Efrat Shimron
Efrat Shimron
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Assistant Professor
Department of Electrical and Computer Engineering (ECE) &
Department of Biomedical Engineering (BME) &
Technion May-Blum-Dahl Human MRI Research Center
Technion - Israel Institute of Technology
About
About
Welcome!
Welcome!
I'm an assistant professor at the Technion - Israel Institute of Technology, with dual affiliation to the Electrical & Computer Engineering (ECE) & Biomedical Engineering (BME) departments. I'm leading the medical AI & MRI lab. My research focuses on developing computational techniques for solving inverse problems in medical imaging, primarily for Magnetic Resonance Imaging (MRI). It combines computer vision, AI, physics, and medical sciences.
I'm an assistant professor at the Technion - Israel Institute of Technology, with dual affiliation to the Electrical & Computer Engineering (ECE) & Biomedical Engineering (BME) departments. I'm leading the medical AI & MRI lab. My research focuses on developing computational techniques for solving inverse problems in medical imaging, primarily for Magnetic Resonance Imaging (MRI). It combines computer vision, AI, physics, and medical sciences.
I was previously a postdoc in the Department of Electrical Engineering and Computer Sciences (EECS) at UC Berkeley, where I worked with Prof. Michael (Miki) Lustig (UC Berkeley) and Prof. Shreyas Vasanawala (Stanford) on developing computational techniques for dynamic (temporal) MRI and investigating AI bias. I also have an ongoing collaboration with the group of Prof. Matthew Rosen (Harvard) regarding the emerging technology of low-field MRI.
I was previously a postdoc in the Department of Electrical Engineering and Computer Sciences (EECS) at UC Berkeley, where I worked with Prof. Michael (Miki) Lustig (UC Berkeley) and Prof. Shreyas Vasanawala (Stanford) on developing computational techniques for dynamic (temporal) MRI and investigating AI bias. I also have an ongoing collaboration with the group of Prof. Matthew Rosen (Harvard) regarding the emerging technology of low-field MRI.
My work was published in PNAS, highlighted in a PNAS commentary article, highlighted in the NIBIB New Horizons plenary talk at the ISMRM'22 conference, and covered in the Berkeley News, Berkeley Engineering Magazine, and UT Austin News.
My work was published in PNAS, highlighted in a PNAS commentary article, highlighted in the NIBIB New Horizons plenary talk at the ISMRM'22 conference, and covered in the Berkeley News, Berkeley Engineering Magazine, and UT Austin News.
I recently received several career awards: the National Alon fellowship (VATAT) for outstanding young PIs (2024), Rising Stars in Electrical Engineering and Computer Sciences (2023), the Weizmann Institute Women’s Postdoctoral Career Development Award in Science (2022-2024) and an Outstanding Emerging Investigator award.
I recently received several career awards: the National Alon fellowship (VATAT) for outstanding young PIs (2024), Rising Stars in Electrical Engineering and Computer Sciences (2023), the Weizmann Institute Women’s Postdoctoral Career Development Award in Science (2022-2024) and an Outstanding Emerging Investigator award.
I am an advocate of reproducible research - all my papers have open-source code (links below). I was also the 2021-2023 Trainee Representative of the ISMRM Reproducible Research Study Group (RRSG) Governing Committee.
I am an advocate of reproducible research - all my papers have open-source code (links below). I was also the 2021-2023 Trainee Representative of the ISMRM Reproducible Research Study Group (RRSG) Governing Committee.
I'm currently recruiting MSc/PhD students and postdocs for my lab at the Technion.
I'm currently recruiting MSc/PhD students and postdocs for my lab at the Technion.
I'm also recruiting for an open postdoc position at Cornell-Tech (New York City), for a joint research project with Prof. Mert Sabuncu.
Interested? See here and email me.
My office: Meyer 960 (Technion's ECE faculty, main building)
My office: Meyer 960 (Technion's ECE faculty, main building)
Research
Research
New paper - from my lab! (2025)
We introduce a novel healthcare paradigm and deep-learning framework for accessible MRI
- Paradigm: longitudinal MRI with baseline high-field scans and follow-up low-field scans.
- AI framework: ViT-Fuser, a vision transformer that learns personalized features from the initial scan and uses them to boost speed and quality of the follow-up low-field scan.
- The paper is here.
This work was led by my student Tal Oved & accepted for a power pitch in ISMRM'25 (Hawaii)
This work was published in PNAS and highlighted in a PNAS commentary article.
It was also covered in the Berkeley News, Berkeley Engineering Magazine, and UT Austin News.
It also received a Magna cum Laude Award at the ISMRM 2021 Conference.
Here is the git repo.
20-min talk from the MRI Together workshop (2021)
5-min talk from ISMRM'21 conference (this link requires registration to conference).
BladeNet: Dynamic Abdominal MRI using PROPELLER and Deep Learning
BladeNet: Dynamic Abdominal MRI using PROPELLER and Deep Learning
I gave oral presentations about this work in the ISMRM conference in London (May 2022) & the Sedona Workshop on Data Sampling and Image Reconstruction (Jan 2023).
This work introduces a new acquisition-reconstruction framework for rapid dynamic (temporal) abdominal MRI with high spatio-temporal resolution and built-in motion correction.
ISMRM 2022 abstract 0684 - here (requires access).
Sedona 2023 abstract - here (requires access).
K-band: a novel strategy for fast MRI
New! The paper is on arXiv: https://arxiv.org/abs/2308.02958
This work was accepted for oral presentations in the 2023 Sedona conference (abstract here - this requires access) and the 2023 ISMRM conference.
A new approach for rapid data acquisition and self-supervised training of MRI reconstruction algorithms w/o ground truth data.
Theoratical convergence guarantees
This work was done by two wonderful students that I have mentored, Frederic Wang & Han Qi (co-first).
TED - Temporal Differences Compressed Sensing for fast dynamic MRI
TED - Temporal Differences Compressed Sensing for fast dynamic MRI
TED is a reconstruction method that I developed for dynamic (temporal) MR imaging.
Here, TED was developed for temperature monitoring, which is required for real-time MR monitoring of high-intensity focused ultrasound. Note that TED is more general and can be suitable for other dynaic MRI applications.
Synthesizing Complex Multi-coil MRI data from Magnitude-only Images.
Synthesizing Complex Multi-coil MRI data from Magnitude-only Images.
New paper! Here.
DL reconstruction techniques are data-hungry, but raw MRI databases are scarce. We propose a method to leverage the huge amounts of magnitude-only data stored in hospitals.
This GAN-based method enables synthesizing complex-valued multi-coil data from magnitude data. The results indicate that neural nets trained on the synthesized data produce competitive state-of-the-art results.
This work is part of my collaboration with the group of Prof. Peder Larson from UCSF.
Accelerating Ultra-Low Field MRI with Compressed Sensing
ISMRM 2022 abstract #88.
This work is part of my collaboration with Dr. David Waddington (University of Sydney) and Prof. Matthew Rosen (The Martinos center, Harvard)
Rigorous Uncertainty estimation for MRI Reconstruction
Rigorous Uncertainty estimation for MRI Reconstruction
ISMRM 2022 abstract #749.
Presented in an oral presentation at the ISMRM conference in London (May 2022).
This work is led by Ke Wang (I'm a co-author).
CORE-PI
CORE-PI
CORE-PI is a method that I developed in my PhD studies. It is a parallel imaging MRI reconstruction method, suitable for 2D Cartesian scans. The main advantage of CORE-PI is that it is parameter-free, i.e. there's no to calibrate any hyperparamer in order to use it.
Check out the code.
CORE-Deblur
CORE-Deblur
CORE-Deblur is another method that I developed in my PhD studies. It is a Parallel Imaging Compressed Sensing (PI-CS) method.
Our paper introduced the concept of MR image reconstruction by deblurring using Compressed Sensing (CS), and showed that CORE-Deblur can expedite CS computations: the number of required iterations is reduced by a factor of 10.
News
News
I'm recruiting for an open postdoc position at Cornell-Tech (New York City), for a joint research project with Prof. Mert Sabuncu.
Interested? See here and email me.Excited to share that we presented our new work on low-field MRI in the ISMRM conference in Hawaii (5/25), and it was very well received by the community. See my LinkedIn posts here and here and coverage by the RadAccess newsletter here.
I'm co-chair for data and challenges of the 2027 MICCAI conference (Hawaii).
I was recently appointed to be an Associate Editor of the MAGMA journal.
New paper out - first one from my lab! Deep learning of personalized priors from past MRI scans enables fast, quality-enhanced point-of-care MRI with low-cost systems (5/2025).
My invited talks:
ISMRM Annual Meeting, title: "AI Reconstructions for Speed" (Hawaii, May 2025)
ISMRM Body MRI workshop, title: "Enhancing MRI with AI" (Philadelphia, March 2025)
BASP Frontiers conference on supervised MRI reconstruction (Switzerland, January 2025)
ESMRMB congress, title: "Image Reconstruction: Compressed Sensing, Model-Based Reconstruction, Machine Learning" (Barcelona, October 2024)
Recent awards:
I received Israel's national Alon fellowship for young PIs. This fellowship was granted to 5 awardees, selected nationwide from the fields of engineering and exact sciences.
My student Tal Oved received the Meyer Excellence Award of the Technion's ECE Department! Tal was the first student to join the lab - I'm very proud of him! (10/24)
New papers (2024):
"Accelerating Low-field MRI: Compressed Sensing and AI for fast noise-robust imaging" https://arxiv.org/abs/2411.06704. Joint work with David Waddington (U. Sydney) and Matthew Rosen (Harvard/MGH)
MRM editorial paper on code review, with Shaihan Malik, Peter Jezzard and colleagues (in press, October 2025)
Our new review on deep learning for MRI reconstruction, with Reinhard Heckel and colleagues (August 2024).
First grant funded - our lab received the Zimin Institute grant for developing AI methods for healthcare! (6/24) news here.
I was interviewed to one of Israel's leading newspapers, Haaretz, regarding the revolution of low-field MRI systems (in Hebrew).
I'm honored to be named as a Horev Fellow as part of the Technion's Leaders in Science and Technology program (October 2023).
Our k-band paper is out: https://arxiv.org/abs/2308.02958
I recently gave a talk at the Martinos center (Harvard/MGH). The recording is available on youtube, here.
Recent papers:
Radiology AI editorial: Sharing Data Is Essential for the Future of AI in Medical Imaging, with Laura Bell (2023).
Bioengineering journal editorial: "AI in MRI: Computational Frameworks for a Faster, Optimized, and Automated Imaging Workflow". Part of the special issue that Or Perlman and I co-edited, titled "AI in MRI: Frontiers and applications"
Nikhil Deveshwar, Abhejit Rajagopal, Sule Sahin, Efrat Shimron, Peder E.Z. Larson
"Synthesizing Complex Multicoil MRI Data from Magnitude-only Images" Bioengineering journal (2023)
Collaborations
Collaborations
Prof. Matthew Rosen, Martinos Center for Biomedical Imaging, Harvard.
Dr. David Waddington - The University of Sydney
Prof. Andrew Webb, Leiden University Medical Center (LUMC), Leiden, The Netherlands.
Prof. Jon Tamir, Electrical and Computer Engineering Department (ECE), UT Austin.
Prof. Martin Uecker, Department of Diagnostic and Interventional Radiology, University Medical Center Göttingen.
Prof. Stella Yu, Director of the ICSI Vision Group at the Berkeley Institute for Data Science.
Prof. Peder Larson, Univeristy of California, San Francisco (UCSF)
Prof. William Grissom, Biomedical Engineering Department, Vanderbilt University, TN.
Students (mentees)
Students (mentees)
At Technion:
Orel Tsioni
Tal Oved
Alon Granek
At UC Berkeley (2020-2023)
Frederic Wang - graduated, now a PhD student at Caltech
Han Qi - graduated, now a PhD student at Harvard
Han Cui
Max Lister
Alma Harlangen
Nikhil Deveshwar (joint BioE track with UCSF)
(c) website copyright Efrat Barak Shimron, 2024
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