Category: 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?

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

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

 

Special Abstract Citation!

Our abstract was cited in the highlights of the 30th Annual Congress of the European Association of Nuclear Medicine (EANM), Vienna 2017. This was for an unusual reason, though we’ve been doing this for years now!

It reads:

Shiri et al. [46] presented a most unusual and scientifically highly interesting paper. These authors sought to predict lung metastases in patients with soft tissue sarcoma applying advanced machine learning to radiomic features. The unusual aspect, however, was the collaboration between Iranian universities and universities in the US, showing that science is above politics.”

 

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

Five oral presentations on our BRAIN initiative efforts

Five conference submissions related to our BRAIN initiative efforts have been accepted as oral presentations at 2017 SPIE conferences (first four at Photonics West and last one at Medical Imaging). We look forward to sharing our interesting findings in active efforts towards transcranial optical and photoacoustic imaging of network activity in the intact brain:

  • H. K. Zhang, J. Kang, P. Yan, D. Abou, H. N. D. Le, D. Thorek, J. Kang, A. Gjedde, A. Rahmim, D. F. Wong, L. M. Loew, and E. M. Boctor
    Listening to membrane potential: photoacoustic voltage sensitive dye recording
  • Y. Zhu, A. K. Jha, J. K. Dreyer, H. N. D. Le, Jin U. Kang, P. E. Roland, D. F. Wong, and A. Rahmim
    A three-step reconstruction algorithm for fluorescence molecular tomography based on compressive sensing
  • H. N. D. Le, Y-T. A. Gau, A. Rahmim, D. F. Wong and J. U. Kang
    Through-skull vasculature assessment using fluorescence brain imaging on murine models at around 800 nm.
  • J. Kang, H. Kai Zhang, A. Rahmim, D. F. Wong, J. U. Kang, and E. M. Boctor
    Toward high-speed transcranial photoacoustic imaging using compact near-infrared pulsed LED illumination system
  • A. K. Jha, Y. Zhu, D. F. Wong, and A. Rahmim
    A radiative transfer equation-based image-reconstruction method incorporating boundary conditions for diffuse optical imaging