Areas of Focus:

Biomarker DiscoveryData-Driven & QuantitativeExperimental Platforms & ModelsNeuro-Immune InteractionsPrecision MedicineCross-Cutting & Special PopulationsNeurologic Diseases
  • Professor, School of Biomedical Engineering, Sagol School of Neuroscience, Tel Aviv University

Dr. Noam Ben-Eliezer is an Associate Professor of Biomedical Engineering and Principal Investigator in the Sagol School of Neuroscience at Tel Aviv University, where he heads the Lab for Advanced MRI. He completed his PhD at the Weizmann Institute of Science and postdoctoral training at New York University, where he pioneered Bloch-simulation–based quantitative T2 mapping that improves the sensitivity and reproducibility of MRI in normal-appearing tissue. He holds an adjunct Associate Professor appointment at NYU’s Center for Advanced Imaging, Innovation and Research.

Dr. Ben-Eliezer’s research focuses on quantitative and microstructural MRI, with an emphasis on in vivo mapping of myelin content, mechanisms of demyelination in multiple sclerosis, and the development of sensitive biomarkers for neurodegenerative and neuroinflammatory diseases. His lab develops advanced multi-parametric quantitative MRI protocols, biophysical signal models, and data-driven reconstruction and denoising algorithms to extract sub-voxel information about brain and muscle microarchitecture. These tools are applied across human and animal models to study CNS myeloarchitecture, predict multiple sclerosis progression, quantify microscopic fat infiltration in neuromuscular disorders, and probe tissue nanoarchitecture including collagen networks and iron-related compartments.

Projects

Featured Pilot Projects

Shedding Light on the Invisible: A New Paradigm for Predicting Multiple Sclerosis Disease Progression Using Novel MRI Tools for Probing Pathology in Normal Appearing Tissues
Project | Tel Aviv University

Shedding Light on the Invisible: A New Paradigm for Predicting Multiple Sclerosis Disease Progression Using Novel MRI Tools for Probing Pathology in Normal Appearing Tissues

Applying advanced quantitative MRI to detect pathology invisible to current clinical tools, this project builds an AI model to predict MS progression and enable earlier, more personalized diagnosis and treatment.