Julien Cohen-Adad

Thomas Dagonneau

Master's Research - Polytechnique Montréal

Naga Karthik Enamundram

PhD - Polytechnique Montréal

Co-supervisor :

Sarath Chandar

emvnagakarthik@gmail.com

PhD - Polytechnique Montréal

Nathan Molinier

PhD - Polytechnique Montréal

Kalum Ost

Collaborating researcher

Simon Queric

Research Intern - Polytechnique Montréal

Abel Salmona Salmona

Research Intern - Polytechnique Montréal

abel.salmona@gmail.com

Julien Thouveny

Master's Research - Université de Montréal

Arthur Toulouse

Master's Research - Polytechnique Montréal

Jan Valosek

Postdoctorate - Polytechnique Montréal

Website

Google Scholar

Publications

SCIseg: Automatic Segmentation of Intramedullary Lesions in Spinal Cord Injury on T2-weighted MRI Scans.

Enamundram Naga Karthik

Jan Valošek

Andrew C. Smith

Dario Pfyffer

Simon Schading-Sassenhausen

Lynn Farner

KA Weber

Kenneth A. Weber

Patrick Freund

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This ar… (see more)ticle will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To develop a deep learning tool for the automatic segmentation of the spinal cord and intramedullary lesions in spinal cord injury (SCI) on T2-weighted MRI scans. Materials and Methods This retrospective study included MRI data acquired between July 2002 and February 2023 from 191 patients with SCI (mean age, 48.1 years ± 17.9 [SD]; 142 males). The data consisted of T2-weighted MRI acquired using different scanner manufacturers with various image resolutions (isotropic and anisotropic) and orientations (axial and sagittal). Patients had different lesion etiologies (traumatic, ischemic, and hemorrhagic) and lesion locations across the cervical, thoracic and lumbar spine. A deep learning model, SCIseg, was trained in a three-phase process involving active learning for the automatic segmentation of intramedullary SCI lesions and the spinal cord. The segmentations from the proposed model were visually and quantitatively compared with those from three other open-source methods (PropSeg, DeepSeg and contrast-agnostic, all part of the Spinal Cord Toolbox). Wilcoxon signed-rank test was used to compare quantitative MRI biomarkers of SCI (lesion volume, lesion length, and maximal axial damage ratio) derived from the manual reference standard lesion masks and biomarkers obtained automatically with SCIseg segmentations. Results SCIseg achieved a Dice score of 0.92 ± 0.07 (mean ± SD) and 0.61 ± 0.27 for spinal cord and SCI lesion segmentation, respectively. There was no evidence of a difference between lesion length (P = .42) and maximal axial damage ratio (P = .16) computed from manually annotated lesions and the lesion segmentations obtained using SCIseg. Conclusion SCIseg accurately segmented intramedullary lesions on a diverse dataset of T2-weighted MRI scans and extracted relevant lesion biomarkers (namely, lesion volume, lesion length, and maximal axial damage ratio). SCIseg is open-source and accessible through the Spinal Cord Toolbox (v6.2 and above). Published under a CC BY 4.0 license.

2024-11-06

Radiology: Artificial Intelligence (published)

Spinal cord evaluation in multiple sclerosis: clinical and radiological associations, present and future

B Mark Keegan

Martina Absinta

Eoin P Flanagan

Roland G Henry

Eric C Klawiter

Shannon Kolind

Stephen Krieger

Cornelia Laule

John A Lincoln

Steven Messina

Jiwon Oh

Nico Papinutto

Seth Aaron Smith

Anthony Traboulsee

2024-11-06

Brain Communications (published)

Considerations and recommendations from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 3 -- Ex vivo imaging: data processing, comparisons with microscopy, and tractography

Kurt G Schilling

Amy F. D. Howard

Francesco Grussu

Andrada Ianus

Brian Hansen

Rachel L. C. Barrett

Manisha Aggarwal

Stijn Michielse

Fatima Nasrallah

W. Syeda

Nian Wang

Jelle Veraart

Alard J. Roebroeck

Andrew F Bagdasarian

Cornelius Eichner

Farshid Sepehrband

Jan Zimmermann

L. Soustelle

Christien Bowman

Benjamin C. Tendler … (see 38 more)

A. Hertanu

Ben Jeurissen

Marleen Verhoye

Lucio Frydman

Y. Looij

David C. Hike

Jeff F. Dunn

Karla L. Miller

Bennett A. Landman

N. Shemesh

Adam Anderson

Emilie McKinnon

Shawna Farquharson

Flavio Dell’ Acqua

Carlo M. Pierpaoli

Ivana Drobnjak

Alexander Leemans

K. Harkins

Maxime Descoteaux

Duan Xu

Hao Huang

Mathieu D. Santin

Samuel C. Grant

Andre Obenaus

Gene S Kim

Dan Wu

D. Bihan

S. Blackband

Luisa Ciobanu

E. Fieremans

Ruiliang Bai

T. Leergaard

Jiangyang Zhang

T. Dyrby

G. A. Johnson

Matthew D. Budde

Ileana Ozana Jelescu

2024-10-24

ArXiv (preprint)

arxiv.org

Spiral volumetric optoacoustic tomography of reduced oxygen saturation in the spinal cord of M83 mouse model of Parkinson's disease.

Benjamin F. Combes

Sandeep Kumar Kalva

Pierre-Louis Benveniste

Agathe Tournant

Man Hoi Law

Joshua Newton

Maik Krüger

Rebecca Z Weber

Inês Dias

Daniela Noain

Xose Luis Dean-Ben

Uwe Konietzko

Christian R. Baumann

Per-Göran Gillberg

Christoph Hock

Roger M. Nitsch

Daniel Razansky

Ruiqing Ni

2024-10-09

European Journal of Nuclear Medicine and Molecular Imaging (published)

<scp>RF</scp> shimming in the cervical spinal cord at <scp>7 T</scp>

Daniel Papp

Kyle M. Gilbert

Gaspard Cereza

Alexandre D'Astous

Nibardo Lopez‐Rios

Mathieu Boudreau

Marcus J. Couch

Pedram Yazdanbakhsh

Robert L. Barry

Eva Alonso‐Ortiz

2024-08-13

Magnetic Resonance in Medicine (published)

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

A. Lebret

Gergely David

Andrew Smith

Kenneth A. Weber

Maryam Seif

Rhscir Network Imaging Group

Patrick Freund

2024-07-24

ArXiv (preprint)

arxiv.org

Myelin basic protein mRNA levels affect myelin sheath dimensions, architecture, plasticity, and density of resident glial cells

Hooman Bagheri

Hana Friedman

Amanda Hadwen

Celia Jarweh

Ellis Cooper

Lawrence Oprea

Claire Guerrier

Anmar Khadra

Armand Collin

Amanda Young

Gerardo Mendez Victoriano

Matthew Swire

Andrew Jarjour

Marie E. Bechler

Rachel S. Pryce

Pierre Chaurand

Lise Cougnaud

Dajana Vuckovic

Elliott Wilion … (see 11 more)

Owen Greene

Akiko Nishiyama

Anouk Benmamar‐Badel

Trevor Owens

Vladimir Grouza

Marius Tuznik

Hanwen Liu

David A. Rudko

Jinyi Zhang

Katherine A. Siminovitch

Alan C. Peterson

2024-07-18

Glia (published)

Automatic Segmentation of the Spinal Cord Nerve Rootlets

Jan Valošek

Theo Mathieu

Raphaëlle Schlienger

Olivia S. Kowalczyk

Precise identification of spinal nerve rootlets is relevant to delineate spinal levels for the study of functional activity in the spinal co… (see more)rd. The goal of this study was to develop an automatic method for the semantic segmentation of spinal nerve rootlets from T2-weighted magnetic resonance imaging (MRI) scans. Images from two open-access MRI datasets were used to train a 3D multi-class convolutional neural network using an active learning approach to segment C2-C8 dorsal nerve rootlets. Each output class corresponds to a spinal level. The method was tested on 3T T2-weighted images from datasets unseen during training to assess inter-site, inter-session, and inter-resolution variability. The test Dice score was 0.67 +- 0.16 (mean +- standard deviation across rootlets levels), suggesting a good performance. The method also demonstrated low inter-vendor and inter-site variability (coefficient of variation= 1.41 %), as well as low inter-session variability (coefficient of variation= 1.30 %) indicating stable predictions across different MRI

2024-07-02

Imaging Neuroscience (published)

arxiv.org

Calibration‐free parallel transmission of the cervical, thoracic, and lumbar spinal cord at <scp>7T</scp>

Christoph S. Aigner

Manuel F. Sánchez Alarcon

Alexandre D'Astous

Eva Alonso‐Ortiz

Sebastian Schmitter

2024-05-10

Magnetic Resonance in Medicine (published)

Repeat it without me: Crowdsourcing the T1 mapping common ground via the ISMRM reproducibility challenge.

Mathieu Boudreau

Agah Karakuzu

Ecem Bozkurt

Madeline Carr

Marco Castellaro

Luis Concha

Mariya Doneva

Seraina A. Dual

Alex Ensworth

Alexandru Foias

Véronique Fortier

Refaat E. Gabr

Guillaume Gilbert

Carri K. Glide‐Hurst

Matthew Grech‐Sollars

Siyuan Hu

Oscar Jalnefjord

Jorge Jovicich

Kübra Keskin … (see 22 more)

Peter Koken

Anastasia Kolokotronis

Simran Kukran

Nam G. Lee

Ives R. Levesque

Bochao Li

Dan Ma

Burkhard Mädler

Nyasha G. Maforo

Jamie Near

Erick Pasaye

Alonso Ramirez‐Manzanares

Ben Statton

Christian Stehning

Stefano Tambalo

Ye Tian

Chenyang Wang

Kilian Weiss

Niloufar Zakariaei

Shuo Zhang

Ziwei Zhao

Nikola Stikov

PURPOSE T1 mapping is a widely used quantitative MRI technique, but its tissue-specific values remain inconsistent across protocols, sites, … (see more)and vendors. The ISMRM Reproducible Research and Quantitative MR study groups jointly launched a challenge to assess the reproducibility of a well-established inversion-recovery T1 mapping technique, using acquisition details from a seminal T1 mapping paper on a standardized phantom and in human brains. METHODS The challenge used the acquisition protocol from Barral et al. (2010). Researchers collected T1 mapping data on the ISMRM/NIST phantom and/or in human brains. Data submission, pipeline development, and analysis were conducted using open-source platforms. Intersubmission and intrasubmission comparisons were performed. RESULTS Eighteen submissions (39 phantom and 56 human datasets) on scanners by three MRI vendors were collected at 3 T (except one, at 0.35 T). The mean coefficient of variation was 6.1% for intersubmission phantom measurements, and 2.9% for intrasubmission measurements. For humans, the intersubmission/intrasubmission coefficient of variation was 5.9/3.2% in the genu and 16/6.9% in the cortex. An interactive dashboard for data visualization was also developed: https://rrsg2020.dashboards.neurolibre.org. CONCLUSION The T1 intersubmission variability was twice as high as the intrasubmission variability in both phantoms and human brains, indicating that the acquisition details in the original paper were insufficient to reproduce a quantitative MRI protocol. This study reports the inherent uncertainty in T1 measures across independent research groups, bringing us one step closer to a practical clinical baseline of T1 variations in vivo.

2024-05-10

Magnetic Resonance in Medicine (published)

Quantifying neurodegeneration of the cervical cord and brain in degenerative cervical myelopathy: A multicentre study using quantitative <scp>magnetic resonance imaging</scp>

Patrick Freund

Viveka Boller

Tim M. Emmenegger

Muhammad Akbar

Markus Hupp

Nikolai Pfender

Claudia A. M. Gandini Wheeler-Kingshott

Michael G. Fehlings

Armin Curt

Maryam Seif

2024-05-07

European Journal of Neurology (published)

Body size interacts with the structure of the central nervous system: A multi-center in vivo neuroimaging study

René Labounek

Monica T. Bondy

Amy L. Paulson

Sandrine Bédard

Mihael Abramovic

Eva Alonso‐Ortiz

Nicole Atcheson

Laura R. Barlow

Robert L. Barry

Markus Barth

Marco Battiston

Christian Büchel

Matthew D. Budde

Virginie Callot

Anna Combes

Benjamin De Leener

Maxime Descoteaux

Paulo Loureiro de Sousa

Marek Dostál

Julien Doyon … (see 74 more)

Adam Dvorak

Falk Eippert

Karla R. Epperson

Kevin S. Epperson

Patrick Freund

Jürgen Finsterbusch

Alexandru Foias

Michela Fratini

Issei Fukunaga

Claudia A. M. Gandini Wheeler-Kingshott

Giancarlo Germani

Guillaume Gilbert

Federico Giove

Francesco Grussu

Akifumi Hagiwara

Pierre-Gilles Henry

Tomáš Horák

Masaaki Hori

James M. Joers

Kouhei Kamiya

Haleh Karbasforoushan

Miloš Keřkovský

Ali Khatibi

Joo‐Won Kim

Nawal Kinany

Hagen H. Kitzler

Shannon Kolind

Yazhuo Kong

Petr Kudlička

Paul Kuntke

Nyoman D. Kurniawan

Slawomir Kusmia

Maria Marcella Lagana

Cornelia Laule

Christine S. W. Law

Csw Law

Tobias Leutritz

Yaou Liu

Sara Llufriu

Sean Mackey

Allan R. Martin

Eloy Martinez-Heras

Loan Mattera

Kristin P. O’Grady

Nico Papinutto

Daniel Papp

Deborah Pareto

Todd B. Parrish

Anna Pichiecchio

Ferran Prados

Àlex Rovira

Marc J. Ruitenberg

Rebecca S. Samson

Giovanni Savini

Maryam Seif

Alan C. Seifert

Alex K. Smith

Seth A. Smith

Zachary A. Smith

Elisabeth Solana

Yuichi Suzuki

George Tackley

Alexandra Tinnermann

Jan Valošek

Dimitri Van De Ville

Marios C. Yiannakas

Kenneth A. Weber

Nikolaus Weiskopf

Richard G. Wise

Patrik O. Wyss

Junqian Xu

Christophe Lenglet

Igor Nestrašil

Clinical research emphasizes the implementation of rigorous and reproducible study designs that rely on between-group matching or controllin… (see more)g for sources of biological variation such as subject’s sex and age. However, corrections for body size (i.e. height and weight) are mostly lacking in clinical neuroimaging designs. This study investigates the importance of body size parameters in their relationship with spinal cord (SC) and brain magnetic resonance imaging (MRI) metrics. Data were derived from a cosmopolitan population of 267 healthy human adults (age 30.1±6.6 years old, 125 females). We show that body height correlated strongly or moderately with brain gray matter (GM) volume, cortical GM volume, total cerebellar volume, brainstem volume, and cross-sectional area (CSA) of cervical SC white matter (CSA-WM; 0.44≤r≤0.62). In comparison, age correlated weakly with cortical GM volume, precentral GM volume, and cortical thickness (-0.21≥r≥-0.27). Body weight correlated weakly with magnetization transfer ratio in the SC WM, dorsal columns, and lateral corticospinal tracts (-0.20≥r≥-0.23). Body weight further correlated weakly with the mean diffusivity derived from diffusion tensor imaging (DTI) in SC WM (r=-0.20) and dorsal columns (-0.21), but only in males. CSA-WM correlated strongly or moderately with brain volumes (0.39≤r≤0.64), and weakly with precentral gyrus thickness and DTI-based fractional anisotropy in SC dorsal columns and SC lateral corticospinal tracts (-0.22≥r≥-0.25). Linear mixture of sex and age explained 26±10% of data variance in brain volumetry and SC CSA. The amount of explained variance increased at 33±11% when body height was added into the mixture model. Age itself explained only 2±2% of such variance. In conclusion, body size is a significant biological variable. Along with sex and age, body size should therefore be included as a mandatory variable in the design of clinical neuroimaging studies examining SC and brain structure.

2024-05-01

bioRxiv (preprint)