Julien Cohen-Adad

Kalum Ost

Maxime Bouthillier

Gergely Dávid

Maryam Seif

Armin Curt

Nikolai Pfender

Markus Hupp

Patrick Freund

Tomáš Horák

Petr Kudlička

Josef Bednařík

Fauziyya Muhammad

Zachary A. Smith

2026-01-31

European Journal of Radiology Artificial Intelligence (published)

Monitoring morphometric drift in lifelong learning segmentation of the spinal cord.

Enamundram Naga Karthik

Sandrine Bédard

Christoph Stefan Aigner

Elise Bannier

Josef Bednařík

Virginie Callot

Anna Combes

Armin Curt

Gergely Dávid

Falk Eippert

Lynn Farner

Michael G. Fehlings

Patrick Freund

Tobias Granberg

Cristina Granziera

RHSCIR Network Imaging Group

Ulrike Horn

Tomáš Horák

Suzanne Humphreys … (see 36 more)

Markus Hupp

Anne Kerbrat

Nawal Kinany

Shannon Kolind

Petr Kudlička

Anna Lebret

Lisa Eunyoung Lee

Cristina Granziera

Allan R. Martin

Govind Nair

Megan McGrath

Kristin P. O’Grady

Jiwon Oh

Russell Ouellette

Nikolai Pfender

Dario Pfyffer

Pierre‐François Pradat

Alexandre Prat

Daniel S. Reich

Ilaria Ricchi

Naama Rotem‐Kohavi

Simon Schading-Sassenhausen

Maryam Seif

Andrew Smith

Seth A. Smith

Grace Sweeney

Roger Tam

Anthony Traboulsee

Constantina A. Treaba

Charidimos Tsagkas

Dimitri Van De Ville

Zachary Vavasour

Kenneth A. Weber

Sarath Chandar

Morphometric measures derived from spinal cord segmentations can serve as diagnostic and prognostic biomarkers in neurological diseases and … (see more)injuries affecting the spinal cord. For instance, the spinal cord cross-sectional area can be used to monitor cord atrophy in multiple sclerosis and to characterize compression in degenerative cervical myelopathy. While robust, automatic segmentation methods to a wide variety of contrasts and pathologies have been developed over the past few years, whether their predictions are stable as the model is updated using new datasets has not been assessed. This is particularly important for deriving normative values from healthy participants. In this study, we present a spinal cord segmentation model trained on a multisite (n=75) dataset, including 9 different MRI contrasts and several spinal cord pathologies. We also introduce a lifelong learning framework to automatically monitor the morphometric drift as the model is updated using additional datasets. The framework is triggered by an automatic GitHub Actions workflow every time a new model is created, recording the morphometric values derived from the model's predictions over time. As a real-world application of the proposed framework, we employed the spinal cord segmentation model to update a recently-introduced normative database of healthy participants containing commonly used measures of spinal cord morphometry. Results showed that: (i) our model performs well compared to its previous versions and existing pathology-specific models on the lumbar spinal cord, images with severe compression, and in the presence of intramedullary lesions and/or atrophy achieving an average Dice score of 0.95 ± 0.03; (ii) the automatic workflow for monitoring morphometric drift provides a quick feedback loop for developing future segmentation models; and (iii) the scaling factor required to update the database of morphometric measures is nearly constant among slices across the given vertebral levels, showing minimum drift between the current and previous versions of the model monitored by the framework. The model is freely available in Spinal Cord Toolbox v7.0.

2026-01-21

Imaging neuroscience (published)

openreview.net

Diffusion tractography outside the brain: the road less travelled

Kurt G. Schilling

Irvin Teh

Richard Dortch

Ibrahim Ibrahim

Nian Wang

Bruce Damon

Rory L. Cochran

Alexander Leemans

Diffusion tractography is a powerful MRI technique for mapping fibrous tissue architecture, traditionally applied to the white matter of the… (see more) brain. This report surveys the growing application of tractography to anatomical structures outside the brain, a domain that presents both unique challenges and unique opportunities. We examine its use in the heart, spinal cord, peripheral nerves, brachial plexus, kidney, skeletal muscle, and prostate. For each region, we detail the necessary methodological adaptations for acquisition, modeling, and processing, and highlight the unique anatomical information that can be derived for research and clinical applications. While significant challenges remain - spanning technical hurdles like physiological motion and susceptibility artifacts, to biological complexities like lower anisotropy and the interpretation of streamline validity - tractography beyond the brain provides invaluable, non-invasive insights into tissue micro-organization, opening a new frontier for biomedical imaging.

2025-12-31

Brain Structure and Function (published)

Canadian Spine Society

Adeesya Gausper

Lindsay M Andras

Ken D. Illingworth

David L. Skaggs

Rachelle Imbeault

Justin Dufresne

Sylvain Deschênes

Marjolaine Roy-Beaudry

Jack Legler

Lee Benaroch

Olivia Serhan

Draydon Cheng

Debra Bartley

Patrick Thornley

Khaled Skaik

Genevieve Belanger

Alexandra Stratton

Coyle Matthew

Stephen P. Kingwell

Eve C. Tsai … (see 355 more)

Eugene K. Wai

Hannah Fonteyne

S. Hryniuk

Eric Parent

Eric Huang

K. Stampe

Marie J Beaulieu

Monica Chan

Gloria Thevasagayam

Gabriela Marino-Merlo

Zaid Salaheen

A. Malvea

Leeor Yefet

Ali Moghaddamjou

Sam Molot-Toker

Eisha Christian

Jennifer L Quon

P. B. Dirks

James M. Drake

James T. Rutka

Abhaya V. Kulkarni

Reinhard Zeller

George M. Ibrahim

Julie Joncas

Soraya Barchi

Stefan Parent

Karim Aboelmagd

Archana Sivakuganandan

Amna Zulfiqar

Anne Murphy

Stanley Moll

Julia Sorbara

Brett Rocos

Mark Camp

Geoffrey K. Shumilak

Jalen Dansby

Andrew Chan-Tai-Kong

David E. Lebel

Daisy A Lu

Monique Clar

M. F. Al-Shakfa

Parham Rasoulinejad

Firoz Miyanji

Karim Kantar

Timothy P. Carey

Ravi Ghag

Brent Lanting

Zeeshan Sardar

Saumayajit Basu

Manish Gupta

Abdullah T. Eissa

So Kato

Lawrence G. Lenke

Kristen Jones

Saumyajit Basu

Michael P Kelly

Justin Smith

S. Strantzas

Yong Qiu

Ferran Pellise

A. Alanay

Nasir A. Quraishi

R. Gray

G. Yoshida

Amer Aziz

Jennyfer Paulla Galdino Chaves

Brian Hsu

Stone Sima

Bhisham Singh

Vinay Kulkarni

Ashish D. Diwan

Taryn Ludwig

Farbod Moghaddam

Mina Aminghafari

May Choi

Eliana Seider

Lauren Daunt

Vanessa Vashishth

Ali Ahmadi

P. Brzozowski

Asra Toobaie

Renan R. Fernandes

Anthony V. Perruccio

Amir Mishreky

Mark Alexander MacLean

Lisa Julien

Glenn Patriquin

Jason Leblanc

Ryan Greene

J. Alant

Sean Barry

R. Glennie

Sean D. Christie

Greg MacIntosh

Daniel P Smith

Erin Bigney

Jeffrey Hebert

Eden Richardson

Neil Manson

Edward Abraham

Abdullah Zein

Kyra Holt

Hannah Isaac

Jillian Kearney

Chris Small

Abdullah A.S.M. AlDuwaisan

V. Smith

Tara Whittaker

Denise Eckenswiller

Elias Soumbasis

Robert Tanguay

Celina Nahanni

Tiffany Lung

James J Young

Chloe N Cadieux

Jin Tong Du

Raja Rampersaud

Andrew Glennie

Cynthia Dunning

Emma Jones

William Oxner

Kaike Lobo

P. Łajczak

Cláudia Santos

Numa Rajab

Rafael Oliveira

Y. Silva

R. Barbosa

Aazad Abbas

Gurjovan Sahi

Michael B Johnson

Edward Buchel

Jay Toor

Ronit Kulkarni

Melanie Bertolino

Chase Walton

Gabriella Rivas

John Glaser

Charles Reitman

James Lawrence

Robert Ravinsky

Mohammed Ali Alvi

Avery B. Nathens

Eva Yuan

Yingshi He

Francois Mathieu

Michael C. Sklar

Samuel Yoon

Luke Reda

Hussain Shakil

S. Sadiqi

S. Muijs

Charlotte Dandurand

Marcel Dvorak

F. C. Oner

Vivian Huong Hoang Thien Le

Pascal Mputu Mputu

Francis Bernard

Yiorgos Alexandros Cavayas

H. Hong

D. Kurban

Tianyu Yang

Nader Fallah

Christiana L Cheng

Suzanne Humphreys

Vanessa K. Noonan

Dana El-mughayyar

Colleen O’Connell

Husain Shakil

Zixuan Hu

Christopher W. Smith

Hui M Ling

Zakariya M Khan

Ervin Sejdic

Errol Colak

Christopher Witiw

Maude Duguay

Juan David Cifuentes Hernandez

Jean-Marc Mac-Thiong

Antoine Dionne

Natan Bensoussan

Andréane Richard-Denis

Louis Carrier

Jocelyn Blanchard

Bernard LaRue

Ariane Paquette

Yan Gabriel Morais David Silva

Christopher Nielsen

Vaidehi Bhatt

Stephen J. Lewis

Y. Raja Rampersaud

Brent Rosenstein

Chanelle Montpetit

Nicolas Vaillancourt

Geoffrey Dover

Christina Weiss

Lee Ann Papula

Antonys Melek

Maryse Fortin

A. Fazlollahi

Uri Barak

Samira Kalhor

Joshua Hien Nguyen

Carlo Santaguida

Ruheksh Raj

Kyle Rogan

Allison Marchuk

Erin Barrett

Anand Masson

Brandy Pratt

Danielle Michaud

Kateryna Skyrda

Sierra Simms

Brandon Herrington

Fawaz Siddiqi

Kevin Gurr

Mathieu Chayer

P-J Arnoux

Jeremy Rawlinson

Olumide Aruwajoye

Carl-Eric Aubin

Hussein Samhat

K. Pedro

A. A. Pirshahid

Genevieve Gore

K. Filion

Oliver Lasry

Jordan J. Levett

Nathan Evanview

G. Mcintosh

Nadav Rinott

Mathieu Laflamme

Andy Liu

Alexander Tuchman

Christopher Mikhail

Vivien K. Chan

James McDonald

Julien Zaldivar

G. Lonjon

Matthieu Vassal

Alexandre Dhenin

Alexis Perez

Martin Dupuy

V. Challier

J. Castelain

S. Ghailane

Matthieu Campana

Jonathan Lebhar

Gilles Guerin

Nicolas Pellet

Yann Sabah

T. Chevillotte

A. Darnis

Joseph Cristini

F.X. Ferracci

J. Delambre

Steffen Queinnec

Alexandre Delmotte

Paulo Marinho

R. Gauthé

P. Hannequin

Vianney Gilard

Jean Meyblum

Alexis Perrin

Raphaël Pietton

Nicolas Lonjon

Antoine Gennari

Ahmed Essa

Michael Craig

Kalum Ost

W. Bradley Jacobs

Peter Lewkonia

Fred Nicholls

Michael Yang

Julien-Cohen Adad

Isaac Wangai

Andrew Nataraj

Osman Hojanepesov

Matthew Skarsgard

Nathan Evaniew

Jérôme Paquet

Perry Dhaliwal

Najmedden Attabib

Chris Bailey

Jefferson R. Wilson

Daniel Kurtz

P. Phan

Christopher Sun

Newton Cho

Abdul Al-Shawwa

Bradley W. Jacobs

Jacques Bouchard

Steven Casha

Stephan duPlessis

Alex Soroceanu

Ganesh Swamy

Kenneth C. Thomas

David W. Cadotte

Landon J Hansen

Stephen Yip

Nicolas Dea

Chetan Bettegowda

Laurence D. Rhines

Daniel M. Sciubba

James M. Schuster

Stefano Boriani

M. Clarke

Paul Arnold

Anne Versteeg

Michael H. Weber

R. de la Garza Ramos

John Shin

Markian Pahuta

A. Luzatti

A. Disch

A. Gasbarrini

Jorrit‐jan Verlaan

William G J Teixeira

Ilya Laufer

Á. Lazáry

Dean Chou

Z. Gokaslan

Addisu Mesfin

Tony Goldschlager

C. Netzer

J. O'Toole

Ori Barzilai

Norio Kawahara

Naresh Kumar

Jeremy J. Reynolds

Rory Goodwin

Jetan Badhiwala

A. Sahgal

Michael G. Fehlings

Alex Kiss

Donald A. Redelmeier

William Chu Kwan

Nikolaus Koegl

Charles G. Fisher

Raphaële Charest-Morin

Alexandra Rocha

Matthew Renaud

Jennifer C. Urquhart

Supriya Singh

Marco Pérez Caceres

Omer Ahmed

Véronique Freire

Jesse Shen

Fidaa Al-shakfa

Danielle Boule

Z. Wang

Christopher S. Lozano

Armaan Malhotra

Vishwathsen Karthikeyan

2025-12-11

Canadian journal of surgery. Journal canadien de chirurgie (published)

Building a library of acute traumatic spinal cord injury images across Canada: a retrospective cohort study protocol

Naama Rotem-Kohavi

Suzanne Humphreys

Vanessa K Noonan

Christiana L Cheng

Mathieu Guay-Paquet

Maxime Bouthillier

Enamundram Naga Karthik

Emma Lichtenstein

Nick Guenther

Kalum Ost

Naj Attabib

Michael Hardisty

Jetan Badhiwala

Jeremie Larouche

Markian Pahuta

Sean Christie

Michael G Fehlings

Daryl Fourney

Brian K Kwon … (see 6 more)

Jean Marc Mac-Thiong

Jérôme Paquet

Philippe Phan

Christopher Witiw

David W Cadotte

MRI is increasingly recognised as a valuable tool for assessing prognosis and predicting outcomes following traumatic spinal cord injury (SC… (see more)I). Several potential MRI biomarkers have been identified, but efforts are still needed to improve the accuracy and feasibility of these biomarkers in clinical practice. This study aims to build a national Canadian SCI imaging repository for storing and analysing imaging data for SCI, with the goal of improving SCI MRI biomarkers to predict outcomes and inform clinical management. As a substudy of the Rick Hansen SCI Registry (RHSCIR), this retrospective multisite study includes individuals who sustained a traumatic cervical SCI between 2015 and 2021, were previously enrolled in RHSCIR, and had MRI scans acquired within 72 hours of injury and before any surgical intervention. Individuals with a penetrating trauma and/or with any prior spine surgery are excluded. The study principal investigator and research associates, experienced with data curation and with the standardised format and specifications of the Brain Imaging Data Structure standard, guide the site’s curator on the steps to perform image deidentification and curation to create standardised datasets across all sites. These datasets are transferred to a Digital Research Alliance of Canada (‘the Alliance’) server designated for this project and concatenated to form the national Canadian SCI imaging repository (Neurogitea). We are using a semiautomated processing pipeline to quantify lesion morphology, together with additional imaging measures that are manually extracted from the images (for instance, the relative maximal spinal cord compression and the maximum canal compromise). Through linkage to RHSCIR clinical and epidemiological data already available on eligible participants, regression analysis is planned to predict neurological outcomes at discharge, including the American Spinal Injury Association Impairment Scale grade, upper and lower extremity motor and sensory scores. This protocol has been submitted by the participating sites to obtain ethics and institutional approvals prior to the study initiation at each site. All 12 sites across Canada have now obtained ethics and institutional approvals. Study results will be disseminated at local, national and international conferences and by journal publications.

2025-11-30

BMJ Open (published)

RootletSeg: Deep learning method for spinal rootlets segmentation across MRI contrasts

Katerina Krejci

Jiri Chmelik

Sandrine B'edard

Falk Eippert

Ulrike Horn

Virginie Callot

Purpose: To develop a deep learning method for the automatic segmentation of spinal nerve rootlets on various MRI scans. Material and Method… (see more)s: This retrospective study included MRI scans from two open-access and one private dataset, consisting of 3D isotropic 3T TSE T2-weighted (T2w) and 7T MP2RAGE (T1-weighted [T1w] INV1 and INV2, and UNIT1) MRI scans. A deep learning model, RootletSeg, was developed to segment C2-T1 dorsal and ventral spinal rootlets. Training was performed on 76 scans and testing on 17 scans. The Dice score was used to compare the model performance with an existing open-source method. Spinal levels derived from RootletSeg segmentations were compared with vertebral levels defined by intervertebral discs using Bland-Altman analysis. Results: The RootletSeg model developed on 93 MRI scans from 50 healthy adults (mean age, 28.70 years

2025-09-17

ArXiv (preprint)

arxiv.org

RootletSeg: Deep learning method for spinal rootlets segmentation across MRI contrasts

Katerina Krejci

Jiri Chmelik

Sandrine B'edard

Falk Eippert

Ulrike Horn

Virginie Callot

Purpose: To develop a deep learning method for the automatic segmentation of spinal nerve rootlets on various MRI scans. Material and Method… (see more)s: This retrospective study included MRI scans from two open-access and one private dataset, consisting of 3D isotropic 3T TSE T2-weighted (T2w) and 7T MP2RAGE (T1-weighted [T1w] INV1 and INV2, and UNIT1) MRI scans. A deep learning model, RootletSeg, was developed to segment C2-T1 dorsal and ventral spinal rootlets. Training was performed on 76 scans and testing on 17 scans. The Dice score was used to compare the model performance with an existing open-source method. Spinal levels derived from RootletSeg segmentations were compared with vertebral levels defined by intervertebral discs using Bland-Altman analysis. Results: The RootletSeg model developed on 93 MRI scans from 50 healthy adults (mean age, 28.70 years

2025-09-17

ArXiv (preprint)

arxiv.org

EPISeg: Automated segmentation of the spinal cord on echo planar images using open-access multi-center data

Rohan Banerjee

Merve Kaptan

Alexandra Tinnermann

Ali Khatibi

Alice Dabbagh

Christian Büchel

Christian W. Kündig

Christine S. W. Law

Csw Law

Dario Pfyffer

David J. Lythgoe

Dimitra Tsivaka

Dimitri Van De Ville

Falk Eippert

Fauziyya Muhammad

Gary H. Glover

Gergely Dávid

Grace Haynes

Jan Haaker

Jonathan C. W. Brooks … (see 23 more)

Jürgen Finsterbusch

Katherine T. Martucci

Kimberly J. Hemmerling

Mahdi Mobarak-Abadi

Mark A. Hoggarth

Matthew A. Howard

Molly G. Bright

Nawal Kinany

Olivia S. Kowalczyk

Patrick Freund

Robert L. Barry

Sean Mackey

Shahabeddin Vahdat

Simon Schading

Stephen B. McMahon

Todd Parish

Véronique Marchand-Pauvert

Yufen Chen

Zachary A. Smith

KA Weber

Kenneth A. Weber

Benjamin De Leener

Functional magnetic resonance imaging (fMRI) of the spinal cord is relevant for studying sensation, movement, and autonomic function. Prepro… (see more)cessing of spinal cord fMRI data involves segmentation of the spinal cord on gradient-echo echo planar imaging (EPI) images. Current automated segmentation methods do not work well on these data, due to the low spatial resolution, susceptibility artifacts causing distortions and signal drop-out, ghosting, and motion-related artifacts. Consequently, this segmentation task demands a considerable amount of manual effort which takes time and is prone to user bias. In this work, we (i) gathered a multi-center dataset of spinal cord gradient-echo EPI with ground-truth segmentations and shared it on OpenNeuro https://openneuro.org/datasets/ds005143/versions/1.3.0, and (ii) developed a deep learning-based model, EPISeg, for the automatic segmentation of the spinal cord on gradient-echo EPI data. We observe a significant improvement in terms of segmentation quality compared to other available spinal cord segmentation models. Our model is resilient to different acquisition protocols as well as commonly observed artifacts in fMRI data. The training code is available at https://github.com/sct-pipeline/fmri-segmentation/, and the model has been integrated into the Spinal Cord Toolbox as a command-line tool.

2025-09-09

Imaging neuroscience (published)

EPISeg: Automated segmentation of the spinal cord on echo planar images using open-access multi-center data

Rohan Banerjee

Merve Kaptan

Alexandra Tinnermann

Ali Khatibi

Alice Dabbagh

Christian Büchel

Christian W. Kündig

Csw Law

Dario Pfyffer

David J. Lythgoe

Dimitra Tsivaka

Dimitri Van De Ville

Falk Eippert

Fauziyya Muhammad

Gary H. Glover

Gergely Dávid

Grace Haynes

Jan Haaker

Jonathan C. W. Brooks

Jürgen Finsterbusch … (see 21 more)

Katherine T. Martucci

Kimberly J. Hemmerling

Mahdi Mobarak-Abadi

Mark A. Hoggarth

Matthew A. Howard

Molly G. Bright

Nawal Kinany

Olivia S. Kowalczyk

Patrick Freund

Robert L. Barry

Sean Mackey

Shahabeddin Vahdat

Simon Schading

Stephen B. McMahon

Todd Parish

Véronique Marchand-Pauvert

Yufen Chen

Zachary A. Smith

Kenneth A. Weber

Benjamin De Leener

Abstract Functional magnetic resonance imaging (fMRI) of the spinal cord is relevant for studying sensation, movement, and autonomic functio… (see more)n. Preprocessing of spinal cord fMRI data involves segmentation of the spinal cord on gradient-echo echo planar imaging (EPI) images. Current automated segmentation methods do not work well on these data, due to the low spatial resolution, susceptibility artifacts causing distortions and signal drop-out, ghosting, and motion-related artifacts. Consequently, this segmentation task demands a considerable amount of manual effort which takes time and is prone to user bias. In this work, we (i) gathered a multi-center dataset of spinal cord gradient-echo EPI with ground-truth segmentations and shared it on OpenNeuro https://openneuro.org/datasets/ds005143/versions/1.3.1 and (ii) developed a deep learning-based model, EPISeg, for the automatic segmentation of the spinal cord on gradient-echo EPI data. We observe a significant improvement in terms of segmentation quality compared with other available spinal cord segmentation models. Our model is resilient to different acquisition protocols as well as commonly observed artifacts in fMRI data. The training code is available at https://github.com/sct-pipeline/fmri-segmentation/, and the model has been integrated into the Spinal Cord Toolbox as a command-line tool.

2025-09-09

Imaging neuroscience (published)

Rootlets-based registration to the PAM50 spinal cord template

Sandrine Bédard

Valeria Oliva

Kenneth A. Weber

Abstract Spinal cord functional MRI studies require precise localization of spinal levels for reliable voxel-wise group analyses. Traditiona… (see more)l template-based registration of the spinal cord uses intervertebral discs for alignment. However, substantial anatomical variability across individuals exists between vertebral and spinal levels. This study proposes a novel registration approach that leverages spinal nerve rootlets to improve alignment accuracy and reproducibility across individuals. We developed a registration method leveraging dorsal cervical rootlets segmentation and aligning them non-linearly with the PAM50 spinal cord template. Validation was performed on a multi-subject, multi-site dataset (n = 267, 44 sites) and a multi-subject dataset with various neck positions (n = 10, 3 sessions). We further validated the method on task-based functional MRI (n = 23) to compare group-level activation maps using rootlet-based registration to traditional disc-based methods. Rootlet-based registration showed superior alignment across individuals compared with the traditional disc-based method on n = 226 individuals, and on n = 176 individuals for morphological analyses. Notably, rootlet positions were more stable across neck positions. Group-level analysis of task-based functional MRI using rootlet-based registration increased Z scores and activation cluster size compared with disc-based registration (number of active voxels from 3292 to 7978). Rootlet-based registration enhances both inter- and intra-subject anatomical alignment and yields better spatial normalization for group-level fMRI analyses. Our findings highlight the potential of rootlet-based registration to improve the precision and reliability of spinal cord neuroimaging group analysis.

2025-08-26

Imaging Neuroscience (published)

arxiv.org

Cervical Spinal Cord Magnetization Transfer Ratio and Its Relationship With Clinical Outcomes in Multiple Sclerosis

Lisa Eunyoung Lee

Irene M. Vavasour

Melanie Guenette

Katherine Sawicka

Neda Rashidi‐Ranjbar

Nathan Churchill

Akash Chopra

Adelia Adelia

Pierre-Louis Benveniste

Anthony Traboulsee

Nathalie Arbour

Fabrizio Giuliani

Larry D. Lynd

Scott B. Patten

Alexandre Prat

Alice Schabas

Penelope Smyth

Roger Tam

Yunyan Zhang … (see 6 more)

Simon J. Graham

Mojgan Hodaie

Anthony Feinstein

Shannon Kolind

Tom A. Schweizer

Jiwon Oh

ABSTRACT Objective The cervical spinal cord (cSC) is highly relevant to clinical dysfunction in multiple sclerosis (MS) but remains understu… (see more)died using quantitative magnetic resonance imaging (MRI). We assessed magnetization transfer ratio (MTR), a semi‐quantitative MRI measure sensitive to MS‐related tissue microstructural changes, in the cSC and its relationship with clinical outcomes in radiologically isolated syndrome (RIS) and MS. Methods MTR data were acquired from 52 RIS, 201 relapsing–remitting MS (RRMS), 47 primary progressive MS (PPMS), and 43 control (CON) participants across four sites in the Canadian Prospective Cohort Study to Understand Progression in MS (CanProCo) using 3.0 T MRI systems. Mean MTR was compared between groups in whole cSC and sub‐regions between C2‐C4. Multiple linear regression was used to evaluate relationships between MTR and clinical outcomes, including the expanded disability status scale (EDSS), walking speed test (WST), and manual dexterity test (MDT). Results There were consistent group differences in MTR, which were most pronounced between PPMS and CON (−5.8% to −3.7%, p ≤ 0.01). In PPMS, lower MTR was associated with greater disability as measured by EDSS (β = −0.3 to −0.1, p ≤ 0.03), WST (β = −0.9 to −0.5, p ≤ 0.04), and MDT (β = −0.6 and − 0.5, p = 0.04). In RRMS, MTR was associated with only EDSS (β = −0.1, p ≤ 0.03). Interpretation In this large sample of RIS and MS, cSC MTR was lowest in PPMS, with associations between MTR and clinical outcomes in MS but not RIS. These findings suggest that MTR provides important information about the underlying tissue microstructural integrity of the cSC relevant to clinical disability in established MS.

2025-06-29

Annals of Clinical and Translational Neurology (published)

Cervical Spinal Cord Magnetization Transfer Ratio and Its Relationship With Clinical Outcomes in Multiple Sclerosis

Lisa Eunyoung Lee