Erjun Zhang, PhD

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Education

PhD in Biomedical Engineering, NeuroPoly Lab, Polytechnique Montreal, University of Montreal, Montreal, Canada, 2025
MEng-Research in Optical Engineering, Beijing University of Technology, Beijing, China, 2018
BEng in Optoelectronic Information Engineering, Shenzhen University, Shenzhen, China, 2014

Research Framework & Projects

I. Methodological Innovation
Diffusion Bubble Model (DBM): Developed a spectrum-based dMRI framework to resolve sub-voxel heterogeneity. Demonstrated clinical utility in separating lesions from normal tissue (NeuroImage 2025). [Open Source Coming Soon]
II. Reproducible Workflows
Neonatal Processing Pipeline: Built atlas-informed segmentation and registration workflows robust to motion and low-resolution clinical data.
End-to-End Mouse MRI Pipeline: (New) Developed a fully automated preprocessing pipeline for mouse brain T1w/T2w/dMRI to support translational validation.
III. Clinical Translation
Neonatal Brain Injury & Development: Characterized maturation trajectories (34-40 weeks PMA) and detected Punctate White Matter Lesions (PWML) with high specificity.
Neurodegeneration (Spinal Cord): (In Progress) Applied DBM to Parkinson's disease spinal cord imaging, successfully distinguishing between different disease stages (severity stratification).
IV. Quantitative Analysis Tools
Microstructure-Informed Segmentation: Leveraged DBM and diffusion metrics to refine brain tissue segmentation (completed in PhD thesis).


V. Future Roadmap (Collaboration Opportunities)
Non-CNS MRI Expansion: Adapting diffusion MRI models for cardiac, abdominal (liver/kidney), and muscle imaging.
Broadening Disease Applications: Applying DBM to characterize complex pathologies beyond injury, such as Multiple Sclerosis (MS) and tumor.
Methodological Refinement:
• Constructing population-specific parameter templates.
• Refining diffusion models with rigorous mathematical constraints.
Deep Learning & Validation:
• Benchmarking against histology or multimodal imaging.
• Developing Physics-Informed Neural Networks for metrics estimation.

Awards & Grants

Mentorship Grant (×2): From Quebec Bio-Imaging Network (QBIN) & Fonds de Recherche du Quebec-Sante (FRQS) , July 2023 & 2024
University of Montreal & China Scholarship Council Joined Scholarship, August 2020 - July 2023
Academic Innovation Award (×2): From Beijing University of Technology, 2017 & 2018
Academic Excellence Award: From Beijing University of Technology, 2015 - 2016

Teaching & Mentoring

Lead Research Mentor & Curriculum Developer
"Introduction to Python-based MRI Data Analysis" Series March 2023 – Present (Annual Cohorts)
Developed and led a multi-year mentorship program training over 10+ students from Neuroscience (Dawson), Computer Science (Xi'an University of Technology), Medicine (Univeristy of Montreal) and Engineering (McGill University and Waterloo University) backgrounds.
• 2026 (Part IV): Leading a cross-institutional collaboration between Dawson College (Canada) and Xi'an University of Technology (China). Guiding CS students on scalable DBM pipelines and fiber tracking algorithms.
• 2023-2025 (Part I-III): Hosted internships via the QBIN Program and FRQS grants. Supervised projects on brain development (2023), injury-focused characterization (2024) segmentation & machine learning (2025).
• Outcomes: Supported 8+ student-led conference abstracts (and poster presentations), mentored one award-winning student presentation and one best-intern award, and several open source github repositories.
Teaching Assistant (Specialized Workshop)
Machine Learning in fMRI using Nilearn | Montreal Artificial Intelligence and Neuroscience (MAIN 2022)Dec. 2022
Assisted in training researchers on implementing ML pipelines for neuroimaging data using the Nilearn ecosystem.
Graduate Teaching Assistant & Textbook Author
Beijing-Dublin International College (Joint program with University College Dublin), 2016-2018
Facilitated English-medium instruction for dual-degree engineering programs.
Coordinated tutorials for core mathematics modules: Linear Algebra and Introduction to Analysis.
Co-authored two textbooks to support the undergraduate curriculum:
• Liu X, Zhang EJ. Linear Algebra, BDIC, 2017. [Book]
• Zhu H, Zhang EJ, Liu X. Advanced Mathematics Supplemental Reading Book, BDIC, 2018. [Book]

Journal Publications

Peer-Reviewed Journal Articles: Medical Imaging & Neurodevelopment
Diffusion Bubble Model: A Novel MRI Approach for Detection and Subtyping of Neonatal Punctate White Matter Lesions Neuroimage, 2025.
Zhang EJ, Leener BD., Lodygensky G. [pdf]
Severe Central Nervous System Demyelination in Sanfilippo Disease Frontiers in Molecular Neuroscience, 2023 16:1323449.
Taherzadeh M, Zhang EJ, Londono I, Leener BD, Wang S, Cooper J, Kennedy T, Morales C, Chen ZS, Lodygensky G, Pshezhetsky A [pdf]
Non-invasive in vivo MRI detects long-term microstructural brain alterations related to learning and memory impairments in a model of inflammation-induced white matter injury Behavioural Brain Research, 428, 113884.
Pierre C, Zhang EJ, Londono I, DeLeener B, Lesage F, and Lodygensky G [link]
Peer-Reviewed Journal Articles: Physical Engineering & Optics
Improving Thermo-optic Properties Of Smart Windows Via Coupling To Radiative Coolers Applied Optics , 59 , no. 13 (2020): D210-D220.
Zhang EJ, Cao Y, Caloz C, Skorobogatiy M. [pdf]
Simple Models For The Operation Of Partially Transparent Radiative Windows and their comparison to the radiative coolers arXiv:1906.07638.
Zhang EJ, Caloz C, Skorobogatiy M. [link]

Conference & Educational Publications

Mentored Student Research
Characterizing Neonatal PWMLs: A Cohort Study from the dHCP
92e Congrès de l'Acfas 2025, Montreal, Canada, 2025.
Emadoye J, Ton That H, Xu K, Zhang EJ (mentor), Nadeau H, Cox S, Lodygensky G, De Leener B.
An Assessment of the Development and Maturation of Neonatal Brain Tissues Using Diffusion MRI
91e Congrès de l'Acfas 2024, Ottawa, Canada, 2024.
Xu K, Hernandez S, Zhang EJ (mentor), Lodygensky G.
Evaluation of Neonatal Brain Tissue Development Using Diffusion MRI
CHU Sainte-Justine Summer Internship Conference 2023, Montreal, Canada, 2023.
Xu K, Hernandez S, Zhang EJ (mentor), DeLeener B, Lodygensky G.
[Reports] [Slides] [Video] [PPT] [Poster]
Selected Research Presentations
Diffusion Bubble Model: A Novel Method For Detecting Neuroinflammation in Mouse Brain
ISMRM 2023: Latest-breaking session, Imaging The Fire In The Brain, Toronto, Canada, 2023.
Zhang EJ, Londono I, Fouquet J, Pshezetsky A, DeLeener B, Lodygensky G. [Abstract][Slides][Video] [Oral Presentation]
Impacts of Prematurity on Neonatal Deep Gray Matter Using Diffusion Basis Spectrum Imaging
OHBM, Montreal, Canada, 2023.
Zhang EJ, et al. [Abstract][Poster]
Evaluation Of Neonatal Brain White Matter Development Using DBSI
PAS, Washington, D.C., USA, 2023.
Zhang EJ, DeLeener B, Lodygensky G. [Poster]
T1w/T2w Ratio Improves Detection of Neonatal Punctate White Matter Lesions
CNPRM | DOHaD | ENRICH | CAMCCO 2025, Montreal, Canada, 2025.
Zhang EJ, Emadoye J, Xu K, Ton That H, De Leener B, Lodygensky G.
A Novel Method For Evaluation of Neonatal Brain Development
10th CNPRM, Montebello, Canada, 2023.
Zhang EJ, DeLeener B, Lodygensky G. [Thematic Oral Presentation]
Simple Models For The Operation Of Partially Transparent Radiative Windows
RIAO, Cancun, Mexico, 2019.
Zhang EJ, Caloz C, Skorobogatiy M. [Oral Presentation]
Books & Educational Publications
Linear Algebra
Liu X, Zhang EJ.
Beijing Dublin International College (BDIC), 2017. [Book PDF]
Advanced Mathematics Supplemental Reading Book
Zhu H, Zhang EJ, Liu X.
Beijing Dublin International College (BDIC), 2018. [Book PDF]

Opensource

• An Introduction to Python-based Medical Data Processing and Analysis, [Table of contents], [Data & Python code], [Slides], [Video], [PPT], [Poster], 2023
• UNet Model and Data Augmentation, [Data & Python code], [report], 2021
• Use DTI-TK to build diffusion MRI template and do registration, [github & Python code], [report], 2022
• Diffusion MRI Reconstruction From Scratch, [github & Python code], [report], 2022

Short Bio

Dr. Erjun Zhang is a biomedical researcher specializing in diffusion MRI modeling and quantitative biomarkers for brain, spinal cord, and body injury and development. He received his PhD in Biomedical Engineering from Polytechnique Montreal (2025) and his MEng in Optical Engineering from Beijing University of Technology (2018). His doctoral work introduced the Diffusion Bubble Model (DBM) to characterize sub-voxel heterogeneity in neonatal brain injury. Dr. Zhang combines rigorous physical modeling with reproducible computational pipelines to translate advanced neuroimaging techniques into clinical applications.

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