Postdoc - Dept. of Radiology, Harvard Medical School and Massachusetts General Hospital
PhD - Biomedical Engineering, Technion – Israel Institute of Technology
MSc (Cum Laude) - Biomedical Engineering, Ben Gurion University of the Negev, Israel
BSc (Cum Laude) - Biomedical Engineering, Ben Gurion University of the Negev, Israel
Medical Imaging, MRI, AI, Machine Learning, Deep Learning, Cancer, Neurological Disorders, Neuroscience, Molecular Imaging
O. Perlman, H. Ito, K. Herz, N. Shono, H. Nakashima, M. Zaiss, E. A. Chiocca, O. Cohen, M. S. Rosen, C. T. Farrar, ”Quantitative imaging of apoptosis following oncolytic virotherapy by magnetic resonance fingerprinting aided by deep learning,” Nature Biomedical Engineering, Vol. 6, pp. 648-657, 2022. https://www.nature.com/articles/s41551-021-00809-7
O. Perlman*, B. Zhu*, M. Zaiss, M. S. Rosen, C. T. Farrar, ”An End-to-End AI-Based Framework for Automated Discovery of Rapid CEST/MT MRI Acquisition Protocols and Molecular Parameter Quantification (AutoCEST),” Magnetic Resonance in Medicine, Vol. 87, pp. 2792-2810, 2022.
∗ Equal contribution. Highlighted by the journal - included in the Editor’s Pick List.
https://onlinelibrary.wiley.com/doi/10.1002/mrm.29173
O. Perlman, H. Ito, A. A. Gilad, M. T. McMahon, E. A. Chiocca, E. H. Nakashima, C. T. Farrar, ”Redesigned reporter gene for improved proton exchange-based molecular MRI contrast,” Scientific Reports, Vol. 10, 20664, 2020. https://www.nature.com/articles/s41598-020-77576-z
O. Perlman, A. Borodetsky, Y. Kauffmann, Y. Shamay, H. Azhar, I. S. Weitz, “Gold/copper@ polydopamine nanocomposite for contrast-enhanced dual modal computed tomography-magnetic resonance imaging,” ACS Applied Nano-Materials, Vol. 2, No. 10, pp. 6124-6134, 2019. https://pubs.acs.org/doi/abs/10.1021/acsanm.9b00791
Our lab explores the molecular mechanisms underlying brain disease and develops methods for early diagnosis and therapy optimization. We develop tools for disentangling the different signals coming from brain metabolites, proteins, and lipids, and evaluate their potential to serve as noninvasive image bio-markers for cell death, ischemia, and disease severity. We design and implement AI-based methods for early interventions along the imaging pipeline, enabling automatic MRI acquisition protocol discovery and quantitative molecular parameters reconstruction. This allows for a drastic reduction in scan time and compatibility with a variety of biological scenarios.
