Efrat Shimron

Postdoctoral Fellow

Department of Electrical Engineering and Computer Sciences (EECS)

UC Berkeley


New!  I'm currenlty joining the Technion - Israel Institute of Technology as an assistant professor, with dual affiliation to the Electrical & Computer Engineering (ECE) department & Biomedical Engineering departments. I'm excited to open my medical AI lab at the Technion. 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

My work was recently 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 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. 

For students/postdocs: I have open positions for all levels (senior undergrads/MSc/PhD/postdoc). If you're interested in joining my team, please reach out.


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

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.

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 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.

The paper is here and the open-source toolbox is here

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

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).

Efrat Shimron - CORE-PI method


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.

Efrat Shimron - 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. 



Students (mentees)

Contact & social media: Email (efrat.s at berkeley dot edu), LinkedIn, Twitter 

Software:  GitHub

(c) website copyright Efrat Barak Shimron, 2022