Journal Articles

2023

• J. Herington, M.D. McCradden, K. Creel., R. Boellaard,  E.C. Jones,  K.C. Prabhat, A.K. Jha, A. Rahmim, P.J.H. Scott, J. Sunderland, R.L. Wahl, Z. Sven, B. Saboury
  Ethical Considerations for Artificial Intelligence in Medical Imaging: Data Collection, Development, and Evaluation
  J. Nucl. Med., In Press, 2023

• J. Herington, M.D. McCradden, K. Creel., R. Boellaard,  E.C. Jones,  K.C. Prabhat, A.K. Jha, A. Rahmim, P.J.H. Scott, J. Sunderland, R.L. Wahl, Z. Sven, B. Saboury
  Ethical Considerations for Artificial Intelligence in Medical Imaging: Deployment and Governance
  J. Nucl. Med., In Press, 2023

• H. Koniar, C. Miller, A. Rahmim, P. Schaffer, C. Uribe
  A GATE simulation study for dosimetry in cancer cell and micrometastasis from the 225Ac decay chain
  EJNMMI Phys., vol. 10, pp. 46., 2023

• I. Shiri, M. Amini, F. Yousefirizi, A. Vafaei Sadr, G. Hajianfar, Y. Salimi, Z. Mansouri, E. Jenabi, M. Maghsoudi, I. Mainta, M. Becker, A. Rahmim, H. Zaidi 
  Information Fusion for Fully Automated Segmentation of Head & Neck Tumors from PET and CT Images
  Med. Physics., In Press, 2023.

• M.R. Salmanpour, M. Hosseinzadeh, S.M. Rezaeijo, A. Rahmim 
  Fusion-based tensor radiomics using reproducible features: Application to survival prediction in head and neck cancer 
  Comp. Meth. Prog. Biomed., vol. 240, pp. 107714, 2023

• M.O. Alamdari, P. Ghafarian, M.R. Ay,  A. Rahmim, M. Bakhshayesh-Karam
  Impact of random and scatter coincidences from outside of FOV on PET/CT imaging with different reconstruction protocols
  Nucl. Sci. Tech., vol. 34, pp. 184, 2023

• B. Ahmed, P. Sheikhzadeh, V. Changizi, M. Abbasi, Y. Soleymani, W. Sarhan, A. Rahmim   
  CT Radiomics Analysis of Primary Colon Cancer Patients With or Without Liver Metastases: A Correlative Study with [18F]FDG PET Uptake Values 
  Abdominal Radiology, In Press, 2023

• C. Ayubcha, S.B. Singh, K.H. Patel, A. Rahmim, J. Hasan, L. Liu, T. Werner, A. Alavi
  Machine learning in the positron emission tomography imaging of Alzheimer’s disease
  Nucl. Med. Comm., vol. 44, pp. 751-766, 2023

• R. Samimi, I. Shiri, Y. Ahmadyar, J. van den Hoff, A. Kamali-Asl, A. Rezaee, F. Yousefirizi, P. Geramifar, A. Rahmim
  Radiomics predictive modeling from dual-time-point FDG PET Ki parametric maps: Application to chemotherapy response in lymphoma
  EJNMMI Research, vol. 13, pp. 70, 2023

• G. Hajianfar, S. Kalayinia, M. Hosseinzadeh, S. Samanian, M. Maleki, V. Sossi, A. Rahmim, M.R. Salmanpour
  Prediction of Parkinson’s disease pathogenic variants using hybrid machine learning systems and radiomic features
  Phys. Med., vol. 113, pp. 102647, 2023

• H. Abdollahi, B. Saboury, M. Soltani,  K. Shi, C. Uribe, A. Rahmim
  Radiopharmaceutical therapy on-a-chip: A perspective on microfluidic-driven digital twins towards personalized cancer therapies
  Science Bulletin, In Press, 2023

• S. Ashrafinia, P. Dalaie, T.H. Schindler, M.G. Pomper, A. Rahmim
  Standardized Radiomics Analysis of Clinical Myocardial Perfusion Stress SPECT Images to Identify Coronary Artery Calcification
  Cureus, vol. 15, pp. e43343, 2023

• I. Klyuzhin, G. Chaussé, I. Bloise, S. Harsini, J.L. Ferres, C. Uribe, A. Rahmim
  PSMA-Hornet: fully-automated, multi-target segmentation of healthy organs in PSMA PET/CT images
  Med. Phys., In Press, 2023

• H. Abdollahi, T. Dehesh, N. Abdalvand, A. Rahmim
  Radiomics and dosiomics based prediction of radiotherapy-induced xerostomia in head and neck cancer patients
  Int. J. Radiat. Biol., In Press, 2023

• M. Hosseinzadeh, A. Gorji, A.F. Jouzdani, S.M. Rezaeijo, A. Rahmim, M.R. Salmanpour
  Prediction of cognitive decline in Parkinson's disease using clinical and DAT SPECT imaging features, and hybrid machine learning systems
  Diagnostics, vol. 13, pp. 1691, 2023

• M. R. Salmanpour, S. M. Rezaeijo, M. Hosseinzadeh, A. Rahmim 
  Deep versus handcrafted tensor radiomics features; Prediction of survival in head and neck cancer using machine learning and fusion techniques
  Diagnostics, vol. 13, pp. 1696, 2023

• A. Rahmim, A. Toosi, M. Salmanpour, N. Dubljevic, I. Janzen, I. Shiri, R. Yuan, C. Ho, H. Zaidi, C. MacAulay, C. Uribe, F. Yousefirizi
  Tensor radiomics: Paradigm for systematic incorporation of multi-flavoured radiomics features
  Quan. Imag. Med. Surg., In Press, 2023

• T. Bradshaw, Z. Huemann, J. Hu, A. Rahmim
  A guide to cross-validation for artificial intelligence in medical imaging 
  Radiology: AI, vol. 5, pp. e220232, 2023

• T. Bradshaw, M. McCradden, A. Jha, J.Dutta, B. Saboury, E. Siegel, A. Rahmim
  Artificial intelligence algorithms need to be explainable – or do they?
  J. Nucl. Med., vol. 64, pp. 976-977, 2023

• C. Miller, C. Uribe, X. Hou, A. Rahmim, A. Celler
  177Lu SPECT imaging in the presence of 90Y: Does 90Y degrade image quantification? A simulation study
  Biomed. Phys. Eng. Express, vol. 9, pp. 045003, 2023

• J. Brosch-Lenz, A. Delker, F. Völter, L. M Unterrainer, L. Kaiser, P. Bartenstein, S. Ziegler, A. Rahmim, C. Uribe, G. Böning
  Towards single time point image-based dosimetry of 177Lu-PSMA-617 therapy
  J. Nucl. Med., vol. 64, pp. 767-774, 2023

• Y. Xu, I. Klyuzhin, S. Harsini, A. Oritz, S. Zhang, F. Benard, R. Dodhia, C.F. Uribe, A. Rahmim, J. Lavista Ferres
  Automatic segmentation of prostate cancer metastases in PSMA PET/CT images using deep neural networks with weighted batch-wise dice loss
  Comp. Biol. Med. vol. 158, pp. 106882, 2023

• M. Souri, M. Kiani Shahvandi, M. Chiani, F. Moradi Kashkooli, A. Farhangi, M. Reza Mehrabi, A. Rahmim, V.M. Savage, M. Soltani
  Stimuli-sensitive nano-drug delivery with programmable size changes to enhance accumulation of therapeutic agents in tumors
  Drug Delivery, vol. 30, pp. 2186312, 2023

• M. Esmaeili, A. Toosi, A. Roshanpoor, V. Changizi, M. Ghazisaeedi, A. Rahmim, M. Sabokrou
  Generative adversarial networks for anomaly detection in biomedical imaging: A study on 7 medical image datasets
  IEEE Access, vol. 11, pp. 17906-17921, 2023

• M.R. Salmanpour, M. Hosseinzadeh, M. Bakhtiyari, M. Maghsudi, A. Rahmim
  Prediction of drug amount in Parkinson's disease using hybrid machine learning systems and radiomics features
  Intern. J. Imaging Syst. Tech., vol. 33, pp. 1437-1449, 2023

• H. Vosoughi, M. Momennezhad, F. Emani, M. Hajizadeh, A. Rahmim, P. Geramifar
  Multicenter quantitative fluorodeoxyglucose positron emission tomigraphy performance harmonization: use of hottest voxels towards more robust quantification
  Quant. Imag. Med. Surg., vol. 13, pp. 2218-2233, 2023

• B. Saboury, T. Bradshaw, R. Boellaard, I. Buvat, J. Dutta, M. Hatt, A. K. Jha, Q. Li, C. Liu, H. McMeekin, M. A. Morris, P. J.H. Scott, E. Siegel, J. J. Sunderland, N. Pandit-Taskar, R. L. Wahl, S. Zuehlsdorff, A. Rahmim
  Artificial intelligence in nuclear medicine: Opportunities, challenges, and responsibilities toward a trustworthy ecosystem
  J. Nucl. Med., vol. 64, pp. 188-196, 2023

• M. Hatt, A. K. Krizsan, A. Rahmim, T. J. Bradshaw, P. F. Costa, A. Forgacs, R. Seifert, A. Zwanenburg, I. El Naqa, P. Kinahan, F. Tixier, A. K. Jha, D. Visvikis
  Joint EANM/SNMMI guideline on radiomics in nuclear medicine
  Eur. J. Nucl. Med. Mol. Imaging, vol. 50, pp. 352-375, 2023

• F. Farhadi, J. R. Rajagopal, E. M. Veziroglu, H. Abdollahi, I. Shiri, M. Nikpanah, M. A. Morris, H. Zaidi, A. Rahmim, B. Saboury
  Multi-scale temporal imaging: from micro- and meso- to macro-scale-time nuclear medicine
  PET Clinics, vol. 18, pp. 135-148, 2023

• H. Abdollahi, A. Rahmim
  Digital twins for personalized healthcare: Application to radiopharmaceutical therapies
  Frontiers in Biomed. Tech., vol. 10, pp. 1-5, 2023

• J. Brosch-Lenz, C. Uribe, A. Rahmim, B. Saboury
  Theranostic digital twins: An indispensable prerequisite for personalized cancer care
  J. Nucl. Med., vol. 64, pp. 501, 2023

• M. R. Salmanpour, M. Bakhtiyari, M. Hosseinzadeh, M. Maghsudi, F. Yousefirizi, M. M. Ghaemi, A. Rahmim
  Application of novel hybrid machine learning systems and radiomics features for non-motor outcome prediction in Parkinson's disease
  Phys. Med. Biol., vol. 68, pp. 035004, 2023

• I. Shiri, A. Vafaei Sadr, A. Akhavan, Y. Salimi, A. Sanaat, M. Amini, B. Razeghi, A. Saberi, H. Arabi, S. Ferdowsi, M. Becker, S. Voloshynovskiy, D. Gündüz, A. Rahmim, Habib Zaidi
  Decentralized collaborative multi-institutional PET attenuation and scatter correction using federated deep learning
  Euro. J. Nucl. Med. Mol. Imag., vol. 50, pp. 1034-1050, 2023

• R. Sharifpour, P. Ghafarian, M. Bakhshayesh-Karam, C. F. Uribe, A. Rahmim, M. R. Ay
  Reducing scan time using time-of-flight protocol in clinical [18F]FDG-PET/CT imaging: A feasibility study
  Iranian J. Nucl. Med., vol. 60, p. 1-10, 2023

• D. Du, W. Lv, J. Lv, X. Chen, H. Wu, A. Rahmim, L. Lu
  Deep-learning based harmonization of CT reconstruction kernels towards improved clinical task performance
  Euro. Radiology, vol. 33, pp. 2426-2438, 2023

2022

• A. Rahmim, J. Brosch-Lenz, A. Fele-Paranj, F. Yousefirizi, M. Soltani, C. Uribe, B. Saboury
  Theranostic digital twins for personalized radiopharmaceutical therapies: Reimagining theranostics via computational nuclear oncology
  Frontiers in Oncology, vol. 12, pp. 1062592, 2022

• A. K. Jha, T. J. Bradshaw, I. Buvat, M. Hatt, P. Kc, C. Liu, N. F. Obuchowski, B. Saboury, P. J. Slomka, J. L. Sunderland, R. L. Walk, Z. Yu, S. Zuehlsdorff, A. Rahmim, R. Boellaard
  Artificial intelligence in nuclear medicine: best practices for evaluation (the RELAINCE guidelines)
  J. Nucl. Med., vol. 63, pp. 1288-1299, 2022

• R. Klein, M. Oliver, D. La Russa, J. Agapito, S. Gaede, J-P. Bissonnettee, A. Rahmim, C Uribe
  COMP Report: CPQR technical quality control guidelines for use of Positron Emission Tomography - Computed Tomography (PET/CT) in Radiation Treatment Planning
  J. Appl. Clin. Med. Phys., vol. 23, pp. e13785, 2022

• R. Fedrigo, W. P. Segars, P. Martineau, C. Gowdy, I. Bloise, C. F. Uribe, A. Rahmim
  Development of scalable lymphatic system in the 4D XCAT phantom: Application to quantitative evaluation of lymphoma PET segmentations
  Med. Phys., vol. 49, pp. 6871-6884, 2022

• C. Sample, N. Jung, A. Rahmim, C. Uribe, H. Clark
  Development of a CT-based auto-segmentation model for prostate-specific membrane antigen (PSMA) positron emission tomography-delineated tubarial glands
  Cureus, vol. 14, pp. e31060, 2022

• P. Moradi, Y. Bouchareb, H. Al Dhuhli, I. Shiri, F. Al Kindi, B. Moradi, H. Zaidi, A. Rahmim
  Two-step machine learning to diagnose and predict involvement of lungs in COVID-19 and pneumonia using CT radiomics
  Comp. Biol. Med., vol. 150, pp. 106165, 2022

• M. Kiani Shahvandi, M. Soltani, F. M. Kashkooli, B. Saboury, A. Rahmim
  Spatiotemporal multi-scale modeling of radiopharmaceutical distributions in vascularized solid tumors
  Scientific Reports, vol. 12, pp. 14582, 2022

• A. Ghafari, P. Sheikhzadeh, N. Seyyedi, M. Abbasi, S. Farzenefar, F. Yousefirizi, M. R. Ay, A. Rahmim
  Generation of 18F-FDG PET standard scan images from short scans using cycle-consistent generative adversarial network
  Phys. Med. Biol., vol. 67, pp. 215005, 2022

• H. Koniar, C. Rodriguez-Rodriguez, V. Radchenko, H. Yang, P. Kunz, A. Rahmim, C. Uribe, P. Schaffer
  SPECT imaging of 226Ac as a theranostic isotope for 225Ac radiopharmaceutical development
  Phys. Med. Biol., vol. 67, pp. 185009, 2022

• I. Shiri, S. Mostafaei, A. Haddadi Avval, Y. Salimi, A. Sanaat, A. Akhavanallaf, H. Arabi, A. Rahmim, H. Zaidi
  High-dimensional multinomial multiclass severity scoring of COVID-19 pneumonia using CT radiomics features and machine learning algorithms
  Scientific Reports, vol. 12, pp. 14817, 2022

• P. Huang, P. B. Illei, ..., A. Rahmim, ..., S. Lam, E. Gabrielson
  Lung cancer recurrence risk prediction through integrated deep learning evaluation
  Cancers, vol. 14, pp. 4150, 2022

• H. Abdollahi, E. Chin, H. Clark, D. E. Hyde, S. Thomas, J. Wu, C. F. Uribe, A. Rahmim
  Radiomics-guided radiation therapy: opportunities and challenges
  Phys. Med. Biol., vol. 67, pp. 12TR02, 2022

• R. F. Gottesman, A. Wu, J. Coresh, D. D. Knopman, C. R. Jack Jr, A. Rahmim et al.
  Association of vascular risk and amyloid burden with subsequent dementia
  Annals of Neurology, vol. 92, pp. 607-619, 2022

• A. Monsef, M. R. Ay, P. Sheikhzadeh, P. Geramifar, A. Rahmim, P. Ghafarian
  Harmonization based on quantitative analysis of standardized uptake value variations across PET/CT scanners: a multicenter phantom study
  Nucl. Med. Comm., vol. 43, pp. 1004-1014

• F. M. Kashkooli, M. A. Abazari, M. Soltani, M. Akbarpour, A. Rahmim
  Spatiotemporal multi-scale computational model for FDG PET imaging at different stages of tumor growth and angiogenesis
  Scientific Reports, vol. 12, pp. 10062, 2022

• W. Lv, H. Xu, X. Han, H. Zhang, J. Ma, A. Rahmim, L. Lu
  Context-aware saliency guided radiomics: application to prediction of outcome and HPV-status from multi-center PET/CT images of head and neck cancer
  Cancers, vol. 14, pp. 1674, 2022

• H. Keller, T. Shek, B. Driscoll, Y. Xu, B. Nghiem, S. Nehmeh, M. Grkovski, C. R. Schmidtlein, M. Budzevich, Y. Balagurunathan, J. J. Sunderland, R. R. Beichel, C. Uribe, T-Y. Lee, F. Li, D. A. Jaffray, I. Yeung
  Noise-based image harmonization significantly increases repeatability and reproducibility of radiomics features in PET images: a phantom study
  Tomography, vol. 8, pp. 1113-1128, 2022

• M. Mousavi, M. Dehghan Manshadi, M. Soltani, F. M. Kashkooli, A. Rahmim, A. Mosavi, M. Kvasnica, P. M. Atkinson, L. Kovacs, A. Koltay, N. Kiss, J. Adeli
  Modeling the efficacy of different anti-angiogenic drugs on treatment of solid tumors using 3D computational modeling and machine learning
  Comp. Biol. Med., vol. 146, pp. 105511, 2022

• I. Shiri, ..., A. Rahmim, H. Zaidi
  COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: analysis of a multi-institutional dataset of 14,339 patients
  Comp. Biol. Med., vol. 145, pp. 105467, 2022

• I. S. Klyuzhin, Y. Xu, A. Ortiz, J. M. L. Ferres, G. Hamarneh, A. Rahmim
  Testing the ability of convolutional neural networks to learn radiomic features
  Comp. Meth. Prog. Biomed., vol. 219, pp. 106750, 2022

• I. Shiri, A. V. Sadr, M. Amini, Y. Salimi, A. Sanaat, A. Akhavanallaf, B. Razeghi, S. Ferdowsi, A. Saberi, H. Arabi, M. Becker, S. Voloshynovskiy, D. Gunduz, A. Rahmim, H. Zaidi
  Decentralized distributed multi-Institutional PET segmentation using a federated deep learning framework
  Clinic. Nucl. Med., vol. 47, 606-617, 2022 

• J-M. Beauregard and A. Rahmim
  Harmonization of nomenclature for molecular imaging metrics of tumour burden: molecular tumour volume (MTV), total lesion activity (TLA) and total lesion fraction (TLF)
  Euro. J. Nucl. Med. Mol. Imag., vol. 49, 424-426, 2022

• R. Fedrigo, D. J. Kadrmas, P. E. Edem, L. Fougner, I. S. Klyuzhin, M. P. Petric, F. Bénard, A. Rahmim, C. Uribe
  Quantitative evaluation of PSMA PET imaging using a realistic anthropomorphic phantom and shell-less radioactive epoxy lesions
  EJNMMI Physics, vol. 9, pp. 2, 2022
• M. Peer-Firozjaei, M. Tajik-Mansoury, P. Geramifar, R. Ghorbani, S. Zarifi, C. Miller, A. Rahmim
  Optimized cocktail of 90Y/177Lu for radionuclide therapy of neuroendocrine tumors of various sizes: a simulation study
  Nucl. Med. Comm., vol. 43, 646-655, 2022
• I. Shiri, M. Amini, M. Nazari, G. Hajianfar, A. Haddadi Avval, H. Abdollahi, M. Oveisi, H. Arabi, A. Rahmim, H. Zaidi
  Impact of feature harmonization on radiogenomics analysis: prediction of EGFR and KRAS mutations from non-small cell lung cancer PET/CT images
  Comp. Biol. Med., vol. 142, 1-12, 2022
• T. J. Bradshaw, R. Boellaard, J. Dutta, A. K. Jha, P. Jacobs, Q. Li, C. Liu, A. Sitek, B. Saboury, P. J. H. Scott, P. J. Slomka, J. J. Sunderland, R. L. Wahl, F. Yousefirizi, S. Zuehlsdorff, A. Rahmim, I. Buvat
  Nuclear medicine and artificial intelligence: Best practices for algorithm development
  J. Nucl. Med, vol. 63, pp. 500-510, 2022
• V. Oreiller, V. Andrearczyk, M. Jreige, S. Boughdad, H. Elhalawani, J. Castelli, M. Vallieres, S. Zhu, J. Xie, Y. Peng, A. Iantsen, M. Hatt, Y. Yuani, J. Ma, X. Yang, C. Rao, S. Pai, K. Ghimire, X. Feng, M. A. Naser, C. D. Fuller, F. Yousefirizi, A. Rahmim, H. Chen, L. Wang, J. O. Prior, A. Depeursinge
  Head and neck tumor segmentation in PET/CT: The HECKTOR challenge
  Medical Image Analysis, vol. 77, pp. 1-16, 2022
• C. Miller, J. Rousseau, C. F. Ramogida, A. Celler, A. Rahmim, C. F. Uribe
  Implications of physics, chemistry and biology for dosimetry calculations using theranostic pairs
  Theranostics, vol. 12, 232-259, 2022
• N. Hasani, M. A. Morris, A. Rahmim, R. M. Summers, E. Jones, E. Siegel, B. Saboury
  Trustworthy artificial intelligence in medical imaging
  PET Clinics, vol 17, pp. 1-12, 2022
• N. Hasani, F. Farhadi, M. A. Morris, M. Nikpanah, A. Rahmim, Y. Xu, A. Pariser, M. T. Collins, R. M. Summers, E. Jones, E. Siegel, B. Saboury
  Artificial intelligence in medical imaging and its impact on the rare disease community: threats, challenges and opportunities
  PET Clinics, vol 17, pp. 13-29, 2022
• K. A. Zukotynski, V. C. Gaudet, C. F. Uribe, K. Chiam, F. Bénard, V. H. Gerbaudo
  Clinical applications of artificial intelligence in positron emission tomography of lung cancer
  PET Clinics, vol 17, pp. 77-84, 2022
• K. Ma, S. A. Harmon, I. S. Klyuzhin, A. Rahmim, B. Turkbey
  Clinical application of artificial intelligence in positron emission tomography: imaging of prostate cancer
  PET Clinics, vol. 17, pp. 137–143, 2022
• N. Hasani, S. S. Paravastu, F. Farhadi, F. Yousefirizi, M. A. Morris, A. Rahmim, M. Roschewski, R. M. Summers, B. Saboury
  Artificial intelligence in lymphoma PET imaging: A scoping review (current trends and future directions)
  PET Clinics, vol. 17, pp. 145-174, 2022
• F. Yousefirizi, P. Decasez, A. Amyar, S. Ruan, B. Saboury, A. Rahmim
  AI-based detection, classification and prediction/prognosis in medical imaging: Towards radiophenomics (arXiv)
  PET Clinics, vol. 17, pp. 183-212, 2022
• M. R. Salmanpour, M. Shamsaei, G. Hajianfar, H. Soltanian-Zadeh, A. Rahmim
  Longitudinal clustering analysis and prediction of Parkinson’s disease progression using radiomics and hybrid machine learning
  Quan. Imag. Med. Surg., vol. 12, pp. 906-919, 2022
• I. Mohammadi, I. Castro, A. Rahmim, J. Veloso
  Motion in nuclear cardiology imaging: types, artifacts, detection and correction techniques
  Phys. Med. Biol, vol. 67, pp. 02TR02, 2022

2021

• N. Fasaeiyan, M. Soltani, F. Moradi Kashkooli, E. Taatizadeh, A. Rahmim
  Computational modeling of FDG PET tracer distribution in solid tumors integrating microvasculature
  BMC Biotechnology, vol. 21, pp. 67, 2021
• C. Uribe, A. Peterson, B. Van, R. Fedrigo, J. Carlson, J. Sunderland, E. Frey, Y. K Dewaraja
  An international study of factors affecting variability of dosimetry calculations, part 1: design and early results of the SNMMI dosimetry challenge
  J. Nucl. Med, vol. 62 (suppl. 3), pp. 36S-47S, 2021
• M. Amini, M. Nazari, I. Shiri, G. Hajianfar, M. R. Deevband, H. Abdollahi, H. Arabi, A. Rahmim, Habib Zaidi
  Multi-level multi-modality (PET and CT) fusion radiomics: Prognostic modeling for non-small cell lung carcinoma
  Phys. Med. Biol, vol. 66, pp. 205017, 2021
• A. Toosi, A. G. Bottino, B. Saboury, E. Siegel, A. Rahmim
  A brief history of AI: how to prevent another winter (a critical review) (arXiv)
  PET Clinics, vol. 16, pp. 449-469, 2021
• A. Sitek, S. Ahn, E. Asma, A. Chandler, A. Ihsani, S. Prevrhal, A. Rahmim, B. Saboury, K. Thielemans
  Artificial intelligence in PET: an industry perspective (arXiv)
  PET Clinics, vol. 16, pp. 483-492, 2021
• A. K. Jha, K. J. Myers, N. A. Obuchowski, Z. Liu, Md Ashequr Rahman, B. Saboury, A. Rahmim, B. A. Siegel
  Objective task-based evaluation of artificial intelligence-based medical imaging methods: Framework, strategies and role of the physician (arXiv)  
  PET Clinics, vol. 16, pp. 493-511, 2021
• F. Yousefirizi, A. Jha, J. Brosch-Lenz, B. Saboury, A. Rahmim
  Toward high-throughput artificial intelligence-based segmentation in oncological PET imaging (arXiv)
  PET Clinics, vol. 16, pp. 577-596, 2021
• F. Orlhac, C. Nioche, I. Klyuzhin, A. Rahmim, I. Buvat
  Radiomics in PET imaging: a practical guide for newcomers (preprint)
  PET Clinics, vol. 16, pp. 597–612, 2021
• J. Brosch-Lenz, F. Yousefirizi, K. Zukotynski, J-M. Beauregard, V. Gaudet, B. Saboury, A. Rahmim, C. Uribe
  Role of artificial intelligence in theranostics: toward routine personalized radiopharmaceutical therapies (arXiv)
  PET Clinics, vol. 16, pp. 627-641, 2021
• F. Moradi Kashkooli, M. Soltani, M. M. Momeni, A. Rahmim
  Enhanced drug delivery to solid tumors via drug-loaded nanocarriers: an image-based computational framework
  Frontiers in Oncology, vol. 11, pp. 655781, 2021
• Y. Bouchareb, P. Moradi Khaniabadi, F. Al Kindi, H. Al Dhuhli, I. Shiri, H. Zaidi, A. Rahmim
  Artificial intelligence-driven assessment of radiological images for COVID-19
  Comp. Biol. Med., vol. 136, pp. 104665, 2021
• Y. Zhu, M. Bilgel, Y. Gao, O. G. Rousset, S. M. Resnick, D. F. Wong, A. Rahmim
  Deconvolution-based partial volume correction of PET images with parallel level set regularization
  Phys. Med. Biol., vol. 66, pp. 145003, 2021
• Y. Gao, Y. Zhu, M. Bilgel, S. Ashrafinia, L. Lu, A. Rahmim
  Voxel-based partial volume correction of PET images via subtle MRI guided non-local means regularization
  Physica Medica, vol. 89, pp. 129-139, 2021
• D. G. Black, Y. Oloumi Yazdi, J. Wong, R. Fedrigo, C. Uribe, D. J. Kadrmas, A. Rahmim, I. S. Klyuzhin
  Design of an anthropomorphic PET phantom with elastic lungs and respiration modeling
  Medical Physics, vol. 48, pp. 4205-4217, 2021
• X. Hou, J. Brosch, C. Uribe, A. Desy, G. Böning, J-M. Beauregard, A. Celler, A. Rahmim
  Feasibility of single-time-point dosimetry for radiopharmaceutical therapies
  J. Nucl. Med., vol. 62, pp. 1006-1011, 2021
• M. R. Salmanpour, M. Shamsaei, A. Rahmim
  Feature selection and machine learning methods for optimal identification and prediction of subtypes in Parkinson’s disease
  Comp. Meth. Prog. Biomed., vol. 206, pp. 106131, 2021
• I. Shiri et al.
  Machine learning-based prognostic modeling using clinical data and quantitative radiomic features from chest CT images in COVID-19 patients
  Comp. Biol. Med., vol. 132, pp. 104304, 2021
• R. L. Wahl et al.
  Mars Shot for nuclear medicine, molecular imaging, and molecularly targeted radiopharmaceutical therapy
  J. Nucl. Med., vol. 62, pp. 6-14, 2021
• J. Brosch, C. Uribe et al.
  Influence of dosimetry method on bone lesion absorbed dose estimates in PSMA therapy: application to mCRPC patients receiving Lu-177-PSMA-I&T
  EJNMMI Physics, vol. 8, pp. 26, 2021
• M. Adams, A. Rahmim, J. Tang
  Improved motor outcome prediction in Parkinson’s disease applying deep learning to DaTscan SPECT images
  Comp. Biol. Med., vol. 132, pp. 104312, 2021
• D. Du, J. Gu, X. Chen, W. Lv, Q. Feng, A. Rahmim, H. Wu, L. Lu
  Integration of PET/CT radiomics and semantic features for differentiation between active pulmonary tuberculosis and lung cancer
  Molec. Imag. Biol., vol. 23, pp. 287–298, 2021
• M. R. Salmanpour, M. Shamsaei, A. Saberi, G. Hajianfar, H. Soltanian-Zadeh, A. Rahmim
  Robust identification of Parkinson’s disease subtypes using radiomics and hybrid machine learning
  Comp. Biol. Med., vol. 129, pp. 104142, 2021
• J-C. Cheng, C. Bevington, A. Rahmim, I. Klyuzhin, J. Matthews, R. Boellaard, V. Sossi
  Dynamic PET image reconstruction utilizing intrinsic data-driven HYPR4D de-noising kernel
  Med. Phys., vol. 5, 2230-2244, 2021

2020

• G. Wang, A. Rahmim, and R. N. Gunn
  PET parametric imaging: past, present, and future
  IEEE Trans. Rad. Plas. Med. Sci., vol. 4, pp. 663 – 675, 2020.
• R. Samimi, A. Kamali-Asl, P. Geramifar, J. van den Hoff, and A. Rahmim
  Short-duration dynamic FDG PET imaging: optimization and clinical application
  Physica Medica, vol. 80, pp. 193-200, 2020.
• H. M. K. McGillivray, C. F. Uribe, A. Rahmim, M. M. Muermann, and R. J. Wassersug
  From diagnostics to theranostics—and why better cancer care will always be costly
  BC Med. Journal, vol. 62, pp. 373-379, 2020.
• X. Hou, H. Ma, P. L. Esquinas, C. F. Uribe, S. Tolhurst, F. Bénard, D. Liu, A. Rahmim, and A. Celler
  Impact of image reconstruction method on dose distributions derived from ⁹⁰Y PET images: phantom and liver radioembolization patient studies
  Phys. Med. Biol., vol. 65, pp. 215022, 2020.
• I. Shiri, H. Abdollahi, M. R. Atashzar, A. Rahmim, and H. Zaidi
  A theranostic approach based on radiolabeled antiviral drugs, antibodies and CRISPR associated proteins for early detection and treatment of SARS-CoV-2 disease
  Nucl. Med. Comm., vol. 41, pp. 837-840, 2020.
• I. Shiri, G. Hajianfar, A. Sohrabi, H. Abdollahi, S. P. Shayesteh, P. Geramifar, H. Zaidi, M. Oveisi, and A. Rahmim
  Repeatability of radiomic features in magnetic resonance imaging of glioblastoma: test-retest and image registration analyses
  Med. Phys., vol. 47, pp. 4265-4280, 2020.
• H. Abdollahi, I. Shiri, J. J. Bevelacqua, A. Jafarzadeh, A. Rahmim, H. Zaidi, S. A. R. Mortazavi, and S. M. J. Mortazavi
  Low dose radiation therapy and convalescent plasma: how a hybrid method may maximize benefits for COVID-19 patients
  J. Biomed. Phys. Eng., vol. 10, pp. 387-394, 2020.
• I. Shiri, H. Arabi, P. Geramifar, G. Hajianfar, P. Ghafarian, A. Rahmim, M. R. Ay, and H. Zaidi
  Deep-JASC: joint attenuation and scatter correction in whole-body ¹⁸F-FDG PET using a deep residual network
  Euro. J. Nucl. Med. Mol. Imag., vol. 47, pp. 2533–2548, 2020.
• K. H. Leung, W. Marashdeh, R. Wray, S. Ashrafinia, M. G. Pomper, A. Rahmim, and A. K. Jha
  A physics-guided modular deep-learning based automated framework for tumor segmentation in PET
  Phys. Med. Biol, vol. 65, pp. 245032, 2020.
• I. Shiri, H. Maleki, G. Hajianfar, H. Abdollahi, S. Ashrafinia, M. Hatt, H. Zaidi, M. Oveisi, and A. Rahmim
  Next generation radiogenomics sequencing for prediction of EGFR and KRAS mutation status in NSCLC patients using multimodal imaging and machine learning algorithms (awarded: Molecular Imaging & Biology, 2020 Clinical Paper of the Year)
  Molec. Imag. Biol, vol. 22, pp. 1132-1148, 2020.
• A. Zwanenburg et al.
  The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping
  Radiology, vol. 295, pp. 328–338, 2020.
• M. McNitt-Gray et al.
  Standardization in quantitative imaging: a multi-center comparison of radiomics features from different software packages on digital reference objects and patient datasets
  Tomography, vol. 6, pp. 118-128, 2020.
• M. Soltani, M. Jabbarifar, F. Moradi, and A. Rahmim
  Evaluation of inverse methods for estimation of mechanical parameters in solid tumors
  Biomed. Phys. Eng. Exp., vol. 6, pp. 035027, 2020.
• J. Kang et al.
  Transcranial photoacoustic imaging of NMDA-evoked focal circuit dynamics in the rat hippocampus
  J. Neural Eng., vol. 17, pp. 025001, 2020.
• W. Lv, S. Ashrafinia, J. Ma, L. Lu, and A. Rahmim
  Multi-level multi-modality fusion radiomics: application to PET and CT imaging for prognostication of head and neck cancer
  IEEE J. Biomed. Health Informatics, vol. 24, pp. 2268-2277, 2020.
• M. R. Salmanpour, M. Shamsaei, A. Saberi, I. S. Klyuzhin, J. Tang, V. Sossi, and A. Rahmim
  Machine learning methods for optimal prediction of motor outcome in Parkinson’s disease
  Physica Medica, vol. 69, pp. 233-240, 2020.
• D. Du, H. Feng, W. Lv, S. Ashrafinia, Q. Yuan, Q. Wang, W. Yang, Q. Feng, W. Chen, A. Rahmim, and L. Lu
  Machine learning methods for optimal radiomics-based differentiation between recurrence and inflammation: application to nasopharyngeal carcinoma post-therapy PET/CT images
  Molec. Imag. Biol., vol. 22, pp. 730–738, 2020.
• S. Rezaei, P. Ghafarian, M. Bakhshayesh-Karam, C. F. Uribe, A. Rahmim, S. Sarkar, and M. R. Ay
  The impact of iterative reconstruction protocol, signal-to-background ratio and background activity on measurement of PET spatial resolution
  Japanese J. Radiology, vol. 38, pp. 231–239, 2020.
• G. Mahmoudi, M. R. Ay, A. Rahmim, and H. Ghadiri
  Computationally efficient system matrix calculation techniques in computed tomography iterative reconstruction
  J. Med. Sign. Sens., vol. 10, pp. 1-11, 2020.

2019

• S. P. Rowe, L. B. Solnes, Y. Yin, G. Kitchen, M. A. Lodge, N. A. Karakatsanis, A. Rahmim, M. G. Pomper, and J. P. Leal
  Imager-4D: New software for viewing dynamic PET scans and extracting radiomic parameters from PET data
  J. Digit. Imag., vol. 32, pp. 1071–1080, 2019.
• M. R. Salmanpour, M. Shamsaei, A. Saberi, S. Setayeshi, I. S. Klyuzhin, V. Sossi, and A. Rahmim
  Optimized machine learning methods for prediction of cognitive outcome in Parkinson’s disease
  Comp. Biol. Med., vol. 111, pp. 103347, 2019.
• S. Rezaei, P. Ghafarian, A. K. Jha, A. Rahmim, S. Sarkar, and M. R. Ay
  Joint compensation of motion and partial volume effects by iterative deconvolution method associated with wavelet-based denoising in oncologic PET/CT imaging
  Physica Medica, vol. 68, pp. 52-60, 2019.
• N. Ahmadi, A. Karimian, M. N. Nasrabadi, and A. Rahmim
  Assessment of fetal and maternal radiation absorbed dose in 18F-FDG PET imaging
  Intern. J. Rad. Res., vol. 17, pp. 651-657, 2019.
• G. Mahmoudi, M. R. Fouladi, M. R. Ay, A. Rahmim, and H. Ghadiri
  Sparse-view statistical image reconstruction with improved total variation regularization for X-ray micro-CT imaging
  J. Instrumentation, vol. 14, pp. P08023, 2019.
• I. Shiri, P. Ghafarian, P. Geramifar, K. H. Leung, M. Ghelichoghli, M. Oveisi, A. Rahmim, and M. R. Ay
  Direct attenuation correction of brain PET images using only Emission data via a deep convolutional encoder-decoder (Deep-DAC)
  European Radiology, vol. 29, pp. 6867-6879, 2019.
• J. Kang et al.
  Transcranial recording of electrophysiological neural activity in the rodent brain in vivo using functional photoacoustic imaging of near-infrared voltage-sensitive dye
  Frontiers in Neuroscience, vol. 13, 579, 2019.
• A. Rahmim, M. A. Lodge, N. A. Karakatsanis, V. Y. Panin, Y, Zhou, A. McMillan, S. Cho, H. Zaidi, M. E. Casey, and R. L. Wahl
  Dynamic whole-body PET imaging: principles, potentials and applications
  Euro. J. Nucl. Med. Mol. Imag., vol. 46, pp. 501–518, 2019.
• J. Tang, B. Yang, N. N. Shenkov, I. S. Klyuzhin, S. Fotouhi, E. Davoodi-Bojd, L. Lu, H. Soltanian-Zadeh, V. Sossi, and A. Rahmim
  Artificial neural network based prediction of outcome in Parkinson’s disease patients using DaTscan SPECT imaging features
  Med. Imag. Biol, vol. 21, pp. 1165-1173, 2019.
• A. Rahmim, K. P. Bak-Fredslund, S. Ashrafinia, L. Lu, C. R. Schmidtlein, R. M. Subramaniam, A. Morsing, S. Keiding, J. Horsager, and O. L. Munk
  Prognostic modeling for patients with colorectal liver metastases incorporating FDG PET radiomic features
  Euro. J. Radiol., vol. 113, pp. 101-109, 2019.
• W. Lv, Q. Yuan, Q. Wang, J. Ma, Q. Feng, W. Chen, A. Rahmim, and L. Lu
  Radiomics analysis of PET and CT components of PET/CT imaging integrated with clinical parameters: application to prognosis for nasopharyngeal carcinoma
  Mol. Imag. Biol, vol. 21, pp. 954-964, 2019.
• A. Ketabi, P. Ghafarian, M. A. Mosleh-Shirazi, S. R. Mahdavi, A. Rahmim, and M. R. Ay
  Impact of image reconstruction methods on quantitative accuracy and variability of FDG-PET volumetric and textural measures in solid tumors
  Euro. Radiol., vol. 29, pp. 2146–2156, 2019.
• I. S. Klyuzhin, J. F. Fu, N. Shenkov, A. Rahmim, and V. Sossi
  Use of generative disease models for analysis and selection of radiomic features in PET
  IEEE Trans. Rad. Plas. Med. Sci., vol. 3, pp. 178-191, 2019.

2018

• I. S. Klyuzhin, J. F. Fu, A. Hong, M. Sacheli, N. Shenkov, M. Matarazzo, A. Rahmim, A. J. Stoessl, and V. Sossi
  Data-driven, voxel-based analysis of brain PET images: application of PCA and LASSO Methods to visualize and quantify patterns of neurodegeneration
  PLoS ONE, vol. 13, pp. e0206607, 2018.
• S. Shojaeilangari, C. R. Schmidtlein, A. Rahmim, and M. R. Ay
  Recovery of missing data in partial geometry PET scanners: compensation in projection space versus image space
  Med. Phys, vol. 45, pp. 5437-5449, 2018.
• Y. Zhu, A. K. Jha, D. F. Wong, and A. Rahmim
  Image reconstruction in fluorescence molecular tomography with sparsity-initialized maximum-likelihood expectation maximization
  Biomed. Opt. Exp., vol. 9, pp. 3106-3121, 2018.
• M. A. Lodge, J. P. Leal, A. Rahmim, J. J. Sunderland, and E. C. Frey
  Measuring PET spatial resolution using a cylinder phantom positioned at an oblique angle
  J. Nucl. Med., vol. 59, pp. 1768-1775, 2018.
• N. Salehi, A. Rahmim, E. Fatemizadeh, A. Akbarzadeh, M. H. Farahani, S. Farzanefar, and M. R. Ay
  Cardiac contraction motion compensation in gated myocardial perfusion SPECT: A comparative study
  Physica Medica, vol. 49, pp. 77-82, 2018.
• H. Ghadiri, M. R. Fouladi, and A. Rahmim
  An analysis scheme for investigation of effects of various parameters on signals in acoustic-resolution photoacoustic microscopy of mice brain: a simulation study
  Frontiers in Biomed. Tech., vol. 4, pp. 59-69, 2018.
• W. Lv, Q. Yuan, Q. Wang, J. Ma, J. Jiang, W. Yang, Q. Feng, W. Chen, A. Rahmim, and L. Lu
  Robustness versus disease differentiation when varying parameter settings in radiomics features: application to nasopharyngeal PET/CT
  Euro. Radiol., vol. 8, pp. 3245–3254, 2018.
• A. K. Jha, Y. Zhu, S. Arridge, D. F. Wong, and A. Rahmim
  Incorporating reflection boundary conditions in the Neumann series radiative transport equation: application to photon propagation and reconstruction in diffuse optical imaging
  Biomed. Opt. Exp., vol. 9, pp. 1389-1407, 2018.
• N. Ahmadi, M. N. Nasrabadi, A. Karimian, and A. Rahmim
  Estimation of fetal absorbed dose from low-dose attenuation-correction CT in PET/CT imaging by using the Body Builder Phantom
  Iranian J. Rad. Safety & Meas., vol. 6, pp. 45-53, 2018.
• A. Emami, H. Ghadiri, A. Rahmim, and M. R. Ay
  A novel dual energy method for enhanced quantitative computed tomography
  J. Instrumentation, vol. 13, pp. P01030, 2018.
• Z. Etemadi, P. Ghafarian, A. Bitarafan-Rajabi, H. Malek, A. Rahmim, and M. R. Ay
  Is correction for metallic artefacts mandatory in cardiac SPECT/CT imaging in the presence of pacemaker and implantable cardioverter defibrillator leads?
  Iranian J. Nucl. Med., vol. 26, pp. 35-46, 2018.
• L. Lu, X. Ma, H. Mohy-ud-Din, J. Ma, Q. Feng, A. Rahmim, and W. Chen
  Enhancement of dynamic myocardial perfusion PET images based on low-rank plus sparse decomposition
  Comp. Meth. Prog. Biomed., vol. 154, p. 57-69, 2018.

2017

• A. Rahmim, P. Huang, N. Shenkov, S. Fotouhi, E. Davoodi-Bojd, L. Lu, Z. Mari, H. Soltanian-Zadeh, and V. Sossi
  Improved prediction of outcome in Parkinson’s disease using radiomics analysis of longitudinal DAT SPECT Images (supplement)
  NeuroImage: Clinical, vol. 16, pp. 539-544, 2017.
• R. Sharifpour, P. Ghafarian, A. Rahmim, and M. R. Ay
  Quantification and reduction of respiratory induced artifacts in positron emission tomography/computed tomography using the time-of-flight technique
  Nucl. Med. Comm., vol. 38, pp. 948–955, 2017.
• M. Khazaee, A. Kamali-Asl, P. Geramifar, and A. Rahmim
  Low-dose 90Y PET/CT imaging optimized for lesion detectability and quantitative accuracy: a phantom study to assess feasibility of pre-therapy imaging to plan therapeutic dose
  Nucl. Med. Comm., vol. 38, pp. 985–997, 2017.
• R. F. Gottesman et al.
  Association between midlife vascular risk factors and estimated brain amyloid deposition
  JAMA, vol. 317, pp. 1443-1450, 2017.
• S. Ashrafinia, H. Mohy-ud-Din, N. A. Karakatsanis, A. K. Jha, M. Casey, D. J. Kadrmas, and A. Rahmim
  Generalized PSF modeling for optimized quantitation in PET imaging
  Phys. Med. Biol., vol. 62, pp. 5149-5179, 2017.
• I. Shiri, A. Rahmim, P. Ghaffarian, P. Geramifar, H. Abdollahi, and A. Bitarafan-Rajabi
  The impact of image reconstruction settings on 18F-FDG PET radiomic features: multi-scanner phantom and patient studies
  European Radiology, vol. 27, pp. 4498-4509, 2017.
• B. A. Anderson, H. Kuwabara, D. F. Wong, J. Roberts, A. Rahmim, J. R. Brasic, and S. M. Courtney
  Linking dopaminergic reward signals to the development of attentional bias: a positron emission tomographic study
  NeuroImage, vol. 157, pp. 27-33, 2017.
• H. K. Zhang, P. Yan, J. Kang, D. Abou, H. N. D. Le, A. K. Jha, D. Thorek, J. Kang, A. Rahmim, D. F. Wong, E. M. Boctor, and L. M. Loew
  Listening to membrane potential: photoacoustic voltage-sensitive dye recording
  J. Biomed. Optics, vol. 22, pp. 045006, 2017.
• J. Tang, X. Wang, X. Gao, P. Segars, M. Lodge, and A. Rahmim
  Enhancing ejection fraction measurement through 4D respiratory motion compensation in cardiac PET imaging (also see featured news article @ medicalphysicsweb)
  Phys. Med. Biol., vol. 62, pp. 4496–4513, 2017.
• N. Ahmadi, M. N. Nasrabadi, A. Karimian, and A. Rahmim
  A TLD based method to estimate bowtie filter shape in PET/CT
  Intern. J. Rad. Res., vol. 15, pp. 383-390, 2017.
• S. K. Gerdekoohi, N. Vosoughi, K. Tanha, M. Asadi, P. Ghafarian, A. Rahmim, and M. R. Ay
  Implementation of absolute quantification in small-animal SPECT imaging: phantom and animal studies
  J. Appl. Clin. Med. Phys., vol. 18, pp. 215-223, 2017.
• P. Knoll, A. Rahmim, S. Gültekin, M. Samal, M. Ljungberg, S. Mirzaei, P. Segars, and B. Szczupak
  Improved scatter correction with factor analysis for planar and SPECT imaging
  Rev. Sci. Instrum., vol. 88, pp. 094303, 2017.
• J. Tang, X. Wang, and A. Rahmim
  MRI assisted dual motion correction for myocardial perfusion defect detection in PET imaging
  Med. Phys., vol. 44, pp. 4536-4547, 2017.
• A. K. Jha, E. Mena, B. Caffo, S. Ashrafinia, A. Rahmim, E. Frey, and R. M. Subramaniam
  A practical no-gold-standard evaluation framework for quantitative imaging methods: application to lesion segmentation in PET
  J. Med. Imag., vol. 4, pp. 011011, 2017.
• E. Mena, M. Taghipour, S. Sheikhbahaei, A. K. Jha, A. Rahmim, L. Solnes, and R. M. Subramaniam
  Value of intratumoral metabolic heterogeneity and quantitative 18F-FDG PET/CT parameters to predict prognosis in patients with HPV-positive primary oropharyngeal squamous cell carcinoma
  Clinic. Nucl. Med., vol. 42, p. e227-e234, 2017.
• M. Soltani, M. Sefidgar, H. Bazmara, M. E. Casey, R. M. Subramaniam, R. L. Wahl, and A. Rahmim
  Spatiotemporal distribution modeling of PET tracer uptake in solid tumors
  Ann. Nucl. Med., vol. 31, pp. 109-124, 2017.
• N. Zeraatkar, A. Rahmim, S. Sarkar, and M. R. Ay
  Development and evaluation of image reconstruction algorithms for a novel desktop SPECT system
  Asia Oceania J. Nucl. Med. Biol., vol. 5, pp. 120-133, 2017.
• M. Soufi, A. Kamali-Asl, P. Geramifar, and A. Rahmim
  A novel framework for automated segmentation and labeling of homogeneous versus heterogeneous lung tumors in [18F]FDG PET imaging
  Molec. Imag. Biol., vol. 19, pp. 456-468, 2017.

2016

• A. Rahmim, Y. Salimpour, S. Jain, S. Blinder, I. S. Klyuzhin, G. S. Smith, Z. Mari, and V. Sossi
  Application of texture analysis to DAT SPECT imaging: relationship to clinical assessments
  NeuroImage: Clinical, vol. 12, pp. e1-e9, 2016.
• R. F. Gottesman et al.
  The ARIC-PET amyloid imaging study: brain amyloid differences by age, race, sex, and APOE (supplement; also see editorial @ Neurology)
  Neurology, vol. 87, pp. 473-480, 2016.
• L. Lu, W. Lv, J. Jiang, J. Ma, Q. Feng, A. Rahmim, and W. Chen
  Robustness of radiomic features in [11C]Choline and [18F]FDG PET/CT imaging of nasopharyngeal carcinoma: impact of segmentation and discretization (supplement)
  Molec. Imag. Biol., vol. 18, pp. 935-945, 2016.
• N. A. Karakatsanis, M. E. Casey, M. A. Lodge, A. Rahmim, and H. Zaidi
  Whole-body direct 4D parametric PET imaging employing nested generalized Patlak expectation-maximization reconstruction (also see 1- featured news article @ medicalphysicsweb; 2- top 10 most popular PMB articles in 2016)
  Phys. Med. Biol., vol. 61, pp. 5456–5485, 2016.
• F. Aalamifar, R. Seifabadi,  M. Bernardo, Ayele H. Negussie, B. Turkbey, M. Merino, P. Pinto, A. Rahmim, B. J. Wood, and E. M. Boctor
  Ultrasound tomosynthesis: a new paradigm for quantitative imaging of the prostate
  Medical Image Computing and Computer Assisted Intervention (MICCAI), pp. 577-584, 2016.
• N. Zeraatkar, M. H. Farahani, A. Rahmim, S. Sarkar, and M. R. Ay
  Design and assessment of a novel SPECT system for desktop open-gantry imaging of small animals: a simulation study
  Med. Phys., vol. 43, pp. 2581-2597, 2016.
• A. Rahmim, C. R. Schmidtlein, A. Jackson, S. Sheikhbahaei, C. Marcus, S. Ashrafinia, M. Soltani, and R. M. Subramaniam
  A novel metric for quantification of homogeneous and heterogeneous tumors in PET for enhanced clinical outcome prediction
  Phys. Med. Biol., vol. 61, pp. 227-242, 2016.
• M. Soufi, A. R. Kamali-Asl, P. Geramifar, M. Abdoli, and A. Rahmim
  Combined fuzzy logic and random walker algorithm for PET image tumor delineation
  Nucl. Med. Comm., vol. 37, 171-181, 2016.
• B. A. Anderson, H. Kuwabara, D. F. Wong, E. G. Gean, A. Rahmim, J R. Brasic , N. George, B. Frolov, S. M. Courtney, and S. Yantis
  The role of dopamine in value-based attentional orienting (also see commentary @ Johns Hopkins Magazine)
  Current Biology, vol. 26, pp. 550-555, 2016.
• S. Sheikhbahaei, R. Wray, B. Young, E. Mena, M. Taghipour, A. Rahmim, and R. M. Subramaniam
  18F-FDG-PET/CT therapy assessment of locally advanced pancreatic adenocarcinoma: impact on management and utilization of quantitative parameters for patient survival prediction
  Nucl. Med. Comm., vol. 37, pp. 231-238, 2016.
• S. Sheikhbahaei, C. Marcus, R. Wray, A. Rahmim, M. A. Lodge, and R. M. Subramaniam
  Impact of point-spread function reconstruction on quantitative 18F-FDG-PET/CT imaging parameters and inter-reader reproducibility in solid tumors
  Nucl. Med. Comm., vol. 37, pp. 288-296, 2016.
• H. Bazmara, M. Soltani, M. Sefidgar, M. Bazargan, M. Mousavi, A. Rahmim
  Blood flow and endothelial cell phenotype regulation during sprouting angiogenesis
  Med. Biol. Eng. Comp., vol. 54, pp. 547-558, 2016.
• R. Wray, C. Marcus, S. Sheikhbahaei, E. Zan, R. Ferraro, A. Rahmim, and R. M. Subramaniam
  Therapy response assessment and patient outcomes in head and neck squamous cell carcinoma: FDG PET Hopkins criteria versus residual neck node size and morphologic features
  Am. J. Roentgenology, vol. 207, pp. 641-647, 2016.

2015

• N. A. Karakatsanis, Y. Zhou, M. A. Lodge, M. E. Casey, R. L. Wahl, H. Zaidi, and A. Rahmim
  Generalized whole-body Patlak parametric imaging for enhanced quantification in clinical PET
  Phys. Med. Biol., vol. 40, pp. 8643-8673, 2015.
• H. Mohy-ud-Din, M. A. Lodge, and A. Rahmim
  Quantitative myocardial perfusion PET parametric imaging at the voxel-level
  Phys. Med. Biol., vol. 60, pp. 6013-6037, 2015.
• H. Mohy-ud-Din, N. A. Karakatsanis, W. Willis, A. K. Tahari, D. F. Wong, and A. Rahmim
  Intra-frame motion compensation in multi-frame brain PET imaging
  Frontiers in Biomed. Tech., vol. 2, pp. 366-379, 2015.
• C. Marcus, A. Antoniou, A. Rahmim, P. Ladenson, and and R. M. Subramaniam
  Fluorodeoxyglucose positron emission tomography/computerized tomography in differentiated thyroid cancer management: Importance of clinical justification and value in predicting survival
  J. Med. Imag. Rad. Onc., vol. 59, 281-288, 2015.
• J. Hossain, Y. Du, J. Links, A. Rahmim, N. Karakatsanis, A. Akhbardeh, J. Lyons, and E. Frey
  Estimation of dynamic time activity curves from dynamic cardiac SPECT imaging
  Phys. Med. Biol., vol. 60, pp. 3193-3208, 2015.
• H. Bazmara, M. Soltani, M. Sefidgar, M. Bazargan, M. Mousavi, A. Rahmim
  The vital role of blood flow-induced proliferation and migration in capillary network formation in a multiscale model of angiogenesis
  PLoS ONE, vol. 10, pp. e0128878, 2015.
• L. Lu, J. Ma, Q. Feng, W. Chen, and A. Rahmim
  Anatomy-guided brain PET imaging incorporating a joint prior model
  Phys. Med. Biol., vol. 60, pp. 2145-66, 2015.
• J. Tang and A. Rahmim
  Anatomy assisted PET image reconstruction incorporating multi-resolution joint entropy
  Phys. Med. Biol., vol. 60, pp. 31-48, 2015.

2014

• F. A. Kotasidis, C. Tsoumpas, and A. Rahmim
  Advanced kinetic modelling strategies: towards adoption in clinical PET imaging (invited review article)
  Clinic. & Transl. Imaging, vol. 2, pp. 219-237, 2014.
• A. Rahmim, A. K. Tahari, and T. H. Schindler
  Towards quantitative myocardial perfusion PET in the clinic
  J. Am. Coll. Radiol., vol. 11, pp. 429-432, 2014.
• A. K. Tahari, P. E. Bravo, A. Rahmim, F. M. Bengel, and Z. Szabo
  Initial human experience with Rubidium-82 renal PET/CT imaging
  J. Med. Imag. Rad. Onc., vol. 58, pp. 25-31, 2014.
• H. Ghadiri, M. B. Shiran, H. R. Soltanian-Zadeh, A. Rahmim, H. Zaidi, and M. R. Ay
  Derivation of attenuation map for attenuation correction of PET data in the presence of nanoparticulate contrast agents using spectral CT imaging
  Ann. Nucl. Med., vol. 28, pp. 559-570, 2014.
• P. Ghafarian, M. R. Ay, A. Fard-Esfahani, A. Rahmim, and H. Zaidi
  Quantification of PET and CT misalignment errors due to bulk motion in cardiac PET/CT imaging: phantom and clinical studies
  Frontiers in Biomed. Tech., vol. 1, pp. 159-167, 2014.
• Z. Shahpouri, A. K. Asl, A. B. Rajabi, J. Hossain, S. M. Entezarmahdi, S. Mohseni, N. Yaghoobi, and A. Rahmim
  A comparative assessment of dynamic and conventional Thallium-201 SPECT myocardial perfusion imaging: Monte Carlo simulations and case studies
  Frontiers in Biomed. Tech., vol. 1, pp. 91-102, 2014.
• N. Zeraatkar, S. Sajedi, M. H. Farahani, H. Arabi, S. Sarkar, P. Ghafarian, A. Rahmim, and M. R. Ay
  Resolution-recovery-embedded image reconstruction for a high-resolution animal SPECT
  Physica Medica, vol. 30, pp. 774-781, 2014.
• S. Sajedi, N. Zeraatkar, V. Moji, M. H. Farahani, S. Sarkar, H. Arabi, B. Tehmoorian, P. Ghafarian, A. Rahmim, and M. R. Ay
  Design and development of a high resolution animal SPECT scanner dedicated for rat and mouse imaging
  Nucl. Inst. & Methods in Phys. Research A, vol. 741, pp. 169-176, 2014.
• A. Rahmim, J. Tang, and H. Mohy-ud-Din
  Direct 4D parametric imaging in dynamic myocardial perfusion PET
  Frontiers in Biomed. Tech., vol. 1, pp. 4-13, 2014.
• N. A. Karakatsanis, G. Loudos, A. Rahmim, and K. Nikita
  Monte-Carlo based characterization of the counting rate (NECR) response for personalized optimization of the administered activity in clinical PET imaging
  Frontiers in Biomed. Tech., vol. 1, pp. 14-34, 2014.

2013

• A. M. Alessio, A. Rahmim, and C. G. Orton (Moderator)
  Resolution modeling enhances PET imaging (invited point/counterpoint) (erratum)
  Med. Phys., vol. 40, pp. 120601, 2013.
• A. Rahmim and J. Tang
  Noise propagation in resolution modeled PET imaging and its impact on detectability
  Phys. Med. Biol., vol. 58, pp. 6945-6968, 2013.
• N. A. Karakatsanis, M. A. Lodge, A. K. Tahari, Y. Zhou, R. L. Wahl, and A. Rahmim
  Dynamic whole body PET parametric imaging: I. concept, acquisition protocol optimization and clinical application
  Phys. Med. Biol., vol. 58, pp. 7391-7418, 2013.
• N. A. Karakatsanis, M. A. Lodge, Y. Zhou, R. L. Wahl, and A. Rahmim
  Dynamic whole body PET parametric imaging: II. task-oriented statistical estimation
  Phys. Med. Biol., vol. 58, pp. 7419-7445, 2013.
• A. Rahmim, J. Qi, and V. Sossi
  Resolution modeling in PET imaging: theory, practice, benefits, and pitfalls (review article)
  Med. Phys., vol. 40, pp. 064301, 2013.
• A. Rahmim, J. Tang, and H. Zaidi
  Four-dimensional image reconstruction strategies in cardiac-gated and respiratory-gated PET imaging (invited review article)
  PET Clinics, vol. 8, pp. 51-67, 2013.
• P. Geramifar, M. S. Zafarghandi, P. Ghafarian, A. Rahmim, and M. R. Ay
  Respiratory-induced errors in tumor quantification and delineation in CT attenuation corrected PET images: effects of tumor size, tumor location and respiratory trace – a simulation study using the 4D XCAT Phantom
  Molec. Imag. Biol., vol. 15, pp. 655-665, 2013.
• A. Mehranian, M. R. Ay, A. Rahmim, and H. Zaidi
  X-ray CT metal artifact reduction using wavelet domain L0 sparse regularization
  IEEE Trans. Med. Imag., vol. 32, pp. 1707-1722, 2013.
• A. Mehranian, M. R. Ay, A. Rahmim, and H. Zaidi
  3D prior image constrained projection completion for X-ray CT metal artifact reduction
  IEEE Trans. Nucl. Sci., vol. 60, pp. 3318-3332, 2013.
• A. Mehranian, A. Rahmim, M. R. Ay, F. Kotasidis, and H. Zaidi
  An ordered-subsets proximal preconditioned gradient algorithm for edge-preserving PET image reconstruction
  Med. Phys., vol. 40, pp. 052503, 2013.
• S. Sajedi, A. Kamali-Asl, M. R. Ay, M. H. Farahani, and A. Rahmim
  A novel non-linear recursive filter design for extracting high rate pulse features in nuclear medicine imaging and spectroscopy
  Med. Eng. Phys., vol. 35, pp. 754-764, 2013.
• A. Mehranian, H. S. Rad, A. Rahmim, M. R. Ay, and H. Zaidi
  Smoothly clipped absolute deviation (SCAD) regularization for compressed sensing MRI using an augmented Lagrangian scheme
  Mag. Reson. Imag., vol. 31, pp. 1399-1411, 2013.

2012

• L. Lu, N. A. Karakatsanis, J. Tang, W. Chen, and A. Rahmim
  3.5D dynamic PET image reconstruction incorporating kinetics-based clusters
  Phys. Med. Biol., vol. 57, pp. 5035-5055, 2012.
• A. Rahmim, Y. Zhou, J. Tang, L. Lu, V. Sossi, and D. F. Wong
  Direct 4D parametric imaging for linearized models of reversibly binding PET tracers using generalized AB-EM reconstruction
  Phys. Med. Biol., vol. 57, pp. 733-755, 2012.

2011

• N. Zeraatkar, M. R. Ay, P. Ghafarian, S. Sarkar, P. Geramifar, and A. Rahmim
  Monte Carlo-based evaluation of inter-crystal scatter and penetration in the PET subsystem of three GE Discovery PET/CT scanners
  Nucl. Inst. & Methods in Phys. Research A, vol. 659, pp. 508-514, 2011.
• P. Geramifar, M. R. Ay, M. S. Zafarghandi, S. Sarkar, G. Loudos, and A. Rahmim
  Investigation of time-of-flight benefits in an LYSO-based PET/CT scanner: A Monte Carlo study using GATE
  Nucl. Inst. & Methods in Phys. Research A, vol. 641, pp. 121-127, 2011.
• J. Tang, W. P. Segars, T-S. Lee, X. He, A. Rahmim, and B. M. W. Tsui
  Quantitative study of cardiac motion estimation and abnormality classification in emission computed tomography
  Med. Eng. Phys., vol. 33, p. 563-572, 2011.
• M. R. Ay, M. Shirmohammad, S. Sarker, A. Rahmim, and H. Zaidi
  Comparative assessment of energy mapping approaches in CT-based attenuation correction for PET
  Molec. Imag. Biol., vol. 13, pp. 187-198, 2011.
• P. Ghafarian P, S. M. Aghamiri, M. R. Ay, A. Rahmim, T. H. Schindler, O. Ratib, and H. Zaidi
  Is metal artefact reduction mandatory in cardiac PET/CT imaging in the presence of pacemaker and implantable cardioverter defibrillator leads?
  Eur. J. Nucl. Med. Mol. Imaging, vol. 38, p. 252-262, 2011.

2010

• J. Tang, H. Kuwabara, D. F. Wong, and A. Rahmim
  Direct 4D reconstruction of parametric images incorporating anato-functional joint entropy
  Phys. Med. Biol., vol. 55, 4261-4272, 2010.
• P. Ghafarian P, S. M. Aghamiri, M. R. Ay, B. Fallahi, A. Rahmim, T. H. Schindler, O. Ratib, and H. Zaidi
  Coronary calcium score scan-based attenuation correction in cardiovascular PET imaging
  Nucl. Med. Commun., vol. 31, p. 780-787, 2010.
• D. F. Wong et al.
  Quantification of cerebral cannabinoid receptors subtype 1 (CB1) in healthy subjects and schizophrenia by the novel PET radioligand [11C]OMAR
  NeuroImage, vol. 52, pp. 1505-1513, 2010.
• J.-C. Cheng, S. Blinder, A. Rahmim, and V. Sossi
  A scatter calibration technique for dynamic brain imaging in high resolution PET
  IEEE Trans. Nucl. Sci., vol. 57, pp. 225-233, 2010.
• M. A. Lodge, A. Rahmim, and R. L. Wahl
  Simultaneous measurement of noise and spatial resolution in PET phantom images
  Phys. Med. Biol., vol. 55, pp. 1069-1081, 2010.
• M. S. Ardestani et al.
  Novel and facile methods for the synthesis of DTPA-mono-amide: a new completely revised strategy in radiopharmaceutical chemistry
  J. Radioanalytical & Nucl. Chem., vol. 283, pp. 447-455, 2010.

2009

• A. Rahmim, J. Tang, and H. Zaidi
  Four-dimensional (4D) image reconstruction strategies in dynamic PET: beyond conventional independent frame reconstruction (review article)
  Med. Phys., vol. 36, pp. 3654-3670, 2009.
• J. Tang and A. Rahmim
  Bayesian PET image reconstruction incorporating anato-functional joint entropy
  Phys. Med. Biol., vol. 54, pp. 7063-7075, 2009.
• J. Tang, A. Rahmim, R. Lautamaki, M. A. Lodge, F. M. Bengel, and B. M. W. Tsui
  Optimization of Rb-82 PET acquisition and reconstruction protocols for myocardial perfusion defect detection
  Phys. Med. Biol., vol. 54, pp. 3161-3171, 2009.
• M. A. Lodge, A. Rahmim, and R. L. Wahl
  A practical, automated quality assurance method for measuring spatial resolution in positron emission tomography
  J. Nucl. Med., vol. 50, pp. 1307-1314, 2009.
• P. Geramifar, M. R. Ay, M. S. Zafarghandi, G. Loudos, and A. Rahmim
  Performance comparison of four commercial GE Discovery PET/CT scanners: a Monte Carlo study using GATE
  Iranian J. Nucl. Med., vol. 17, pp. 26-33, 2009.
• S. Lashkari, M. R. Ay, S. Sarkar, and A. Rahmim
  The influence of crystal size and material on intercrystal scattering and parallax in PET block detectors: a Monte Carlo study
  Iranian J. Med. Phys., vol. 5, pp. 67-76, 2009.

2008

• A. Rahmim, J. Tang, M. A. Lodge, S. Lashkari, M. R. Ay, R. Lautamaki, B. M. W. Tsui, and F. M. Bengel
  Analytic system matrix resolution modeling in PET: an application to Rb-82 cardiac imaging
  Phys. Med. Biol., vol. 53, pp. 5947-5965, 2008.
• A. Rahmim, K. Dinelle, J.-C. Cheng, M. A. Shilov, W. P. Segars, S. C. Lidstone, S. Blinder, O. G. Rousset, H. Vajihollahi, B. M. W. Tsui, D. F. Wong, and V. Sossi
  Accurate event-driven motion compensation in high-resolution PET incorporating scattered and random events
  IEEE Trans. Med. Imag., vol. 27, pp. 1018-1033, 2008.
• A. Rahmim, J.-C. Cheng, K. Dinelle, M. A. Shilov, W. P. Segars, O. G. Rousset, B. M. W. Tsui, D. F. Wong, and V. Sossi
  System matrix modelling of externally tracked motion
  Nucl. Med. Comm., vol. 29, pp. 574-581, 2008.
• A. Rahmim and H. Zaidi
  PET versus SPECT: strengths, limitations and challenges (invited review article)
  Nucl. Med. Comm., vol. 29, pp. 193-207, 2008.
• O. G. Rousset, D. L. Collins, A. Rahmim, and D. F. Wong
  Design and implementation of an automated partial volume correction in PET: application to dopamine receptor quantification in the normal human striatum
  J. Nucl. Med., vol. 49, pp. 1097-1106, 2008.
• K. M. Wilcox, Y. Zhou, D. F. Wong, M. Alexander, A. Rahmim, J. Hilton, and M. R. Weed
  Blood levels and DA transporter occupancy of orally administered methylphenidate in juvenile rhesus monkeys measured by high resolution PET
  Synapse, vol. 62, pp. 950-952, 2008.
• M. Shirmohammad, M. R. Ay, S. Sarkar, and A. Rahmim
  Comparing 511keV attenuation maps obtained from different energy mapping methods for CT based attenuation correction of PET data
  Iranian J. Med. Phys., vol. 5, pp. 23-34, 2008.
• H. F. Moghaddam, M. S. Ardestani, M. Saffari, L. Navidpour, A. Shafiee, and A. Rahmim
  Dopaminergic but not glutamatergic neurotransmission is increased in the striatum after selective cyclooxygenase-2 inhibition in normal and hemiparkinsonian rats
  Basic & Clinical Pharmacology & Toxicology, vol. 103, pp. 293-296, 2008.

2007

• A. Rahmim, O. G. Rousset, and H. Zaidi
  Strategies for motion tracking and correction in PET (invited review article)
  PET Clinics, vol. 2, pp. 251-266, 2007.
• O. G. Rousset, A. Rahmim, A. Alavi, and H. Zaidi
  Partial volume correction strategies in PET (invited review article)
  PET Clinics, vol. 2, pp. 235-249, 2007.
• J.-C. Cheng, A. Rahmim, S.Blinder, M-L. Camborde, K. Raywood, and V Sossi
  A scatter corrected list-mode reconstruction and a practical scatter/random approximation technique for dynamic PET imaging
  Phys. Med. Biol, vol. 52, pp. 2089-2106, 2007.
• S. Sarkar, M. A. Oghabian, I. Mohammadi, A. Mohammadpur, and A. Rahmim
  A linogram/sinogram cross-correlation method for motion correction in planar and SPECT imaging
  Trans. Nucl. Sci., vol. 54, pp. 71-79, 2007.

2006

• A. Rahmim and R. L. Wahl
  An overview of clinical PET/CT (invited review article)
  Iranian J. Nucl. Med., vol. 14, pp. 1-14, 2006
• A. Horti, H. Fan, H. Kuwabara, J. Hilton, H. Ravert, D. P. Holt, M. Alexander, A. Kumar, A. Rahmim, U. Scheffel, D. F. Wong, and R. F. Dannals
  11C-JHU75528: a radiotracer for PET imaging of CB1 cannabinoid receptors
  J. Nucl. Med., vol. 47, pp. 1689-1696, 2006.

2005

• A. Rahmim, J. C. Cheng, S. Blinder, M-L. Camborde, and V. Sossi
  Statistical dynamic image reconstruction in state-of-the-art high resolution PET
  Phys. Med. Biol., vol. 50, pp. 4887-4912, 2005.
• V. Sossi, M-L. Camborde, G. Tropini, D. Newport, A. Rahmim, D. J. Doudet, and T. J. Ruth
  The influence of measurement uncertainties on the evaluation of the distribution volume ratio and binding potential in rat studies on a microPET R4: a phantom study
  Phys. Med. Biol., vol. 50, pp. 2859-2869, 2005.

2004

• A. Rahmim, M. Lenox, A. J. Reader, C. Michel. Z. Burbar, T. J. Ruth, and V. Sossi
  Statistical list-mode image reconstruction for the high resolution research tomograph
  Phys. Med. Biol., vol. 49, pp. 4239-4258, 2004.
• A. Rahmim, P. Bloomfield, S. Houle, M. Lenox, C. Michel, K. R. Buckley, T. J. Ruth, and V. Sossi
  Motion compensation in histogram-mode and list-mode EM reconstructions: beyond the event-driven approach
  IEEE Trans. Nucl. Sci., vol. 51, pp. 2588-2596, 2004.
• V. Sossi, K. R. Buckley, P. Piccioni, A. Rahmim, M.-L. Camborde, E. Strome, S. Lapi, and T. J. Ruth
  Printed sources for positron emission tomography (PET)
  IEEE Trans. Nucl. Sci., vol. 52, pp. 114-118, 2004.