Advanced Medical Imaging Research Lab - AMIR

Prof. Dafna Ben Bashat, PhD is a Full Professor at the Sagol School of Neuroscience and the Gray Faculty of Medical & Health Sciences, Tel Aviv University. She has served for the past 27 years as the Deputy Director of the Sagol Brain Institute and the Director of MRI Physics and Research at the Tel Aviv Sourasky University Medical Center. She leads the Advanced Medical Imaging Research Lab (AMIR), where her research focuses on the development of quantitative MRI methodologies, imaging biomarkers, and artificial intelligence tools for precision medicine. Her work spans brain tumors, neurodevelopment, neurodegeneration, and fetal and placental imaging, with a strong emphasis on translating advanced imaging technologies into routine clinical practice.
Her research integrates MRI physics with Artificial Intelligence to address fundamental challenges in radiology and a wide range of clinical disciplines, with the goals that support physicians in clinical decision-making and improve personalized patient care. Prof. Ben Bashat has authored approximately 150 peer-reviewed publications in leading scientific journals, with more than 7,500 citations. She has led and participated in numerous national and international research collaborations and is a frequent invited speaker, keynote lecturer, and session organizer at prestigious international conferences, including ISMRM, MICCAI, SNO, and RSNA.

Dr. Moran Artzi, PhD is a Senior Lecturer at Sagol School of Neuroscience and the Gray Faculty of Medical & Health Sciences, Tel Aviv University. She has served for the past 10 years as a Co-Director of the Pre-Surgical Brain Mapping Service at the Sagol Brain Institute. Dr. Artzi is an expert in machine learning, computer vision, and deep learning for medical imaging applications. Her main research focuses on the concept of virtual biopsy, enabling a non-invasive characterization of tumor biology, molecular profiles, and disease subtypes. She also develops predictive models that enhance diagnostic accuracy, treatment planning, and response assessment in complex neurological disorders.

Anna Levchakov, MSc - Electric Engineer - Laboratory Manager: Oversees the day-to-day operations of the AMIR Lab, coordinating research activities, project logistics, regulatory processes, and collaboration across multidisciplinary teams to ensure the successful execution of clinical and research studies.

Tuvia Ganot, BA, Radiographer – Senior MRI Technologist: A senior MRI technologist with extensive expertise in advanced clinical and research MRI acquisitions, responsible for implementing and optimizing specialized imaging protocols and supporting the translation of innovative MRI methodologies into clinical practice.

Shelly Gur, M.Sc, PhD candidate  – Clinical Research Coordinator: Coordinates clinical research studies, patient recruitment, regulatory approvals, and study logistics, serving as a key liaison between investigators, clinical teams, and research participants to ensure the efficient conduct of clinical research.

Doctoral Candidates (PhD)
• Yael Herman Moshe
• Shahar Zachariah
• Aaron Olender
• Dana Schonberger*
• Sapir Fajerzstein*
• Yossi Nahmias
• Abeer Saeed

Master’s Candidates (MSc)
• Yuval Buchsweiler
• Mor Alon
• Eyal Hizmi
• Rotem Orad
• Neama Khalil
• Sean Zaretzky

Doctoral Candidates (PhD)
• Gilad Liberman
• Maya Weinstein
• Moran Artzi
• Efrat Kliper
• Dafi Link Sourani
• Oren Gery
• Neta-li Mor
• Netanel Avisdris
• Bosmat Yehuda
• Aviad Rabinowich

Master’s Candidates (MSc)
• Yosi Levy
• Eilon Gersten
• Ida Sivan
• Vered Kronfeld-Duenias
• Moran Arzi
• Maya Weinstein
• Reut Granot Halevy
• Guy Nadav
• Keren Rotker
• Dor Amran
• Shiran Halabi
• Ori Ben-Zvi
• Zeev Hananis
• Idan Brssler
• Shelly Gur

This collaboration advances the concept of virtual biopsy, leveraging advanced MRI analysis and deep learning techniques to non-invasively characterize central nervous system lesions. The group develops tools for differentiating between distinct pathologies, tumor types, and molecular profiles directly from imaging data, supporting accurate diagnosis and personalized treatment planning while reducing reliance on invasive procedures.

• Key Collaborators: Orna Aizenstein, Dr. O. Haim, Dr. Segev Gabay, Prof. Ido Strauss, Prof Assaf Tal, Prof. Jonathan Roth. 

The collaboration focuses on the imaging-based assessment of treatment response in patients with brain tumors. Using advanced MRI techniques, including Dynamic Contrast-Enhanced (DCE) MRI, together with artificial intelligence methods, the group has developed tools that differentiate true tumor progression from treatment-related changes. These approaches improve clinical decision-making and have been approved for clinical use in Israel, including incorporation into the national health basket.

Key Collaborators: Orna Aizenstein, Prof. D.T. Blumenthal, Dr. F. Bokstein, Prof. Ido Strauss, Prof Assaf Tal, Dr. Lior Zach.  

The AMIR Lab is internationally recognized for pioneering quantitative fetal and placental MRI and artificial intelligence to advance precision prenatal medicine. The group develops novel imaging biomarkers and automated analysis tools to non-invasively characterize fetal brain and body development, placental structure and function, and the maternal–placental–fetal unit.
Key innovations include quantitative assessment of fetal body composition, placental perfusion and function, and fetal brain development. These technologies improve the diagnosis and outcome prediction of pregnancies complicated by fetal growth restriction, congenital CMV infection, and genetic disorders affecting brain development. Current March of Dimes-funded studies investigate how maternal factors-including sleep, stress, nutrition, and physical activity—influence fetal development through the placenta.

Key Collaborators: Prof. Liat Ben-Sira, Prof. G. Malinger, Prof. Karina Krajden Haratz, Prof. Liran Hiersch, Prof. Riva Tauman.

In these studies we apply multimodal AI and deep learning approaches that integrate medical imaging with clinical data to improve prediction, risk stratification, and clinical decision support in musculoskeletal and oncologic diseases. For example, recent work demonstrated improved prediction of orthopedic management decisions by combining X-ray imaging with clinical variables in patients with metastatic bone disease.

• Key Collaborator: Amir Sternheim, Prof. Zohar Yosibash

The AMIR Lab develops advanced quantitative MRI techniques and imaging biomarkers to improve the early diagnosis, characterization, and monitoring of neurodegenerative disorders. By integrating neuromelanin-sensitive MRI, quantitative susceptibility and iron imaging, Chemical Exchange Saturation Transfer (CEST), perfusion imaging, and artificial intelligence, the group investigates the biological mechanisms underlying Parkinson's disease, Alzheimer's disease, and related dementias. Current research focuses on identifying sensitive biomarkers of neurodegeneration, vascular dysfunction, and disease progression, with the goal of enabling earlier diagnosis, predicting clinical outcomes, and supporting precision medicine.



Key Collaborator: Prof. Anat Mirelman, Prof. Avner Thaler, Dr. Amgad Droby, Dr. Orna Aizenstein, Dr. Yair Wexler, Dr. Tamara Shinar, Prof. Nora Bregman, Dr. Or Perlman.

The AMIR Lab pioneers quantitative cerebrovascular imaging by integrating advanced MRI physics with artificial intelligence to characterize cerebral hemodynamics in health and disease. Combining TWIST, ASL, and other quantitative MRI techniques, the lab investigates cerebral blood flow, vascular permeability, collateral circulation, and tissue perfusion in Moyamoya disease, ischemic stroke, intracranial aneurysms, and related vascular disorders. The goal is to develop imaging biomarkers that enable precision diagnosis, predict cognitive outcomes, and guide personalized treatment.

• Key Collaborator: Orna Aizenstein, Prof. Moran Housman, Dr. Shelly Shiran, Prof. Udi Sadeh Gonik, Prof. Liat Ben Sira, Prof. Yael Leitner, Dr. Ayelet Zerem.

PI
Prof. Dafna Ben Bashat & Dr. Moran Artzi

Technological Core (AI/MRI)
Multi-parametric analysis of DCE-MRI using quantitative signal analysis, machine learning, and deep learning

Clinical Objective
To quantitatively characterize MRI changes and accurately distinguish treatment-related changes from tumor recurrence.
Approved by the Israeli National Health Basket as a clinical decision-support tool.

PI
Prof. Dafna Ben Bashat 

Technological Core (AI/MRI)
AI-based 3D reconstruction, multi-class segmentation, and advanced quantitative fetal MRI analysis

Clinical Objective
To provide automated quantitative assessment of fetal development and growth for improved prenatal evaluation.
Supported by the Israel Innovation Authority.

PI
Prof. Dafna Ben Bashat 

Technological Core (AI/MRI)
Advanced diffusion MRI, high b-value diffusion-weighted imaging, and tractography

Clinical Objective
To identify accelerated white matter maturation and characterize brain–behavior relationships during early childhood in autism spectrum disorder.
This pioneering work was the first to demonstrate accelerated white matter maturation in autism and has subsequently been replicated by multiple independent international studies.

PI
Dr. Moran Artzi

Technological Core (AI/MRI)
Deep learning foundation models applied to conventional MRI.

Clinical Objective
To enable automated detection of spinal cord lesions and localization of spinal dural arteriovenous fistula (SDAVF) feeders, facilitating earlier diagnosis and treatment planning.

PI
Prof. Dafna Ben Bashat 

Technological Core (AI/MRI)
Multi-parametric placental and fetal MRI, quantitative imaging biomarkers, AI-based image analysis, and advanced MRI quantification

Clinical Objective
To identify MRI biomarkers of placental dysfunction associated with maternal obstructive sleep apnea and to improve the assessment and prediction of adverse fetal growth and pregnancy outcomes.
Supported by the March of Dimes (MOD).