Erjun Zhang

PhD in Biomedical Engineering
Institute of Biomedical Engineering
Polytechnique Montreal, University of Montreal
Lab: NeuroPoly (L5626, the green floor at Poly)
Room: 1.7.13, TransMedTech Institute, CHU Sainte-Justine Research Center
Email: erjunzhang [at] outlook [dot] com
Status: Seeking Postdoctoral Opportunities (International) | Availability: Immediate (Holding 3-year Canadian Open Work Permit)
GitHub LinkedIn Google Scholar CV

I am a medical imaging researcher specializing in diffusion MRI biophysical modeling and its clinical translation. My work revolves around advancing diffusion MRI to study neurodevelopment and injury. During my PhD at Polytechnique Montreal and CHU Sainte-Justine, I introduced the Diffusion Bubble Model (DBM) to detect and subtype punctate white matter lesions. Currently, I am leading a cross-institutional research initiative—integrating teams from Canada and China—to build scalable, reproducible MRI workflows (QC, registration, and segmentation) that bridge the gap between advanced modeling and large-scale clinical cohorts.

My research goal is to advance biophysical modeling and benchmarking frameworks to translate quantitative MRI biomarkers into precision medicine for development and injury across the brain, spinal cord, and body.

News:

Mar 11, 2026: Invited talk, FNNDSC Lecture Series, Boston Children's Hospital / Harvard Medical School (Boston, MA).
Feb 2026: Excited to return to Dawson College for the 4th consecutive year as a Research Mentor, leading a remote computational project for the 2026 summer cohort.
Jan 2026: Completed the development of an end-to-end mouse brain MRI preprocessing and analysis pipeline (T1w/T2w/dMRI).
Nov 2025: Transitioned to a Research Fellow role at CHUSJ, focusing on the finalization of $DBM$ manuscripts and interdisciplinary collaborations.
Oct 2025: Officially completed PhD studies and received completion letter (successfully defended on Sep 9, 2025).
June 2025: First-author paper accepted in NeuroImage: Diffusion Bubble Model: A Novel MRI Approach for Detection and Subtyping of Neonatal PWML .

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Education

PhD in Biomedical Engineering Oct 2025
Institute of Biomedical Engineering, Polytechnique Montreal
Thesis: DBM: A Spectrum-Based Diffusion MRI Framework for Neonatal Brain Segmentation, Developmental Assessment, and Injury Characterization.
Supervisors: Prof. Benjamin De Leener & Prof. Gregory A. Lodygensky.
MEng in Optical Engineering July 2018
Beijing University of Technology, China
Thesis: Numerical Simulation and Image Reconstruction for Structured Illumination Super-Resolution Fluorescence Microscopy.
Focus: Computational Imaging, Inverse Problems, Advanced Optics.
BEng in Optoelectronic Information Engineering June 2014
Shenzhen University, China
Focus: Digital Image Processing, Information Optics, and Image Analysis

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

Teaching & Mentoring

Publications

Opensource

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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