Conference Abstracts/Presentations at the 2024 Annual SNMMI Meeting

Over 30 abstracts by our Qurit lab and our collaborators were accepted to the Annual Meeting of the Society of Nuclear Medicine & Molecular Imaging (SNMMI) held in Toronto in June 8-11, 2024. We are highly looking forward to the meeting:

• F. Yousefirizi, K. J. Savage, L.H. Sehn, C. Gowdy, P. Tonseth, A. R. Hayden, D. Wilson, C.F. Uribe, A. Rahmim
  Prognostic Role of End-of-Treatment FDG PET in Primary Mediastinal Large B-Cell Lymphoma: Application of Deep Neural Network for Segmentation-Free Progression Prediction
• A. Toosi, S. Kurkowska, L. Polson, N. Colpo, C. Dellar, W. R. Parulekar, F. Saad, K. Chi, F. Benard, P. Esquinas Fernandez, A. Rahmim, C. Uribe
  Accelerating SPECT Imaging for Dosimetry via Projection Interpolation using Denoising Diffusion Probabilistic Models
• H. Abdollahi, A. Feleh-Paranj, A. Rahmim
  Enhancing Dose Predictions in Radiopharmaceutical Therapies by Applying Machine Learning to Time-Activity Curve Features: A Simulation Study using PBPK Modeling
• M. R. Salmanpour, A. Fathi Jouzdani, A. Gorji, F. Panahabadi, M. Rajabi, A. Abootorabi, N. Sanati, A. M. Ahmadzadeh, A. Rahmim
  Semi-supervised vs. Supervised Machine Learning Approaches for Improved Overall Survival Prediction: Application to Lung Cancer PET/CT Images
• F. Yousefirizi, C. Holloway, P. Tonseth, A. Alexander, S. Harsini, C. F. Uribe, A. Rahmim
  Prognostic Significance of Lymph Node Involvement in Cervical Cancer Patients with Negative Post-Treatment PET Scans
• A. Toosi, I. Shiri, F. Nasr, H. Zaidi, A. Rahmim
  Fully Automated Segmentation-Free Outcome Prediction for Head and Neck Cancer Using Multi-Angle Maximum Intensity Projections (MA-MIPS) of FDG-PET Images
• F. Yousefirizi, G. Hajianfar, C. Holloway, P. Tonseth, A. Alexander, M. Sabouri, H. Zaidi, C.F. Uribe, A. Rahmim
  Disease-free survival prediction in cervical cancer utilizing radiomics analysis of pre-treatment 18F-FDG PET images
• S. Ahamed, M. Tek, S. Kurkowska, C. F. Uribe, A. Rahmim
  Leveraging counterfactual generative diffusion probabilistic model for anomaly detection: Application to lung cancer PET images
• L. Polson, Nikolaos Karaktsanis, Carlos Uribe, Arman Rahmim
  PyTomography: Advancements in AI-Based Image Reconstructions 
• L. Polson, Nikolaos Karaktsanis, Arman Rahmim, Carlos Uribe
  PyTomography: Advancements in List-mode and Time-of-Flight PET Image Reconstruction
• C. Li, L. Polson, C. Miller, C. Uribe, A. Rahmim
  Optical Surface Information-Based Respiratory Phase-Sorting for SPECT Imaging: A Simulation Study
• C. Li, L. Polson, C. Uribe, A. Rahmim
  A respiratory-motion-incorporated 4D reconstruction method for gated SPECT based on PyTomography
• S. Kurkowska, J. Brosch-Lenz, P. L. Esquinas, A. Toosi, L. Polson, A. Rahmim, F. Benard, C. F. Uribe
  PyTheranostics: a Novel Software Tool to Accelerate Research in Personalized Theranostics with Preclinical and Clinical Dosimetry 
• S. Kurkowska, N. Colpo, C. Dellar, W. Parulekar, F. Saad, K. Chi, K. Zukotynski, J. Beauregard, F. Benard, C. F. Uribe
  Calibration Procedures for Multi-Center Dosimetry Studies with 177Lu Radiopharmaceuticals: Experience from the Canadian Cancer Trial Group PR.21 Trial (NCT 04663997)
• S. Kurkowska, J. Brosch-Lenz, E. Frey, Y. K. Dewaraja, J. Sunderland, C. F. Uribe
  Impact of Fitting Functions on Time-Integrated Activities Estimates for 177Lu-DOTATATE Therapy – Results from SNMMI 177Lu Dosimetry Challenge
• S. Kurkowska, E. Frey, Y. K. Dewaraja, J. Sunderland, C. F. Uribe
  Impact of Segmentation Methods on Healthy Organ and Tumor Activity Estimates for 177Lu-DOTATATE Therapy – Results from SNMMI 177Lu Dosimetry Challenge
• N. Aghakhanolia, A. Sanaat, F. Yousefirizi, A. Toosi, H. Zaidi, A. Rahmim
  Prediction of Standard-Dose Brain 18F-Flortaucipir PET Images via Implicit Neural Networks
• M. Sabouri, A. Toosi, G. Hajianfar, M. Javad Yasemi, M. Edalat-Javid, S. Valavi, A. Bitarafan Rajabi, I. Shiri, H. Zaidi, C. Uribe, A. Rahmim
  SPECT Myocardial Perfusion Imaging Projection Generation: A Dual Approach Utilizing DDPM and CNN, and Comparative Analysis on Dual-Domains
• M. Sabouri, O. Gharibi, G. Hajianfar, Z. Hosseini, Z. Akbari, F. Yousefirizi, A. Bitarafan-Rajabi, H. Zaidi, I. Shiri, A. Rahmim
  AI-based Cardiac Resynchronization Therapy Response Evaluation Using Quantitative SPECT Features
• M. Sabouri, A. Haddadi Avval, S. Bagheri, A. Asadzadeh, M. Sehati, M. Mogharrabi, M. Arefnia, F. Yousefirizi, G. Hajianfar, H. Zaidi, A. Rahmim
  Machine Learning and Radiomics-based Classification of Thyroid Disease Using 99mTc-Pertechnetate Scintigraphy
• E. Mollaheydar, H. Ahn, B. Sabouri, A. Rahmim, E. Cytrynbaum
  Spatiotemporal kinetic modeling of radiopharmaceutical therapies at cellular resolution in heterogeneous tumors
• O. K. Dzikunu, L. Polson, M. Sabouri, S. Ahamed, A. Rahmim, C. Uribe
  PyTomography-Powered 3D Slicer Extension: A fast and easy way for image reconstruction
• O. K. Dzikunu, S. Ahamed, A. Toosi, S. Harsini, F. Bénard, C Uribe, A. Rahmim
  A 3D UNet for automated metastatic lesions detection and segmentation from PSMA-PET images of patients with biochemical recurrence prostate cancer
• I. Martin, C. Ansel, A. Wurzer, C. Miller, K. Kasch, A. Rahmim, F. Benard, C. Uribe, J. Brosch-Lenz
  Quantitative imaging of microscopic distributions of 225Ac-PSMA in a murine model for dosimetry at the cellular level
• A. Akhavanallaf, A. Peterson, J. Blakkisrud, G. Kayal, N. Lafreniere, S. Kurkowska, S. Yadav, N. Cole, C. Uribe, A. Stokke, K. Sjogreen Gleisner, J-M. beauregard, T. Hope, A. Rahmim, K Kit Wong, Y. Dewaraja
  Prediction of 177Lu-PRRT Kidney Absorbed Doses from Pretherapy SSTR-PET: Findings from Multicenter Data
• M. Rezaeian, A. Naseri, D. Liu, M. Soltani, A. Rahmim
  In Silico Computational Fluid Dynamics Modeling of Yttrium-90 Microspheres Targeting Liver Tumors
• F. Eydi, A. Piranfar, B. Saboury, A. Rahmim, M. Soltani
  Investigation of Key Transport Parameters Impacting 177Lu-PSMA Therapy: Computational Modeling of Solid Tumors Integrating Microvasculature
• A. Fathi Jouzdani, A. Abootorabi, M. Rajabi, F. Panahabadi, A. Gorji, N. Sanati, A. M. Ahmadzadeh, A. Mousavi, L. Bonnie, C. Ho, R. Yuan, A. Rahmim, M. R. Salmanpour
  Impact of Clinical Features Combined with PET/CT Imaging Features on Survival Prediction of Outcome in Lung Cancer
• A. Gorji, A. Fathi Jouzdani, A. Abootorabi, F. Panahabadi, M. Rajabi, N. Sanati, A. M. Ahmadzadeh, A. Mousavi, A. Rahmim, M. R. Salmanpour
  Unveiling the Black Box: A Synergistic Exploration of Deep Learning Features in Relation to Radiomics and Clinical Information for Enhanced Interpretability in Medical Imaging
• I. Alberts, S. Xue, …, A. Rahmim, T. Pyka, A. Rominger
  Long-axial field-of-view PET/CT results in improved radiomics feature reliability
• Q. de Bourbon, S. Ahamed, A. Rahmim, P. Blanc-Durand, R. Klein
  Characterizing the limits of lesion detection by AI using synthetic lesions
• N. A. Karakatsanis, D. Atkinson, …, L. Polson, A. Rahmim, …, K. Thielemans
  Usability of PETSIRD, the PET Raw Data open format of the Emission Tomography Standardization Initiative (ETSI): results from ETSI’s first hackathon
• N. A. Karakatsanis, F. Yousefirizi, A. Jha, L. Monaco, T. Bradshaw, I. Buvat, R. Boellaard, A. Rahmim
  An Initiative of the SNMMI AI Task Force for an Online Database of Shareable AI Models in Nuclear Medicine and Molecular Imaging to Raise Awareness and Promote Reproducibility, Training Diversification and Adoptability
• T. Yusufaly, E. Roncali, C. Uribe, …, B. Saboury, A. Rahmim
  Computational Nuclear Oncology for Precision Radiopharmaceutical Therapies
• B. Saboury, J. Brosch-Lenz, M. Morris, F. Farhadi, E. Veziroglu, P. Alvarez, A. Rahmim, M. Ghesanni, E. Siegel
  Heptathlon of Sustainable Meaningful Access to RadioPharmaceutical Therapy (RPT 3.0)