Jon Tamir, PhD
EECS, UC Berkeley

468 Cory Hall (Swarm Lab) Berkeley, CA 94720

Github / Google Scholar / LinkedIn

I will be joining UT Austin ECE as an assistant professor, January 2020



I am a research associate in Electrical Engineering and Computer Sciences at UC Berkeley, working with Miki Lustig. I received my PhD from UC Berkeley in 2018. My research focus spans computational magnetic resonance imaging, signal processing, and inverse problems. I am primarily interested in applying advanced imaging and reconstruction techniques to pediatric MRI, with the goal of enabling real clinical adoption.

I am also a part-time research scientist at Subtle Medical.

I got my undergaduate degree in Electrical and Computer Engineering at UT Austin.

In Winter 2016, I was a visiting scientist at GE Healthcare Israel, working with Yuval Zur. In Summer 2015 I interned at Arterys. Some time before that I interned at National Instruments. And way before that I interned at Centaur Technology.

Research Overview

Unsupervised Deep Basis Pursuit

Learning without ground-truth data
> Slides

Fast, Targeted Pediatric Knee MRI Exam

Using T2 Shuffling in the clinic

T1-T2 Shuffling: Multi-Contrast 3D Fast Spin-Echo with T1 and T2 Sensitivity

T1-T2 Shuffling is an MRI acquisition and reconstruction method based on 3D Fast Spin-Echo, and extends T2 Shuffling. The method mitigates image blur and rerospectively synthesizes T1-weighted and T2-weighted volumetric images. By varying the repetition times (TR) accross the different echo trains, T1 sensitivity is encoded in the imaging data. The TR values are chosen based on maximizing Fisher Information for T1 estimation. A joint T1-T2 subspace is computed from an ensemble of simulated FSE signal evolutions, and linear combinations of the subspace coefficients are computed to generate synthetic T1-weighted and T2-weighted image contrasts.

T2 Shuffling: Sharp, Multi-Contrast, 3D Fast Spin-Echo MRI

T2 Shuffling is an MRI acquisition and reconstruction method based on 3D Fast Spin-Echo. The method accounts for temporal dynamics during the echo trains to reduce image blur and resolve multiple image contrasts along the T2 relaxation curve. The echo train ordering is randomly shuffled during the acquisition according to variable density Poisson disk sampling masks. The shuffling leads to reduced image blur at the cost of noise-like artifacts. The artifacts are iteratively suppressed in a regularized reconstruction based on compressed sensing, and the full signal dynamics are recovered.

Berkeley Advanced Reconstruction Toolbox (BART)

BART is a collection of tools for prototyping new MRI reconstruction methods and integrating them into the clinic.

Jan. 19, 2016: We gave a demo of BART at the 2016 ISMRM Workshop on Data Sampling and Image Reconstruction. You can find all the materials presented at the workshop, including quick installation steps and demo walkthroughs, here: Bart Workshop Materials

Logo credit: Michelle Tamir

Undergraduate Researchers

I like working with undergraduates on interesting projects. If you are interested, please contact me!

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