Efrat Shimron

Postdoctoral Fellow

Department of Electrical Engineering and Computer Sciences (EECS)

UC Berkeley

Efrat Shimron

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 modelling and computational imaging.

In 2015-2019 I was a PhD student at the Technion - Israel Institute of Technology. I spent wonderful years in the lab of my PhD advisor Prof. Haim Azhari, where I developed sparsity-based methods for MR image reconstruction.

Research

This work received a Magna cum Laude Award at the International Society of Magnetic Resonance in Medicine (ISMRM) 2021 Annual Meeting

New! The Subtle Crimes paper is now available in arXiv and its git repo is available in GitHub.

A recorded 20-min talk and a 5-min talk are available online (the 5-min one requires registration to the ISMRM'21 conference).


Efrat Shimron - TED Compressed Sensing

Temporal Differences (TED) Compressed Sensing

TED is a Compressed Sensing method for dynamic MRI, which enables accurate temperature reconstruction from sub-Nyquist k-space measurements. Our paper demonstrated that TED is highly suitable for temperature monitoring in MR-guided High Intensity Focused Ultrasound (MRgHIFU) treatments.

TED is generally applicable to dynamic MRI scans and can therefore be applied for different scan types. Our code is simple & fast. Let us know if you find a cool new app!

Efrat Shimron - CORE-PI method

CORE-PI

CORE-PI is a parallel imaging method for MRI. It enables reconstruction from subsampled k-space data acquired with multi-coil arrays in 2D Cartesian scans. CORE-PI is parameter-free, i.e. you there's no to calibrate any hyperparamer in order to use it. Check out our code!

Efrat Shimron - CORE-Deblur

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

  • December 2021: the git repo of the Subtle Data Crimes paper is published!

  • December 2021: I gave a talk about Subtle Data Crimes at the MRI Together conference (Dec 15, 2021).

  • October 2021: I gave a talk titled "Subtle Inverse Crimes: When AI is Overly-optimistic" at the Utah Imaging Elevated Symposium for Emerging Investigators. I really enjoyed this wonderful in-person conference!

  • September 2021: our Subtle Inverse Crimes paper (preprint) is available online - here.

  • September 2021: I gave a talk at Harvard's Workshop on MRI Acquisition and Reconstruction.

  • June 2021: I was named as an Emerging Investigator at the Imaging Elevated: Utah Symposium for Emerging Investigators (October 2021).

  • May 2021: our "Subtle Inverse Crimes" abstract received a magna cum laude award in the ISMRM conference and was selected as a finalist in the Magnetic Moments competition. Check it out to see our surprising results! The abstract is here and the video is is here .

  • May 2021: I co-organized the MR-Pub Competition for Interactive Code Demos.

  • March 2021: I was elected for the ISMRM Reproducible Research Study Group Governing Committee, as the Trainee Representative.

  • December 2020: I gave a tutorial on advanced regularization methods for dynamic MRI using BART, an open-source toolbox for MRI. The python tutorial and slides are available here.

  • September 2020: I was awarded the Israel Council for Higher Education (Vatat) National Fellowship for Excelling Post-doctoral Researchers in Data Science for my postdoctoral studies in UC Berkeley and Stanford.

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

Software: GitHub

Collaborations

Copyright Efrat Shimron, 2020