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. 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 MRI reconstruction methods for rapid dynamic MRI scans. My research leverages Machine Learning, physics-based modeling and computational imaging.
In 2015-2019 I was a PhD student at the Technion - Israel Institute of Technology. I spent joyful years in the lab of my PhD advisor Prof. Haim Azhari, where I developed sparsity-based methods for MR image reconstruction.
My work was published in PNAS, highlighted in a PNAS commentary article, and covered in the Berkeley News and UT Austin News. I was also named as an emerging investigator.
I am an advocate of reproducible research - all my papers have open-source code packages (see links below). I'm also the 2021-2023 Trainee Representative of the ISMRM Reproducible Research Study Group (RRSG) Governing Committee.
Research
New! This work was highlighted in a PNAS commentary article.
The paper was published in PNAS and covered in the Berkeley News and UT Austin News.
This work received a Magna cum Laude Award at the ISMRM 2021 Annual Meeting. Here is the git repo.
Here's a 20-min talk from the MRI Together international workshop (Dec 2021) and a 5-min talk from ISMRM'21 conference (this link requires registration to conference).
BladeNet: Dynamic Abdominal MRI using PROPELLER and Deep Learning
ISMRM 2022 abstract 0684.
New! I gave an oral presentation at the ISMRM conference in London (May 2022).
This work introduces a new method for rapid dynamic (temporal) abdominal MRI with high spatio-temporal resolution and built-in motion correction.
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).
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 Matthew Rosen (The Martinos center, Harvard)
TED - Temporal Differences Compressed Sensing for fast dynamic MRI
TED is a general MRI reconstruction method for dynamic (temporal) imaging.
In this work, TED was developed for temperature monitoring. However, the method is more general and can be implemented to other applications.
CORE-Deblur
CORE-Deblur 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
Talks (upcoming & recent):
"Open Science and Data Crimes: The Risk in Naive Training of Medical AI Algorithms", University of Basel, October 2022. Host: Dr. Francesco Santini.
"Inverse data crimes", ESMRMB international wokrshop on machine learning in MRI. July 12, 2022. Organizers: Dr. Kerstin Hammernik & Dr. Thomas Kustner.
"Implicit data crimes", King's College London, June 14, 2022. Host: Prof. Claudia Prieto.
"Implicit data crimes", Inria, the French institute of computer Science, Paris (May 2022). Host: Prof. Phillippe Ciuciu.
I'm co-editing a special issue of the Bioengineering journal (IF ~4.7), titled "AI in MRI: Frontiers and applications". The information is here. Deadline: 20 December 2022. Open for submission!
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 was published in PNAS and covered in the Berkeley News, Berkeley Engineering Magazine and UT Austin News (March 2022).
March 2022: I gave a talk in the BAIR Visual Computing Workshop. Title: "Data Crimes: The Risk in Naïve Training of Medical AI Algorithms".
March 2022: I am co-organizing the 2nd MR Pub competition for open science, together with the ISMRM RRSG committee. Details soon!
Feb 2022: Three abstracts (1 first-authored and 2 co-authored) were accepted to ISMRM'22:
Shimron et al., "BladeNet: Rapid PROPELLER Acquisition and Reconstruction for High spatio-temporal Resolution Abdominal MRI".
Waddington, Shimron, Hindley, Koonjoo, Rosen, "Accelerating Ultra-Low Field MRI with Compressed Sensing". This work is part of my collaboration with the group of Prof. Matthew Rosen (MGH, Harvard).
Wang et al., "Rigorous Uncertainty Estimation for MRI Reconstruction" - collaboration between our lab and Jitendra Malik's group.
December 2021: the git repo of the Data Crimes paper is published!
December 2021: I gave a talk about Data Crimes at the MRI Together conference (Dec 15, 2021).
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
Frederic Wang
Jerry (Boyuan) Ma
Han Qi
Max Lister
Alma Harlangen
(c) website copyright Efrat Barak Shimron, 2022