Erjun Zhang, Ph.D.

[expand]

[expand all]

[collapse all]

Education

PhD in Biomedical Engineering, NeuroPoly, 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

Conventional DTI often struggles to resolve complex microstructural environments, particularly in rapidly developing neonatal brain. To overcome this, I developed the Diffusion Bubble Model (DBM). By modeling water diffusion as a continuous spectrum of sub-voxel compartments, it utilizes spectral visualization and pattern metrics to precisely track brain maturation and disease pathology.


(A) DTI: Diffusion tensor map.
→

(B) Diffusion bubble model.
→

(C) DBM spectrum.
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

(A) DBM resolves complex microstructural gradients and pathology timelines in neonatal PWML without geometric priors.

(B) DBM captures rapid microstructural maturation and regional anisotropy in the neonatal Corpus Callosum (CC).
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): Quebec Bio-Imaging Network (QBIN) & Fonds de Recherche du Quebec-Sante (FRQS), 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 (Detailed record of independent student supervision is available here)
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.
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.
• Zhu H, Zhang EJ, Liu X. Advanced Mathematics Supplemental Reading Book, BDIC, 2018.

Journal Publications & PhD Thesis

PhD Thesis & Peer-Reviewed Journal Articles: Medical Imaging & Neurodevelopment
The Diffusion Bubble Model: A Spectrum-Based Diffusion MRI Framework for Neonatal Brain Segmentation, Developmental Assessment, and Injury Characterization PhD Thesis, Polytechnique Montréal, 2026. [PDF]
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]
Synthetic analogue of adrenocorticotropic hormone, ACTH(4-7)PGP delays neurological manifestations in diseases of mucopolysaccharidosis III spectrum by reducing neuroinflammation and rescuing neurotransmission, synaptogenesis, and axonal demyelination [Preprint] bioRxiv, 2026.
Moore T, Dubot P, Viana G, Bose P, Zhang EJ, Nasseri B, Pan X, Robertson DN, Feulner LM, Taherzadeh M, van Vliet PP, Bonneil É, Khan S, Zhang L, Attanasio F, Singamsetty S, Durcan TM, Tomatsu S, Thibault P, Morales CR, Di Cristo G, Andelfinger G, Piñeyro G, Boehm J, Lodygensky GA, Wood J, Pshezhetsky AV. [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, 2022, 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]

Invited Talks & Seminars

• Decoding Neonatal Brain Microstructure: From Discrete Compartments to the Continuous Spectrum of DBM.
  Invited Lecture, FNNDSC Lecture Series, Boston Children’s Hospital / Harvard Medical School, March 11, 2026.

Selected Conference Publications

Microstructural Subtyping Of Neonatal Punctate White Matter Lesions Via The Diffusion Bubble Model Predicts Long-Term Neurodevelopmental Outcomes
EAPS 2026 (12th Congress of the European Academy of Paediatric Societies), Athens, Greece, 2026.
Zhang EJ, DeLeener B, Lodygensky G. [Oral Presentation]
Assessment of a central nervous system demyelination in Sanfilippo disease by diffusion tensor imaging: a non-invasive method to evaluate disease progression and therapeutic response
4th International Conference on Sanfilippo Syndrome and Related Diseases, Geneva, Switzerland, 2025.
Zhang EJ, Moore T, Dubot P, Wood J, Lodygensky GA, Pshezhetsky AV. [Oral Presentation]
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.
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. [Online Oral Presentation]

Mentoring & Educational Publications

Mentored Student Research
Global Z-score Normalization Enables Training-free Automatic Detection of Punctate White Matter Lesions in Neonates
McGill Science Undergraduate Research Journal (MSURJ) OSE, Montreal, Canada, 2026.
Wat HC, Xu K, Zhang EJ (mentor), Lodygensky G.
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.
Identifying, Segmenting and Analyzing PWMLs
CRBLM Careers and Trajectories Panel, Montreal, Canada, 2025.
Baker K*, Gornostayev F*, Wat HC*, Cox S, Nadeau H, Zhang EJ (mentor), De Leener B, Lodygensky G.
[Poster]
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]
Books & Educational Publications
Linear Algebra
Liu X, Zhang EJ.
Beijing Dublin International College (BDIC), 2017. [pdf]
Advanced Mathematics Supplemental Reading Book
Zhu H, Zhang EJ, Liu X.
Beijing Dublin International College (BDIC), 2018. [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

Page content updated:
Statistics: visitors, views (since March 31, 2023)