Author: Quantitative Tomography Lab

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?

Presentations at 2019 SNMMI Annual Meeting

The published abstracts can now be found here:
https://rahmimlab.com/publications/conference_proceedings/

Quantitative Tomography Lab

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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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