Efrat Shimron
Postdoctoral Fellow
Department of Electrical Engineering and Computer Sciences (EECS)
UC Berkeley
About
I am a postdoc in the Department of Electrical Engineering and Computer Sciences (EECS) at UC Berkeley. My research focuses on the development of data-driven computational techniques for solving inverse problems in medical imaging. I have the pleasure of being co-advised by Prof. Michael (Miki) Lustig (UC Berkeley) and Prof. Shreyas Vasanawala (Stanford). I work with their collaborative groups on developing medical imaging frameworks, focusing on dynamic (temporal) imaging. I also collaborate with the group of Prof. Matt Rosen (Harvard) on developing imaging techniques with the emerging technology of ultra-low-field MRI. Previously, on 2015-2019, I was a PhD student at the Technion - Israel Institute of Technology, advised by Prof. Haim Azhari. My PhD dissertation focused on developing sparsity-based methods for image reconstruction.
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 recently received several career awards: Rising Stars in Electrical Engineering and Computer Sciences (2023), Outstanding Emerging Investigator, and the Weizmann Institute Women’s Postdoctoral Career Development Award in Science (2022-2024).
I am an advocate of reproducible research - all my papers have open-source code (links below). I'm also the 2021-2023 Trainee Representative of the ISMRM Reproducible Research Study Group (RRSG) Governing Committee.
Research
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
This work was accepted for an oral presentation in the 2023 Sedona conference - abstract here (requires access).
This work introduces:
A new approach for rapid data acquisition, compatible with any Cartesian MRI protocol. Useful for building new databases for deep learning!
A strategy for self-supervised training of MRI reconstruction algorithms w/o ground truth data.
This work was done by two wonderful students Frederic Wang & Han Qi, whom I supervised.
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)
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.
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 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 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
Two abstracts accepted for oral talks in the ISMRM Workshop on Data Sampling and Image Reconstruction (Sedona, 2023):
Shimron et al., "BladeNet: an acquisition-reconstruction framework for free-breathing dynamic MRI"
Wang et al., "K-band: Training self-supervised reconstruction networks using limited-resolution data"
I recently received two career awards (August 2022):
Rising Stars in Electrical Engineering and Computer Sciences (EECS) (2023). The Rising Stars annual event was launched at MIT in 2012, and since then every year it is hosted by a different university. This year it is hosted by UT Austin.
Women’s Postdoctoral Career Development Award in Science (national Israeli fellowship) (2022-2024), given by the Weizmann Institute of Science.
I'm co-editing a special issue titled "AI in MRI: Frontiers and applications" for the Bioengineering journal (impact factor 5.05), together with Dr. Or Perlman. The special issue is open for submission! See details here. Deadline: February 1st, 2023.
I'm co-organizing a session with Prof. Florian Knoll for the upcoming in-person BASP Frontiers conference (Switzerland, February 2023). Session title: "Potential Pitfalls of Deep Learning in Medical Image Reconstruction".
My talks - upcoming & recent:
"Fundamentals of Deep Learning" - Educational talk in the ISMRM Annual Meeting, Toronto, June 2023.
"Potential Pitfalls of Deep Learning in Medical Image Reconstruction" - BASP Frontiers conference, Switzerland, February 2023. Co-organized with Prof. Florian Knoll.
"Data Crimes and BladeNets: Frontiers in Medical AI"
Technion (February 2023)
University of Sydney (December 2022)
Rising Stars conference (Austin, TX, October 2022).
UT Austin, ECE Machine Learning seminar (October 2022). Host: Prof. Jon Tamir.
Memorial Sloan Kettering (MSK), New York (Sep 2022). Host: Prof. Ricardo Otazo.
Department of Computer Sciences and Applied Mathematics, Weizmann Institute of Science (Aug 2022). Host: Prof. Robert Krauthgamer.
Aspect Imaging, Israel (Aug 2022). Host: Dr. Gil Farkash.
"Data Crimes: The Risk in Naive Training of Medical AI Algorithms":
University of Basel, October 2022. Host: Dr. Francesco Santini.
University of Minnesota (July 2022). Host: Prof. Mehmet Ackakaya.
ESMRMB international course on machine learning in MRI (July 2022). Organized by Dr. Kerstin Hammernik & Dr. Thomas Kustner.
King's College London (June 2022). Host: Prof. Claudia Prieto.
Inria, the French Institute of Computer Science, Paris (May 2022). Host: Prof. Phillippe Ciuciu.
My activity in the ISMRM 2022 conference in London (May 2022):
I gave an oral presentation titled: "BladeNet: Rapid PROPELLER Acquisition and Reconstruction for High spatio-temporal Resolution Abdominal MRI". Abstract #0684.
I organized the MR-Pub II competition for open code.
I moderated these sessions:
Software tutorials for the whole community: introduction & panel discussion. May 9, 8:00-9:00. Details here.
Software tutorials for the whole community: software tools I & II. May 10, 8:00-9:00. Details here.
(RRSG) Member Initiated Symposium (MIS), titled: "The Reproducibility Crisis - Perspectives from Funders, Researchers and Journal Editors"..
Deep Learning image reconstruction. Details here.
April 2022: My work was highlighted in a PNAS commentary article.
March 2022: my Data Crimes paper was published in PNAS and covered in the Berkeley News, Berkeley Engineering Magazine and UT Austin News (March 2022).
October 2021: I was named as an Emerging Investigator at the Imaging Elevated: Utah Symposium for Emerging Investigators.
Collaborations
Prof. Jon Tamir, Electrical and Computer Engineering Department (ECE), UT Austin.
Prof. Matthew Rosen, Martinos Center for Biomedical Imaging, Harvard.
Dr. David Waddington - The University of Sydney
Prof. Martin Uecker, Department of Diagnostic and Interventional Radiology, University Medical Center Göttingen.
Prof. William Grissom, Biomedical Engineering Department, Vanderbilt University, TN.
Prof. Andrew Webb, Leiden University Medical Center (LUMC), Leiden, The Netherlands.
Prof. Stella Yu, Director of the ICSI Vision Group at the Berkeley Institute for Data Science.
Ke Wang, PhD candidate at UC Berkeley's Electrical Engineering and Computer Sciences (EECS) Department
Students (mentees)
Han Cui
Max Lister
Alma Harlangen
(c) website copyright Efrat Barak Shimron, 2022