. Rahmim
    MR-guided partial volume correction of 3D PET images using a split Bregman optimized parallel level set framework
  • C. Miller, A. Rahmim, and A. Celler
    Dual-isotope peptide receptor radionuclide therapies with 177Lu and 90Y: is quantitative imaging possible?
  • C. F. Uribe, N. Colpo, E. Rousseau, F. Lacroix-Poisson, D. Wilson, A. Rahmim, and F. Bénard
    Regularized reconstruction improves signal-to-noise and quantification for 18F- PSMA PET/CT imaging
  • S. Rezaei, P. Ghafarian, A. K. Jha, A. Rahmim, S. Sarkar, and M. R. Ay
    Joint compensation for motion and partial volume effects in PET/CT images of lung cancer patients: impact on quantification for different image reconstruction methods
  • H. Vosoughi, P. Geramifar, M. Hajizade, F. Emami, A. Rahmim, and M. Momennezhad
    Optimized PET reconstructions: can they be harmonized as well?
Interesting links/news Workshop/conference

An Interview on Radiomics

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

Conference works

Presentations at 2019 SNMMI Annual Meeting

The published abstracts can now be found here:

Conference works

Presentations at 2019 SNMMI Annual Meeting

Eight accepted works by our group and collaborators (4 oral; 4 posters) are being presented at the 2019 Annual Meeting of the Society of Nuclear Medicine & Molecular Imaging (SNMMI) in Anaheim, June 22-25:

  • K. H. Leung, S. Ashrafinia, M. S. Sadaghiani, P. Dalaie, R. Tulbah, Y. Yin, R. VanDenBerg, J. P. Leal, M. A. Gorin, Y. Du, M. G. Pomper, S. P. Rowe, and A. Rahmim
    A fully automated deep-learning based method for lesion segmentation in 18F-DCFPyL PSMA PET images of patients with prostate cancer
  • Y. Zhu, Y. Gao, O. Rousset, D. F. Wong, and A. Rahmim
    Post-reconstruction MRI-guided enhancement of PET images using parallel level set method with Bregman iteration
  • J. Kim, S. Seo, S. Ashrafinia, A. Rahmim, V. Sossi, and I. S. Klyuzhin
    Training of deep convolutional neural nets to extract radiomic signatures of tumors
  • P. E. Bravo, B. Fuchs, A. K Tahari, D. Pryma, J. Dubroff, and A. Rahmim
    Quantitative renal PET imaging with Rubidium-82 can discriminate individuals with different degrees of renal impairment
  • S. Ashrafinia, M. S. Sadaghiani, P. Dalaie, R. Tulbah, Y. Yin, K. H. Leung, R. VanDenBerg, J. P. Leal, M. A. Gorin, M. G. Pomper, A. Rahmim, and S. P. Rowe
    Characterization of segmented 18F-DCFPyL PET/CT lesions in the context of PSMA-RADS structured reporting
  • I. Shiri, K. H. Leung, P. Ghafarian, P. Geramifar, M. Oveisi, M. R. Ay, and A. Rahmim
    HiResPET: high resolution PET image generation using deep convolution encoder decoder network
  • I. Shiri, K H. Leung, P. Geramifar, P. Ghafarian, M. Oveisi, M. Reza Ay, and A. Rahmim
    PSFNET: ultrafast generation of PSF-modelled-like PET images using deep convolutional neural network
  • I. Shiri, K. H. Leung, P. Ghafarian, P. Geramifar, M. Oveisi, M. R. Ay, and A. Rahmim
    Simultaneous attenuation correction and reconstruction of PET images using deep convolutional encoder decoder networks from emission data