Mathieu Boudreau

Projects

Figures from my own work, the open datasets behind them, and the publishing infrastructure they were built on.

Anatomy-based B0 field map simulation
Four-stage pipeline. A sagittal T1-weighted MRI of the head and torso is segmented into coloured anatomical labels covering skull, brain, vertebrae and soft tissue; the labels are converted to a susceptibility map; the susceptibility map is converted to a delta B-zero field map shown in red and blue.
From a T1-weighted scan to a simulated ΔB0 field map: segmentation into anatomical labels, conversion to a tissue susceptibility map, then to the field map used for shim-coil development. Built for whole-spine, an open database of 60 head-to-torso datasets.
The same segmentation in three dimensions, rotating as it cycles through the labelled structures — skull and vertebrae, brain and spinal cord, airway and lungs. These are the labels the susceptibility and field maps above are derived from. Presented at ISMRM 2025.

T1 mapping reproducibility challenge
Top: T1 estimates plotted against reference values, with inter-submission and intra-submission scatter and percentage error. Below: T1 values for white matter genu, splenium, cortical grey matter and deep grey matter, plotted per submission, showing the spread across sites.
The same phantom measured at 27 imaging sites across three scanner vendors. Each cluster is one submission; the spread between them is the reproducibility problem the challenge set out to quantify. Co-first author, Magnetic Resonance in Medicine, 2024 — a top-10 most-cited and top-10% most-viewed article of that year. Data at OSF.

Longitudinal quantitative MRI
Study design: six participants scanned at ten time points on a 3T scanner between 2019 and 2022. Brain acquisitions include T1-weighted, MP2RAGE, MT saturation and diffusion; spine acquisitions include T1-weighted, T2-weighted, multi-echo gradient echo, MT saturation and diffusion-weighted imaging.
Six participants, up to ten sessions each, over three years — brain and cervical spinal cord, with the quantitative contrasts each measurement targets. The question was how much a qMRI biomarker drifts when nothing about the subject has changed. First author, Imaging Neuroscience, 2025; 10.1162/imag_a_00409.

B1 mapping for T1 bias correction
Axial brain images comparing five approaches across two acquisitions: nominal, reference double angle, Bloch-Siegert, actual flip angle imaging, and echo-planar double angle. Below, the corresponding B1 maps in a rainbow colour scale and the resulting T1 maps.
Five ways of measuring the transmit field, and what each one does to the T1 map derived from it. Choosing wrongly biases every downstream measurement, which is the thread running through most of my work. First author, Journal of Magnetic Resonance Imaging, 2017 — my most-cited first-author paper.

Open datasets

4
  • whole-spine Open BIDS database of 60 head-to-torso anatomical MRI datasets with segmentation labels, tissue susceptibility maps, and 60 anatomy-based simulated ΔB0 field maps for brain and spine shim-coil development. First author of the associated study. 2024. OpenNeuro ds005616
  • CNeuroMod anatomical quantitative MRI dataset Longitudinal brain and cervical spinal-cord quantitative-MRI dataset: 6 participants scanned up to 10 times over 3 years (T1, MTR, MTsat, diffusion), released in BIDS with fully reproducible, containerized processing pipelines. First author of the associated study. Part of the Courtois NeuroMod project. Initial release 2022. doi:10.1162/imag_a_00409
  • ISMRM T1-mapping reproducibility challenge dataset Open database of 95 standardized T1-mapping datasets crowdsourced from 27 imaging sites across three scanner vendors, with raw acquisitions and processed T1 maps. Co-first author of the associated study. ~2020. osf.io/ywc9g
  • qMRLab demonstration datasets Curated example quantitative-MRI datasets supporting the qMRLab toolbox tutorials and documentation. osf.io/tmdfu

Reproducible publishing

4
  • Quantitative MRI mOOC A free open course on quantitative MRI, published through NeuroLibre with runnable notebooks. First author and first editor. See Teaching.
  • NeuroLibre Contributor to a preprint server for executable, fully reproducible neuroscience publications. Remotely mentored Skopje-based graduate students, including Kiril Zelenkovski and Filip Velkov, in building interactive, reproducible research notebooks, 2020 – 2023. Member, Communications division, Canadian Open Neuroscience Platform, 2019 – present; Member, Operations Committee, Canadian Open Neuroscience Platform, 2021 – 2023.
  • T1/T1ρ mapping methods — interactive book chapter Authored the T1-mapping chapter in Quantitative Magnetic Resonance Imaging (Academic Press, 2020) and engineered a novel workflow to make it fully interactive and reproducible: containerized MATLAB/Python notebooks (Docker/MyBinder) that run the analysis in-browser, generate interactive Plotly figures, and produce the published output from a single source.
  • Longitudinal medical-imaging reliability report Built the reliability/variability analysis across 3 years of repeated measurements for a large multi-year longitudinal medical-imaging dataset, ran containerized processing pipelines to extract metrics, and delivered a fully reproducible, interactive report (Docker, Jupyter, Plotly).