Marzieh S. Tahaei has a M.Sc. degree in Artificial Intelligence (Computer Engineering) from Sharif University of Technology. She is currently a PhD candidate in Biomedical Engineering at McGill Univesity under the supervision of Prof. Louis Collins and Prof. Andrew Reader. Her thesis focuses on Robust Neurological Parameter Estimation from PET Data. Her research interests include medical imaging reconstruction and processing, machine learning and complex brain networks.
Tahaei, Marzieh S, Andrew J. Reader, “Patch-based image reconstruction for PET using prior-image derived dictionaries.” Physics in Medicine and Biology, vol 61 , pp. 6833–6855,2016.
Tahaei, Marzieh S., Mahdi Jalili, and Maria G. Knyazeva. “Synchronizability of EEG-based functional networks in early Alzheimer’s disease.” Neural Systems and Rehabilitation Engineering, IEEE Transactions on 20, no. 5 , pp: 636-641, 2012.
Tahaei, Marzieh S., and Andrew J. Reader. “Combining different variance reduction approaches for PET image reconstruction.” In 2014 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), pp. 1-6. IEEE, 2014.
Tahaei, Marzieh S., and Andrew J. Reader. “A novel methods for incorporating MR-based dictionaries in PET reconstruction”. International Conference on Medical Image Computing and Computer Assisted Intervention. MICCAI, 2015 Workshop on Computational Methods for Molecular Imaging.
Reader, Andrew J., Marzieh S. Tahaei, Arman Rahmim, Sune H. Keller, Stephan Blinder, Merence Sibomana, and Jean-Paul Soucy. “Multi-centre assessment of HRRT image uniformity via 68 Ge and 18 F cylindrical and anthropomorphic phantoms.” In Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 2013 IEEE, pp. 1-8. IEEE, 2013.
Reader, Andrew J., Marzieh S. Tahaei, Reda Bouhachi, Stefania Matei, Ron Mio, and Jean-Paul Soucy. “Evaluation of the HRRT and the HR+ for the task of relative region analysis using a realistic head and brain phantom.” In Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 2013 IEEE, pp. 1-7. IEEE, 2013.
Shaban, Amirreza, Hamid R. Rabiee, Marzieh S. Tahaei, and Erfan Salavati. “Nonlinear Unsupervised Feature Learning: How Local Similarities Lead to Global Coding.” In Data Mining Workshops (ICDMW), 2012 IEEE 12th International Conference on, pp. 506-513. IEEE, 2012.
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