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.
Author: Quantitative Tomography Lab
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”.
Lecture at the University of Groningen on May 8, 2019 , entitled: “Does Dynamic PET Imaging have a Future in Clinical Oncologic Practice?”
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).
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?
Here’s a podcast interview at the annual meeting of SNMMI on radiomics:
The published abstracts can now be found here:
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…
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