Julien Cohen-Adad

Thomas Dagonneau

Maîtrise recherche - Polytechnique

Naga Karthik Enamundram

Doctorat - Polytechnique

Co-superviseur⋅e :

Maîtrise recherche - Polytechnique

Youssef Laatar

Maîtrise recherche - Polytechnique

Julien Laborde-Peyré

Stagiaire de recherche - Polytechnique

Nilser Laines Medina

Doctorat - Polytechnique

Nathan Molinier

Doctorat - Polytechnique

Quentin Revillon

Stagiaire de recherche - Polytechnique

Arthur Toulouse

Maîtrise recherche - Polytechnique

Publications

Considerations and recommendations from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 2 - Ex vivo imaging: added value and acquisition

Kurt G. Schilling

Francesco Grussu

Andrada Ianus

Brian Hansen

Amy F. D. Howard

Rachel L. C. Barrett

Manisha Aggarwal

Stijn Michielse

Fatima Nasrallah

Warda Syeda

Nian Wang

Jelle Veraart

Alard Roebroeck

Andrew F. Bagdasarian

Cornelius Eichner

Farshid Sepehrband

Jan Zimmermann

Lucas Soustelle

Christien Bowman

Benjamin C. Tendler … (voir 38 de plus)

Andreea Hertanu

Ben Jeurissen

Marleen Verhoye

Lucio Frydman

Yohan van de Looij

David Hike

Jeff F. Dunn

Karla Miller

Bennett A. Landman

Noam Shemesh

Adam Anderson

Emilie McKinnon

Shawna Farquharson

Flavio Dell'Acqua

Carlo Pierpaoli

Ivana Drobnjak

Alexander Leemans

Kevin D. Harkins

Maxime Descoteaux

Duan Xu

Hao Huang

Mathieu D. Santin

Samuel C. Grant

Andre Obenaus

Gene S. Kim

Dan Wu

Denis Le Bihan

Stephen J. Blackband

Luisa Ciobanu

Els Fieremans

Ruiliang Bai

Trygve B. Leergaard

Jiangyang Zhang

Tim B. Dyrby

G. Allan Johnson

Julien Cohen‐Adad

Matthew D. Budde

Ileana O. Jelescu

The value of preclinical diffusion MRI (dMRI) is substantial. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dM… (voir plus)RI is increasingly used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages including higher signal-to-noise ratio and spatial resolution compared to in vivo studies, and enabling more advanced diffusion contrasts. Another major advantage of ex vivo dMRI is the direct comparison with histological data as a methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work represents "Part 2" of a 3-part series of recommendations and considerations for preclinical dMRI. We describe best practices for dMRI of ex vivo tissue, with a focus on the value that ex vivo imaging adds to the field of dMRI and considerations in ex vivo image acquisition. We give general considerations and foundational knowledge that must be considered when designing experiments. We describe differences in specimens and models and discuss why some may be more or less appropriate for different studies. We then give guidelines for ex vivo protocols, including tissue fixation, sample preparation, and MR scanning. In each section, we attempt to provide guidelines and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should lie. An overarching goal herein is to enhance the rigor and reproducibility of ex vivo dMRI acquisitions and analyses, and thereby advance biomedical knowledge.

2025-03-03

Magnetic Resonance in Medicine (publié)

Normalizing Spinal Cord Compression Measures in Degenerative Cervical Myelopathy.

Maryam Seif

Armin Curt

Simon Schading-Sassenhausen

Nikolai Pfender

P. Freund

Markus Hupp

2025-02-28

The spine journal (publié)

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

Warda Syeda

Nian Wang

Jelle Veraart

Alard Roebroeck

Andrew F. Bagdasarian

Cornelius Eichner

Farshid Sepehrband

Jan Zimmermann

Lucas Soustelle

Christien Bowman

Benjamin C. Tendler … (voir 38 de plus)

Andreea Hertanu

Ben Jeurissen

Marleen Verhoye

Lucio Frydman

Yohan van de Looij

David Hike

Jeff F. Dunn

Karla Miller

Bennett Landman

Noam Shemesh

Arthur Anderson

Emilie McKinnon

Shawna Farquharson

Flavio Dell’Acqua

Carlo Pierpaoli

Ivana Drobnjak

Alexander Leemans

Kevin D. Harkins

Maxime Descoteaux

Duan Xu

Hao Huang

Mathieu D. Santin

Samuel C. Grant

Andre Obenaus

Gene S. Kim

Dan Wu

Denis Le Bihan

Stephen J. Blackband

Luisa Ciobanu

Els Fieremans

Ruiliang Bai

Trygve B. Leergaard

Jiangyang Zhang

Tim B. Dyrby

G. Allan Johnson

Matthew D. Budde

Ileana O. Jelescu

2025-02-25

Magnetic Resonance in Medicine (publié)

Considerations and recommendations from the <scp>ISMRM</scp> diffusion study group for preclinical diffusion <scp>MRI</scp>: Part 1: In vivo small‐animal imaging

Ileana O. Jelescu

Francesco Grussu

Andrada Ianus

Brian Hansen

Rachel L. C. Barrett

Manisha Aggarwal

Stijn Michielse

Fatima Nasrallah

Warda Syeda

Nian Wang

Jelle Veraart

Alard Roebroeck

Andrew F. Bagdasarian

Cornelius Eichner

Farshid Sepehrband

Jan Zimmermann

Lucas Soustelle

Christien Bowman

Benjamin C. Tendler

Andreea Hertanu … (voir 37 de plus)

Ben Jeurissen

Marleen Verhoye

Lucio Frydman

Yohan van de Looij

David Hike

Jeff F. Dunn

Karla Miller

Bennett Landman

Noam Shemesh

Arthur Anderson

Emilie McKinnon

Shawna Farquharson

Flavio Dell’Acqua

Carlo Pierpaoli

Ivana Drobnjak

Alexander Leemans

Kevin D. Harkins

Maxime Descoteaux

Duan Xu

Hao Huang

Mathieu D. Santin

Samuel C. Grant

Andre Obenaus

Gene S. Kim

Dan Wu

Denis Le Bihan

Stephen J. Blackband

Luisa Ciobanu

Els Fieremans

Ruiliang Bai

Trygve B. Leergaard

Jiangyang Zhang

Tim B. Dyrby

G. Allan Johnson

Matthew D. Budde

Kurt G Schilling

2025-02-25

Magnetic Resonance in Medicine (publié)

Recommendations and guidelines from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 1 -- In vivo small-animal imaging

Ileana O. Jelescu

Francesco Grussu

Andrada Ianus

Brian Hansen

Rachel L. C. Barrett

Manisha Aggarwal

Stijn Michielse

Fatima Nasrallah

Warda Syeda

Nian Wang

Jelle Veraart

Alard Roebroeck

Andrew F. Bagdasarian

Cornelius Eichner

Farshid Sepehrband

Jan Zimmermann

Lucas Soustelle

Christien Bowman

Benjamin C. Tendler

Andreea Hertanu … (voir 37 de plus)

Ben Jeurissen

Marleen Verhoye

Lucio Frydman

Yohan van de Looij

David Hike

Jeff F. Dunn

Karla Miller

Bennett A. Landman

Noam Shemesh

Adam Anderson

Emilie McKinnon

Shawna Farquharson

Flavio Dell'Acqua

Carlo Pierpaoli

Ivana Drobnjak

Alexander Leemans

Kevin D. Harkins

Maxime Descoteaux

Duan Xu

Hao Huang

Mathieu D. Santin

Samuel C. Grant

Andre Obenaus

Gene S. Kim

Dan Wu

Denis Le Bihan

Stephen J. Blackband

Luisa Ciobanu

Els Fieremans

Ruiliang Bai

Trygve B. Leergaard

Jiangyang Zhang

Tim B. Dyrby

G. Allan Johnson

Julien Cohen‐Adad

Matthew D. Budde

Kurt G. Schilling

The value of in vivo preclinical diffusion MRI (dMRI) is substantial. Small-animal dMRI has been used for methodological development and val… (voir plus)idation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. Many of the influential works in this field were first performed in small animals or ex vivo samples. The steps from animal setup and monitoring, to acquisition, analysis, and interpretation are complex, with many decisions that may ultimately affect what questions can be answered using the data. This work aims to serve as a reference, presenting selected recommendations and guidelines from the diffusion community, on best practices for preclinical dMRI of in vivo animals. In each section, we also highlight areas for which no guidelines exist (and why), and where future work should focus. We first describe the value that small animal imaging adds to the field of dMRI, followed by general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in animal species and disease models and discuss how they are appropriate for different studies. We then give guidelines for in vivo acquisition protocols, including decisions on hardware, animal preparation, imaging sequences and data processing, including pre-processing, model-fitting, and tractography. Finally, we provide an online resource which lists publicly available preclinical dMRI datasets and software packages, to promote responsible and reproducible research. An overarching goal herein is to enhance the rigor and reproducibility of small animal dMRI acquisitions and analyses, and thereby advance biomedical knowledge.

2025-02-25

Magnetic Resonance in Medicine (publié)

Automatic segmentation of spinal cord lesions in MS: A robust tool for axial T2-weighted MRI scans

Enamundram Naga Karthik

Julian McGinnis

Ricarda Wurm

Sebastian Ruehling

Robert Graf

Pierre-Louis Benveniste

Markus Lauerer

Jason Talbott

Rohit Bakshi

Shahamat Tauhid

Timothy Shepherd

Achim Berthele

Claus Zimmer

Bernhard Hemmer

Daniel Rueckert

Benedikt Wiestler

Jan S. Kirschke

Mark Mühlau

Deep learning models have achieved remarkable success in segmenting brain white matter lesions in multiple sclerosis (MS), becoming integral… (voir plus) to both research and clinical workflows. While brain lesions have gained significant attention in MS research, the involvement of spinal cord lesions in MS is relatively understudied. This is largely owed to the variability in spinal cord magnetic resonance imaging (MRI) acquisition protocols, high individual anatomical differences, the complex morphology and size of spinal cord lesions - and lastly, the scarcity of labeled datasets required to develop robust segmentation tools. As a result, automatic segmentation of spinal cord MS lesions remains a significant challenge. Although some segmentation tools exist for spinal cord lesions, most have been developed using sagittal T2-weighted (T2w) sequences primarily focusing on cervical spines. With the growing importance of spinal cord imaging in MS, axial T2w scans are becoming increasingly relevant due to their superior sensitivity in detecting lesions compared to sagittal acquisition protocols. However, most existing segmentation methods struggle to effectively generalize to axial sequences due to differences in image characteristics caused by the highly anisotropic spinal cord scans. To address these challenges, we developed a robust, open-source lesion segmentation tool tailored specifically for axial T2w scans covering the whole spinal cord. We investigated key factors influencing lesion segmentation, including the impact of stitching together individually acquired spinal regions, straightening the spinal cord, and comparing the effectiveness of 2D and 3D convolutional neural networks (CNNs). Drawing on these insights, we trained a multi-center model using an extensive dataset of 582 MS patients, resulting in a dataset comprising an entirety of 2,167 scans. We empirically evaluated the model’s segmentation performance across various spinal segments for lesions with varying sizes. Our model significantly outperforms the current state-of-the-art methods, providing consistent segmentation across cervical, thoracic and lumbar regions. To support the broader research community, we integrate our model into the widely-used Spinal Cord Toolbox (v7.0 and above), making it accessible via the command sct_deepseg lesion_ms_axial_t2 -i <path-to-image.nii.gz>.

2025-01-22

medRxiv (prépublication)

Body size and intracranial volume interact with the structure of the central nervous system: A multi-center in vivo neuroimaging study

René Labounek

Monica T. Bondy

Amy Paulson

Mihael Abramovic

Eva Alonso‐Ortiz

Nicole Atcheson

Laura Barlow

Robert 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 … (voir 73 de plus)

Adam Dvorak

Falk Eippert

Karla R. Epperson

Kevin 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

Sławomir Kuśmia

Maria Marcella Laganà

Cornelia Laule

Christine Law

Tobias Leutritz

Yaou Liu

Sara Llufriú

Sean Mackey

Allan R. Martin

Eloy Martínez‐Heras

Loan Mattera

Kristin P. O’Grady

Nico Papinutto

Daniel S. Papp

Deborah Pareto

Todd B. Parrish

Anna Pichiecchio

Ferrán 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

Dimitri Van De Ville

Marios Yiannakas

Kenneth A. Weber

Nikolaus Weiskopf

Richard G. Wise

Patrik O. Wyss

Junqian Xu

Julien Cohen‐Adad

Christophe Lenglet

Igor Nestrašil

Clinical research emphasizes the implementation of rigorous and reproducible study designs that rely on between-group matching or controllin… (voir plus)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 correlates 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). Intracranial volume (ICV) correlates with body height (r = 0.46) and the brain volumes and CSA-WM (0.37 ≤ r ≤ 0.77). In comparison, age correlates with cortical GM volume, precentral GM volume, and cortical thickness (-0.21 ≥ r ≥ -0.27). Body weight correlates with magnetization transfer ratio in the SC WM, dorsal columns, and lateral corticospinal tracts (-0.20 ≥ r ≥ -0.23). Body weight further correlates 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 correlates with brain volumes (0.39 ≤ r ≤ 0.64), and 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 age, sex, or sex and age, explained 2 ± 2%, 24 ± 10%, or 26 ± 10%, of data variance in brain volumetry and SC CSA. The amount of explained variance increased to 33 ± 11%, 41 ± 17%, or 46 ± 17%, when body height, ICV, or body height and ICV were added into the mixture model. In females, the explained variances halved suggesting another unidentified biological factor(s) determining females’ central nervous system (CNS) morphology. In conclusion, body size and ICV are significant biological variables. 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; and body size and ICV should be considered as covariates in statistical analyses. Normalization of different brain regions with ICV diminishes their correlations with body size, but simultaneously amplifies ICV-related variance (r = 0.72 ± 0.07) and suppresses volume variance of the different brain regions (r = 0.12 ± 0.19) in the normalized measurements.

2024-12-31

Imaging Neuroscience (publié)

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 Law

Dario Pfyffer

David J. Lythgoe

Dimitra Tsivaka

Dimitri Van De Ville

Falk Eippert

Fauziyya Muhammad

Gary H. Glover

Gergely David

Grace Haynes

Jan Haaker

J.C. Brooks

Jürgen Finsterbusch … (voir 21 de plus)

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 Barry

Sean Mackey

Shahabeddin Vahdat

Simon Schading‐Sassenhausen

Stephen B. McMahon

Todd Parish

Véronique Marchand‐Pauvert

Yufen Chen

Zachary A. Smith

Kenneth A. Weber

Benjamin De Leener

Julien Cohen‐Adad

Functional magnetic resonance imaging (fMRI) of the spinal cord is relevant for studying sensation, movement, and autonomic function. Prepro… (voir plus)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/1.3.1 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.

2024-12-31

Imaging Neuroscience (publié)

Morphometric characteristics of tibial nerve and their relationship with age

Shahram Oveisgharan

Armand Collin

Jingyun Yang

Sue E. Leurgans

Veronique VanderHorst

David A. Bennett

Osvaldo Delbono

Aron S. Buchman

Peripheral nerve comprises a crucial component of the distributed motor/sensory system. However, there is a paucity of data on peripheral ne… (voir plus)rve morphology derived from large numbers of older adults. This study aimed to quantify the morphometric characteristics of myelinated nerve fibres of the tibial nerve obtained from deceased community-dwelling older adults and examine their association with age. The tibial nerves were obtained from consecutive autopsies of older adults without a history of diabetes who were participants of the Rush Memory and Aging Project, an ongoing longitudinal clinical-autopsy study. A nerve fascicle, obtained from a fixed popliteal segment of the tibial nerve, was separated from the blood vessels and adipose tissue for postmortem examination under an optical microscope. Morphometric characteristics of the myelinated nerve fibres were automatically segmented and quantified using our open-source software AxonDeepSeg. The participants (N = 140) had a mean age of 92.0 years (SD = 5.4) at death, and 72.1% (N = 101) were women. We examined 754 247 myelinated nerve fibres, with an average 5387 (SD = 3436) nerve fibres per participant. The average diameter of myelinated nerve fibres was 4.9 µm (SD = 3.1), axon diameter was 2.0 µm (SD = 1.4), myelin thickness was 1.4 µm (SD = 0.96) and the g-ratio (ratio of axon diameter to myelinated nerve fibre diameter) was 0.45 (SD = 0.17). The relationship between axon diameter and myelin thickness was nonlinear. Myelin was thicker in larger axons up to a diameter of 8 µm, beyond which myelin thickness plateaued. Older age at death was associated with smaller myelinated nerve fibres, smaller axons and thinner myelin. However, age at death was not correlated with myelinated nerve fibre density and was not associated with the average of g-ratio. The association between older age and smaller myelinated nerve fibres was largely attributable to a lower percentage of myelinated nerve fibres >8 µm. We conclude that the smaller tibial myelinated nerve fibres observed in older adults may reflect axonal atrophy rather than degeneration and regeneration of the myelinated nerve fibres. Further research is needed to investigate the pathologies and molecular mechanisms underlying these age-related morphometric changes and their clinical implications in older adults.

2024-12-31

Brain Communications (publié)

Rootlets-based registration to the PAM50 spinal cord template

Valeria Oliva

Kenneth A. Weber II

Spinal cord functional MRI studies require precise localization of spinal levels for reliable voxelwise group analyses. Traditional template… (voir plus)-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 to the traditional disc-based method. Notably, rootlet positions were more stable across neck positions. Group-level analysis of task-based functional MRI using rootlet-based increased Z scores and activation cluster size compared to 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.

2024-12-31

Imaging Neuroscience (publié)

Spinal cord demyelination predicts neurological deterioration in patients with mild degenerative cervical myelopathy

Abdul Al-Shawwa

Michael Craig

Kalum Ost

David Anderson

Steve Casha

W Bradley Jacobs

Nathan Evaniew

Saswati Tripathy

Jacques Bouchard

Peter Lewkonia

Fred Nicholls

Alex Soroceanu

Ganesh Swamy

Kenneth C Thomas

Stephan duPlessis

Michael MH Yang

Nicholas Dea

Jefferson R Wilson

David W Cadotte

Degenerative cervical myelopathy (DCM) is the most common form of atraumatic spinal cord injury globally. Clinical guidelines regarding surg… (voir plus)ery for patients with mild DCM and minimal symptoms remain uncertain. This study aims to identify imaging and clinical predictors of neurological deterioration in mild DCM and explore pathophysiological correlates to guide clinical decision-making. Patients with mild DCM underwent advanced MRI scans that included T2-weighted, diffusion tensor imaging and magnetisation transfer (MT) sequences, along with clinical outcome measures at baseline and 6-month intervals after enrolment. Quantitative MRI (qMRI) metrics were derived above and below maximally compressed cervical levels (MCCLs). Various machine learning (ML) models were trained to predict 6 month neurological deterioration, followed by global and local model interpretation to assess feature importance. A total of 49 patients were followed for a maximum of 2 years, contributing 110 6-month data entries. Neurological deterioration occurred in 38% of cases. The best-performing ML model, combining clinical and qMRI metrics, achieved a balanced accuracy of 83%, and an area under curve-receiver operating characteristic of 0.87. Key predictors included MT ratio (demyelination) above the MCCL in the dorsal and ventral funiculi and moderate tingling in the arm, shoulder or hand. qMRI metrics significantly improved predictive performance compared to models using only clinical (bal. acc=68.1%) or imaging data (bal. acc=57.4%). Reduced myelin content in the dorsal and ventral funiculi above the site of compression, combined with sensory deficits in the hands and gait/balance disturbances, predicts 6-month neurological deterioration in mild DCM and may warrant early surgical intervention.

2024-12-31

BMJ Neurology Open (publié)

Towards contrast-agnostic soft segmentation of the spinal cord

Enamundram Naga Karthik

Charidimos Tsagkas

Emanuele Pravatà

Cristina Granziera

Andrew Smith

Kenneth Arnold Weber II

Spinal cord segmentation is clinically relevant and is notably used to compute spinal cord cross-sectional area (CSA) for the diagnosis and … (voir plus)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 (

2024-12-31

Medical Image Anal. (publié)