Portrait of Julien Cohen-Adad

Julien Cohen-Adad

Associate Academic Member
Associate Professor, Polytechnique Montréal, Electrical Engineering Department
Adjunct Professor, Université de Montréal, Department of Neuroscience
Research Topics
Medical Machine Learning

Biography

Julien Cohen-Adad is a professor at Polytechnique Montréal and the associate director of the Neuroimaging Functional Unit at Université de Montréal. He is also the Canada Research Chair in Quantitative Magnetic Resonance Imaging.

His research focuses on advancing neuroimaging methods with the help of AI. Some examples of projects are:

- Multi-modal training for medical imaging tasks (segmentation of pathologies, diagnosis, etc.)

- Adding prior from MRI physics to improve model generalization

- Incorporating uncertainty measures to deal with inter-rater variability

- Continuous learning strategies when data sharing is restricted

- Bringing AI methods into clinical radiology routine via user-friendly software solutions

Cohen-Adad also leads multiple open-source software projects that are benefiting the research and clinical community (see neuro.polymtl.ca/software.html). In short, he loves MRI with strong magnets, neuroimaging, programming and open science!

Current Students

Master's Research - Polytechnique Montréal
Co-supervisor :
PhD - Polytechnique Montréal
Co-supervisor :
PhD - Polytechnique Montréal
Master's Research - Polytechnique Montréal
PhD - Polytechnique Montréal
PhD - Polytechnique Montréal
Collaborating researcher
Research Intern - Polytechnique Montréal
Master's Research - Université de Montréal
Master's Research - Polytechnique Montréal
Postdoctorate - Polytechnique Montréal

Publications

Longitudinal reproducibility of brain and spinal cord quantitative MRI biomarkers
Mathieu Boudreau
Agah Karakuzu
Arnaud Boré
Basile Pinsard
Kiril Zelenkovski
Eva Alonso‐Ortiz
Julie Boyle
Lune Bellec
Quantitative MRI (qMRI) promises better specificity, accuracy, repeatability, and reproducibility relative to its clinically-used qualitativ… (see more)e MRI counterpart. Longitudinal reproducibility is particularly important in qMRI. The goal is to reliably quantify tissue properties that may be assessed in longitudinal clinical studies throughout disease progression or during treatment. In this work, we present the initial data release of the quantitative MRI portion of the Courtois project on neural modelling (CNeuroMod), where the brain and cervical spinal cord of six participants were scanned at regular intervals over the course of several years. This first release includes three years of data collection and up to ten sessions per participant using quantitative MRI imaging protocols (T1, magnetization transfer (MTR, MTsat), and diffusion). In the brain, T1MP2RAGE, FA, MD, and RD all exhibited high longitudinal reproducibility (intraclass correlation coefficient— ICC ≃ 1 and within-subject coefficient of variations— wCV 1%). The spinal cord cross-sectional area (CSA) computed using T2w images and T1MTsat exhibited the best longitudinal reproducibility (ICC ≃ 1 and 0.7 respectively, and wCV 2.4% and 6.9%). Results from this work show the level of longitudinal reproducibility that can be expected from qMRI protocols in the brain and spinal cord in the absence of hardware and software upgrades, and could help in the design of future longitudinal clinical studies.
Longitudinal reproducibility of brain and spinal cord quantitative MRI biomarkers
Mathieu Boudreau
Agah Karakuzu
Arnaud Boré
Basile Pinsard
Kiril Zelenkovski
Eva Alonso‐Ortiz
Julie Boyle
Lune Bellec
Quantitative MRI (qMRI) promises better specificity, accuracy, repeatability, and reproducibility relative to its clinically-used qualitativ… (see more)e MRI counterpart. Longitudinal reproducibility is particularly important in qMRI. The goal is to reliably quantify tissue properties that may be assessed in longitudinal clinical studies throughout disease progression or during treatment. In this work, we present the initial data release of the quantitative MRI portion of the Courtois project on neural modelling (CNeuroMod), where the brain and cervical spinal cord of six participants were scanned at regular intervals over the course of several years. This first release includes three years of data collection and up to ten sessions per participant using quantitative MRI imaging protocols (T1, magnetization transfer (MTR, MTsat), and diffusion). In the brain, T1MP2RAGE, FA, MD, and RD all exhibited high longitudinal reproducibility (intraclass correlation coefficient— ICC ≃ 1 and within-subject coefficient of variations— wCV 1%). The spinal cord cross-sectional area (CSA) computed using T2w images and T1MTsat exhibited the best longitudinal reproducibility (ICC ≃ 1 and 0.7 respectively, and wCV 2.4% and 6.9%). Results from this work show the level of longitudinal reproducibility that can be expected from qMRI protocols in the brain and spinal cord in the absence of hardware and software upgrades, and could help in the design of future longitudinal clinical studies.
Reproducible Spinal Cord Quantitative MRI Analysis with the Spinal Cord Toolbox.
Jan Valošek
SCIsegV2: A Universal Tool for Segmentation of Intramedullary Lesions in Spinal Cord Injury
Enamundram Naga Karthik
Jan Valošek
Lynn Farner
Dario Pfyffer
Simon Schading-Sassenhausen
Anna Lebret
Gergely David
Andrew C. Smith
Kenneth A. Weber
Maryam Seif
Rhscir Network Imaging Group
Patrick Freund
Towards contrast-agnostic soft segmentation of the spinal cord
Sandrine Bédard
Enamundram Naga Karthik
Charidimos Tsagkas
Emanuele Pravatà
Cristina Granziera
Andrew C. Smith
Kenneth Arnold Weber
Spinal cord segmentation is clinically relevant and is notably used to compute spinal cord cross-sectional area (CSA) for the diagnosis and … (see more)monitoring of cord compression or neurodegenerative diseases such as multiple sclerosis. While several semi and automatic methods exist, one key limitation remains: the segmentation depends on the MRI contrast, resulting in different CSA across contrasts. This is partly due to the varying appearance of the boundary between the spinal cord and the cerebrospinal fluid that depends on the sequence and acquisition parameters. This contrast-sensitive CSA adds variability in multi-center studies where protocols can vary, reducing the sensitivity to detect subtle atrophies. Moreover, existing methods enhance the CSA variability by training one model per contrast, while also producing binary masks that do not account for partial volume effects. In this work, we present a deep learning-based method that produces soft segmentations of the spinal cord. Using the Spine Generic Public Database of healthy participants (
Effectiveness of regional diffusion MRI measures in distinguishing multiple sclerosis abnormalities within the cervical spinal cord
Haykel Snoussi
Olivier Commowick
Benoit Combes
Elise Bannier
Slimane Tounekti
Anne Kerbrat
Christian Barillot
Emmanuel Caruyer
Multiple sclerosis (MS) is an inflammatory disorder of the central nervous system. Although conventional magnetic resonance imaging (MRI) is… (see more) widely used for MS diagnosis and clinical follow‐up, quantitative MRI has the potential to provide valuable intrinsic values of tissue properties that can enhance accuracy. In this study, we investigate the efficacy of diffusion MRI in distinguishing MS lesions within the cervical spinal cord, using a combination of metrics extracted from diffusion tensor imaging and Ball‐and‐Stick models.
Influence of preprocessing, distortion correction and cardiac triggering on the quality of diffusion MR images of spinal cord
Kurt G Schilling
Anna J. E. Combes
Karthik Ramadass
Francois Rheault
Grace Sweeney
Logan Prock
Subramaniam Sriram
John C Gore
Bennett A Landman
Seth A. Smith
Kristin P. O’Grady
Diffusion MRI of the spinal cord (SC) is susceptible to geometric distortion caused by field inhomogeneities, and prone to misalignment acro… (see more)ss time series and signal dropout caused by biological motion. Several modifications of image acquisition and image processing techniques have been introduced to overcome these artifacts, but their specific benefits are largely unproven and warrant further investigations. We aim to evaluate two specific aspects of image acquisition and processing that address image quality in diffusion studies of the spinal cord: susceptibility corrections to reduce geometric distortions, and cardiac triggering to minimize motion artifacts. First, we evaluate 4 distortion preprocessing strategies on 7 datasets of the cervical and lumbar SC and find that while distortion correction techniques increase geometric similarity to structural images, they are largely driven by the high-contrast cerebrospinal fluid, and do not consistently improve the geometry within the cord nor improve white-to-gray matter contrast. We recommend at a minimum to perform bulk-motion correction in preprocessing and posit that improvements/adaptations are needed for spinal cord distortion preprocessing algorithms, which are currently optimized and designed for brain imaging. Second, we design experiments to evaluate the impact of removing cardiac triggering. We show that when triggering is foregone, images are qualitatively similar to triggered sequences, do not have increased prevalence of artifacts, and result in similar diffusion tensor indices with similar reproducibility to triggered acquisitions. When triggering is removed, much shorter acquisitions are possible, which are also qualitatively and quantitatively similar to triggered sequences. We suggest that removing cardiac triggering for cervical SC diffusion can be a reasonable option to save time with minimal sacrifice to image quality.
Influence of preprocessing, distortion correction and cardiac triggering on the quality of diffusion MR images of spinal cord
Kurt G Schilling
Anna J. E. Combes
Karthik Ramadass
Francois Rheault
Grace Sweeney
Logan Prock
Subramaniam Sriram
John C Gore
Bennett A Landman
Seth A. Smith
Kristin P. O’Grady
Diffusion MRI of the spinal cord (SC) is susceptible to geometric distortion caused by field inhomogeneities, and prone to misalignment acro… (see more)ss time series and signal dropout caused by biological motion. Several modifications of image acquisition and image processing techniques have been introduced to overcome these artifacts, but their specific benefits are largely unproven and warrant further investigations. We aim to evaluate two specific aspects of image acquisition and processing that address image quality in diffusion studies of the spinal cord: susceptibility corrections to reduce geometric distortions, and cardiac triggering to minimize motion artifacts. First, we evaluate 4 distortion preprocessing strategies on 7 datasets of the cervical and lumbar SC and find that while distortion correction techniques increase geometric similarity to structural images, they are largely driven by the high-contrast cerebrospinal fluid, and do not consistently improve the geometry within the cord nor improve white-to-gray matter contrast. We recommend at a minimum to perform bulk-motion correction in preprocessing and posit that improvements/adaptations are needed for spinal cord distortion preprocessing algorithms, which are currently optimized and designed for brain imaging. Second, we design experiments to evaluate the impact of removing cardiac triggering. We show that when triggering is foregone, images are qualitatively similar to triggered sequences, do not have increased prevalence of artifacts, and result in similar diffusion tensor indices with similar reproducibility to triggered acquisitions. When triggering is removed, much shorter acquisitions are possible, which are also qualitatively and quantitatively similar to triggered sequences. We suggest that removing cardiac triggering for cervical SC diffusion can be a reasonable option to save time with minimal sacrifice to image quality.
Pontomedullary junction as a reference for spinal cord cross-sectional area: validation across neck positions
Sandrine Bédard
Maxime Bouthillier
An 8‐channel Tx dipole and 20‐channel Rx loop coil array for MRI of the cervical spinal cord at 7 Tesla
Nibardo Lopez‐Rios
Kyle M. Gilbert
Daniel Papp
Gaspard Cereza
Alexandru Foias
Deshpande Rangaprakash
Markus W. May
Bastien Guerin
Lawrence L. Wald
Boris Keil
Jason P. Stockmann
Robert L. Barry
The quality of cervical spinal cord images can be improved by the use of tailored radiofrequency (RF) coil solutions for ultrahigh field ima… (see more)ging; however, very few commercial and research 7‐T RF coils currently exist for the spinal cord, and in particular, those with parallel transmission (pTx) capabilities. This work presents the design, testing, and validation of a pTx/Rx coil for the human neck and cervical/upper thoracic spinal cord. The pTx portion is composed of eight dipoles to ensure high homogeneity over this large region of the spinal cord. The Rx portion is made up of twenty semiadaptable overlapping loops to produce high signal‐to‐noise ratio (SNR) across the patient population. The coil housing is designed to facilitate patient positioning and comfort, while also being tight fitting to ensure high sensitivity. We demonstrate RF shimming capabilities to optimize B1+ uniformity, power efficiency, and/or specific absorption rate efficiency. B1+ homogeneity, SNR, and g‐factor were evaluated in adult volunteers and demonstrated excellent performance from the occipital lobe down to the T4‐T5 level. We compared the proposed coil with two state‐of‐the‐art head and head/neck coils, confirming its superiority in the cervical and upper thoracic regions of the spinal cord. This coil solution therefore provides a convincing platform for producing the high image quality necessary for clinical and research scanning of the upper spinal cord.
Dynamic shimming in the cervical spinal cord for multi-echo gradient-echo imaging at 3 T
Eva Alonso‐Ortiz
Daniel Papp
A. D'Astous
8-channel Tx dipole and 20-channel Rx loop coil array for MRI of the cervical spinal cord at 7 Tesla
Nibardo Lopez Rios
Kyle M. Gilbert
Daniel Papp
Gaspard Cereza
Alexandru Foias
D. Rangaprakash
Markus W. May
Bastien Guerin
Lawrence L. Wald
Boris Keil
Jason P. Stockmann
Robert L. Barry