Prof. Dafna[Dafna] Ben Bashat

Diagnostic Imaging
School of Continuing Medical Education
דימות סגל אקדמי קליני



1998-Current     Deputy Director Sagol Brain Institute &

In charge of MRI systems, Tel Aviv Sourasky Medical Center

1991-1998          Ph.D.; Physical Chemistry, Tel Aviv University, Direct track towards PhD

1988-1991          B.Sc.; Chemistry, Tel Aviv University, Summa cum laude




Advanced MRI and analysis methods

Research Topics My main research goals are to improve diagnosis and prognosis of several brain disorders in clinical setup, by facilitating the development of more accurate methods. Applications include fetal and neonatal brain development, and neurological disorders such as Parkinson’s Disease.

Research Methods: In my lab we use advanced MRI methods, including anatomical, diffusion based and functional methods focusing on the cerebral vascular system. My group develops both MR acquisition methods and uses machine learning algorithms for image analysis.

Projects for Students: 

1.  Quantitative assessment of normal fetal brain development and early identification of developmental disorders

2.  Fetal blood circulation: anatomy, hemodynamics, function and interplay with fetal brain development

3. Quantitative assessment of fetal structure and organs development using deep learning methods

4.  The role of blood brain barrier integrity in the development of neurological disorders


Selected publications:


1.        Elka Miller, Abhijeet Taori, Jorge Davila, Liat Ben Sira, Dafna Ben Bashat, “MR Imaging of the Developing Fetal Brain Structures”. Book: Factors affecting neurodevelopment- genetics, neurology, behavior and diet, Chapter 17, Elsevier, 2021.

2.        Netanell Avisdris, Bossmat Yehuda, Ori Ben-Zvi, Daphna Link-Sourani, Liat Ben-Sira, Elka Miller, Elena Zharkov, Dafna Ben Bashat, Leo Joskowicz, “Automatic linear measurements of the fetal brain on MRI with deep neural networks” CARS, 2021.

3.        Soroush Heidari Pahlavian, Oren Geri, Jonathan Russin, Samantha J Ma, Arun Amar, Danny J.J. Wang. Dafna Ben Bashat, Lirong Yan, Semi-automatic Cerebrovascular Territory Mapping Based on Dynamic ASL MR Angiography without Vessel-Encoded Labeling, MRM, 2020,.

4.        Gal Dudovitch, Dafna Sourani Link, Liat Ben Sira, Elka Miller, Dafna Ben Bashat, Leo Joskowicz. ”Deep learning automatic fetal structures segmentation in MRI scans with few annotated datasets”. MICCAI 2020

5.        Moran Artzi, Sapir Gershov, Liat Ben-Sira, Jonathan Roth, Ben Shofty(S), Tomer Gazit, Tali Halag-Milo, Shlomi Constantini, Dafna Ben Bashat. “Automatic segmentation, classification and follow-up of optic pathway gliomas using deep learning and fuzzy c-means clustering based on MRI”. MEDICAL PHYSICS. Sep 2020; 

6.        Daphna Link, Ariel Many, Liat Ben Sira, Ricardo Tarrasch, Stella Bak, Zoya Gordon, Simcha Yagel, Shaul Harel, Dafna Ben Bashat. “A quantitative method for human placental vascular tree characterization based on ex-vivo MRI with a potential application to placental insufficiency assessment”, Placenta, 2020, July: 34-43.

7.        Artzi M., Liberman, G., Blumenthal TD., Aizenstein O., Bokstein F., Ben Bashat D.  “Differentiation between vasogenic edema and infiltrative tumor in patients with high grade gliomas using texture patch based analysis”. Journal of Magnetic Resonance Imaging; 2018, 48:729-736.

8.        Oren Geri, Shelly I Shiran, Jonathan Roth, Moran Artzi, Liat Ben-Sira, and Dafna Ben Bashat. “Vascular territorial segmentation and volumetric blood flow measurement using dynamic contrast enhanced magnetic resonance angiography of the brain”. J Cereb Blood Flow Metab. 2017, Oct;37(10).

9.        D. Link, M. B. Braginsky, L. Joskowicz, L. Ben Sira, S. Harel, A. Many, R. Tarrasch, G. Malinger, M. Artzi, C. Kapoor, E. Miller, D. Ben Bashat. “Automatic Measurement of Fetal Brain Development from Magnetic Resonance Imaging: New Reference Data”. Fetal Diagn Ther 2018;43:113–122.

10.    Deborah T. Blumenthal, Artzi M, Liberman G, Felix Bokstein, Orna Aizenstein, Ben Bashat D. “Classification of High Grade Glioma into Tumor and Non-Tumor Components using Support Vector Machine”, American Journal of Neuro-Radiology. AJNR Am J Neuroradiol. 2017 May;38(5):908-914

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