Medical Imaging Research Using Deep Learning

BIDS Health and Life Sciences Lead Maryam Vareth is offering this project (#1) through UC Berkeley's Undergraduate Research Apprentice Program (URAP) for the Spring 2022 academic semester. Eligible undergraduates may apply online January 11-24, 2022.

Project Description

One of the fastest growing fields of research in medical imaging during the last several years is the use of machine learning methods for image reconstruction.

This project aims to use deep learning approaches in image reconstruction to accelerate Magnetic Resonance Imaging (MRI) acquisition and in result reduce MRI examination times for patients. Two of the most influential development in this area during the last two decades have been parallel imaging and compressed sensing. Both of these rapid imaging techniques are based on the principle of reducing the number of lines that are acquired in k-space, which reduces the scan time and then exploiting the redundancy in measured data during the image reconstruction. In Parallel imaging, the redundancy arises from the simultaneous acquisition of MR signal with multiple receive coils; in compressed sensing, it derives from the observation that images are generally compressible. Machine learning approaches have adopted similar strategies for the acceleration of MRI, which set the main design criteria for this project. The majority of existing work have focused on designing better reconstruction models given a pre-determined acquisition trajectory, ignoring the question of trajectory optimization. In this project, we also focus on learning acquisition trajectories given a fixed image reconstruction model.

To make the image reconstruction problem realistic, we will use a large-scale database of raw (complex-valued) k-space data obtained directly from MRI scanners (

The specific selection of tasks will depend on the skill sets and interest of the students and could include developing, implementing, refining, and testing algorithms and workflows to achieve the specific goals of this project. The students will work in teams and closely with graduate students and post-docs.

Proposed Tasks:
• Re-implementation and/or standardization of existing approaches
• Writing modules for validation
• Generic testing and debugging
• Presenting work at group meetings
• Formal presentation at the end of the semester to BIDS community
• Upon successful progress, contribute to a manuscript

fastMRI: An Open Dataset and Benchmarks for Accelerated MRI

Students from various majors are encouraged to apply, including but not limited to EECS, BioE, CS, and Data Science. We are looking for 2 highly inquisitive students who have:

Required Qualifications: 
• Interest in open source software development, data science, medical imaging, machine learning, engineering and healthcare research
• Great teamwork (e.g. communication skills, punctuality, organization)
• Proficiency in programming languages (Python and/or MATLAB)
• Working knowledge of Tensorflow/Keras or Pytorch
• Working knowledge of version control (e.g. GitHub)
• Familiarity with Linux/Unix environment

Recommended Qualifications: 
• Working knowledge of basic machine learning and deep learning (cost function, cross-validation, overfitting, error analysis, etc)
• Working knowledge of signal processing and Image processing
• CS 188 and/or CS 189 • EE 120 and/or EE 145B

Off-Campus Research Site: During the Fall 2020 semester, we will only meet and communicate via Zoom, Slack,and email.

Related websites:


Spring 2022 BIDS Undergraduate Internships - Apply January 11-24
Fall 2021 BIDS Undergraduate Internships - Apply August 18-30
Spring 2021 BIDS Undergraduate Internships - Apply January 12-25 
Fall 2021 BIDS Undergraduate Internships - Apply August 19-31

BIDS Affiliates

Maryam Vareth

Health and Life Sciences Lead