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 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.

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

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

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

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):

  • 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

Students (mentees)

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

Software: GitHub

(c) website copyright Efrat Barak Shimron, 2022