Department of Electrical Engineering and Computer Sciences (EECS)
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, focusing on body imaging. My research leverages Machine Learning, physics-based modeling and computational imaging. Before that, 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.
I recently received several career awards: Rising Stars in Electrical Engineering and Computer Sciences (2023), Outstanding Emerging Investigator, and Women’s Postdoctoral Career Development Award in Science of the Weizmann Institute (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.
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)
BladeNet: Dynamic Abdominal MRI using PROPELLER and Deep Learning
ISMRM 2022 abstract 0684.
I gave an oral presentation at the ISMRM conference in London (May 2022).
This work introduces a new acquisition-reconstruction framework for rapid dynamic (temporal) abdominal MRI with high spatio-temporal resolution and built-in motion correction.
The abstract is here (link requires ISMRM'22 registration)
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.
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 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 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.
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), endowed 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: 20 December 2022.
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:
"Data Crimes and BladeNets: Frontiers in Medical AI"
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.
October 2021: I was named as an Emerging Investigator at the Imaging Elevated: Utah Symposium for Emerging Investigators.
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.
Ke Wang, PhD candidate at UC Berkeley's Electrical Engineering and Computer Sciences (EECS) Department
Jerry (Boyuan) Ma
(c) website copyright Efrat Barak Shimron, 2022