Category: 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

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

Eight abstracts accepted to the SNMMI 2016 meeting

Eight submissions from our lab and our collaborations were accepted (5 orals, 3 posters) to the annual meeting of the Society of Nuclear Medicine & Molecular Imaging (SNMMI), held in San Diego this year (June 11-15, 2016). This is always a fantastic meeting that brings together the best in the field, with very strong basic science and clinical tracks. The abstracts are as follows:

  • Application of texture analysis to DaTscan images for enhanced assessment of progression in Parkinson’s disease   
    A. Rahmim, Y. Salimpour, S. Jain, S. Blinder, I. Klyuzhin, G. Smith, Z. Mari, and V. Sossi
  • Application of novel PET metric to quantify liver metastases for enhanced prognostication of clinical outcome
    A. Rahmim, C. R. Schmidtlein, K. P. Bak-Fredslund, R. M. Subramaniam, A. Morsing, S. Keiding, and O. L. Munk
  • Adaptive PSF modeling for enhanced heterogeneity quantification in oncologic PET imaging
    S. Ashrafinia, E. M. Gonzalez, H. Mohy-ud-Din, A. K. Jha, R. Subramaniam, and A. Rahmim
  • Simultaneous SUV/Patlak-4D whole-body PET: a multi-parametric 4D imaging framework for routine clinical application
    A. Karakatsanis, M. A. Lodge, G. Fahrni, M. E. Casey, Y. Zhou, R. Subramaniam, H. Zaidi, and A. Rahmim
  • Optimized Haralick texture quantification to track Parkinson’s disease progression from DAT SPECT images
    A. Rahmim, Y. Salimpour, S. Blinder, I. Klyuzhin, and V. Sossi
  • PSF overestimation improves PET image SUV quantification
    S. Ashrafinia, H. Mohy-ud-Din, N. A. Karakatsanis, M. E. Casey, M. A. Lodge, and A. Rahmim
  • Robustness of textural features in 11C-choline and 18F-FDG PET/CT scans of nasopharyngeal carcinoma
    L. Lu, W. Lv, J. Jiang, J. Ma, Q. Feng, A. Rahmim, and Wufan Chen
  • Enhanced dynamic cardiac PET imaging using complementary reconstruction
    B. Yang, A. Rahmim, and J. Tang

 

BIOMED presentation for BRAIN initiative efforts

Our conference paper to the OSA Biomedical Optics Conference (BIOMED) has been accepted for presentation:

A. K. Jha, Y. Zhu, J. Dreyer, J. Kang, A. Gjedde, D. Wong, A Rahmim
Incorporating boundary conditions in the integral form of the radiative transfer equation for transcranial imaging

This work is part of our funded BRAIN initiative effort. We are focusing on the inverse problem associated with transcranial optical and photoacoustic imaging. To achieve accurate image generation, we require accurate models for photon propagation through the tissue, accounting for boundary conditions, which this abstract pursues.

Interestingly, BIOMED has devoted a topical meeting/section to “Optics and the Brain”, given renewed funds and efforts by the US BRAIN initiative and European Human Brain Project to gain a better understanding the brain as a critical frontier in science and medicine.