Info for prospective students

Hello

Thanks for your interest in joining the lab :-)

Research in the lab

The lab focuses on developing machine learning (ML) techniques for medical imaging with MRI, for example:

• • ML methods for image reconstruction from sub-sampled data - theory & algorithms.

  • ML methods for enhancing image quality, e.g. improved signal-to-noise (SNR) in low-field MRI.

  • ML motion correction techniques.

  • ML techniques for next-generation MRI scanners, low-field MRIs, which are small and portable

  • 3D-printing of realistic phantoms.

  • Acquiring new MRI data, based on sampling patterns that we design.

  • Personalized healthcare algorithms.

Desired theoretical background (coursework)

Applicants are expected to have substantial background in signal and image processing.  
Specifically, the following Technion courses (or similar) are very useful:

• • Introduction to Digital Signal Processing (DSP) course (ECE 044198) - this is essential.

  • Image processing course (ECE 046200 or similar) 

  • Introduction to machine learning (ECE 046195 , BME 336546  or similar)

  • Deep learning courses  (ECE 046211 , CS 236605 , or similar)

Please see below a list of other relevant courses.

Desired hands-on experience:

• Substantial hands-on coding experience in Python / Matlab is required

• Experience in computer vision / deep learning (e.g. from undergrad projects or industry) is helpful

• Hands-on experience in 3D-Printing would also be great

Interested in joining?

Please email me - efrat.s at technion dot ac dot il

When applying, please attach your CV and grades transcript and mention which program (degree & department) you're applying for. Please also mention which relevant courses you took, any previous experience (e.g. projects in industry or academia), and describe your research interests.

I get many applications, and I do my best to answer all of them. However, sometimes I miss emails because I receive high volumes of emails. If you didn't get a response after a few days, please resend the email. 

Looking forward to hearing from you! :-)

Thanks,

Efrat

Courses that I will teach in Spring 2025: 

1. Introduction to magnetic resonance imaging (MRI) (BME 336504) - this provides an introduction to MRI physics (spin dynamics), the imaging process, and basic algorithms of image reconstruction from measurements. This course provides hands-on experience via simulations and programming basic MRI techniques (pulse sequences) in python. New! ECE students can now take this course as part of 3 elective "chains" (שרשראות בחירה) - (1) signal and image processing, (2) biological signals and systems, (3) electromagnetics and photonics.

2. Advanced topics in deep learning for medical imaging (ECE 0480350) - in this course, we will learn state-of-the-art deep learning methods for MRI reconstruction by reviewing recent papers and ongoing work in the field. 

Courses - more info

The following courses are relevant to the research in our lab:

Basic Courses in signal & image processing - these are highly needed

• • Signals and systems (ECE 044130)

  • Introduction to Digital Signal Processing (044198) - this is essential. 

  • Image processing (ECE 046200 / CS 236860) or medical image processing (BME 336027)

  • Alternatively, knowledge of the Fourier transform, Nyquist sampling, and aliasing is essential.

Machine learning - basic courses

• • Introduction to machine learning* (ECE 046195 , BME 336546  or 096411 or similar)

  • Introduction to unsupervised learning (ECE 046202)

  • Algebraic methods for data science (095295)

  • Deep learning course  (ECE 046211 , CS 236605 , or similar)

  • Deep learning on computational accelerators (CS 236781)

Medical imaging courses

• • Introduction to medical imaging (basics of MRI, CT etc.) - BME 336502* or 046831 (Guy Gilboa's course - given in spring)

  • Principles of MRI (magnetic resonance imaging) BME 336504 - I will teach this course every spring starting March 2025  

  • Introduction to medical image processing* (BME 336027)

  • Deep learning applications in MRI* (BME 3380028) (Moti Freiman's course - given in spring)

  • Advanced topics in medical image reconstruction (ECE 480350) - My new course - will be given every spring

  • Advanced topics in medical image analysis (BME 3360033) (Moti Freiman's course - given in spring)

  • Lab in Biomedical Engineering 3 (335003) - some labs focDr. Moti Freiman's 

Optimization courses

• • Introduction to optimization* (046197 or 96311 or 97311 or 98311 or 236330) 

  • Nonlinear models in operations research (096327)

  • Numerical algorithms* (CS 234125) 

Advanced courses in computer vision & machine learning

• Algorithms and applications in computer vision (ECE 046746)

• Generative AI - Diffusion models - Michael Elad's new course (CS 236759) New! (spring 2024/2025) - highly recommended

• Diffusion Models - Miki Elad's course* (CS 236610)  - all the lectures are available on Youtub. See the course's website (scroll to the bottom).

• Digital Signal Processing ECE 046745 (Guy Gilboa's course)

• Variational Methods in Image Processing (ECE 049064) (Guy Gilboa's course)

• Advanced Topics in Deep Learning: Transformers (ECE 046010) (Yosi Keshet's course)

*Please notice: ECE students can take only up 9 points of courses from other departments and those courses must appear in a specific list - please see here.