Julien Cohen-Adad

Marco Masullo

Fabio Mangini

Lorenzo Giovannelli

Daniele Mascali

Tommaso Gili

Valerio Pisani

Ugo Nocentini

Federico Giove

Michela Fratini

BACKGROUND: Functional Magnetic Resonance Imaging (fMRI) is based on the Blood Oxygenation Level Dependent contrast and has been exploited f… (see more)or the indirect study of the neuronal activity within both the brain and the spinal cord. However, the interpretation of spinal cord fMRI (scfMRI) is still controversial and its diffusion is rather limited because of technical limitations. Overcoming these limitations would have a beneficial effect for the assessment and follow-up of spinal injuries and neurodegenerative diseases. PURPOSE: This study was aimed at systematically verify whether sagittal scanning in scfMRI using EPI readout is a viable alternative to the more common axial scanning, and at optimizing a pipeline for EPI-based scfMRI data analysis, based on Spinal Cord Toolbox (SCT). METHODS: Forty-five healthy subjects underwent MRI acquisition in a Philips Achieva 3T MRI scanner. T2*-weighted fMRI data were acquired using a GE-EPI sequence along sagittal and axial planes during an isometric motor task. Differences on benchmarks were assessed via paired two-sample t-test at p=0.05. RESULTS: We investigated the impact of the acquisition strategy by means of various metrics such as Temporal Signal to Noise Ratio (tSNR), Dice Coefficient to assess geometric distortions, Reproducibility and Sensitivity. tSNR was higher in axial than in sagittal scans, as well as reproducibility within the whole cord mask (t=7.4, p0.01) and within the GM mask (t=4.2, p0.01). The other benchmarks, associated with distortion and functional response, showed no differenc

2025-05-12

PLOS One (published)

Monitoring morphometric drift in lifelong learning segmentation of the spinal cord

Enamundram Naga Karthik

Sandrine B'edard

Jan Valovsek

Christoph Aigner

Elise Bannier

Josef Bednavr'ik

Virginie Callot

Anna Combes

Armin Curt

Gergely David

Falk Eippert

Lynn Farner

M. G. Fehlings

Patrick Freund

Tobias Granberg

Cristina Granziera

Rhscir Network Imaging Group

Ulrike Horn

Tom'avs Hor'ak

Suzanne Humphreys … (see 36 more)

Markus Hupp

Anne Kerbrat

Nawal Kinany

Shannon Kolind

Petr Kudlivcka

Anna Lebret

Lisa Eunyoung Lee

Caterina Mainero

Allan R. Martin

Megan McGrath

Govind Nair

Kristin P. O’Grady

Jiwon Oh

Russell Ouellette

Nikolai Pfender

Dario Pfyffer

P. Pradat

Alexandre Prat

Emanuele Pravatà

D. S. Reich

Ilaria Ricchi

Naama Rotem-Kohavi

Simon Schading-Sassenhausen

Maryam Seif

Andrew C. Smith

Seth Aaron Smith

Grace Sweeney

Roger Tam

Anthony Traboulsee

Constantina A. Treaba

Charidimos Tsagkas

Zachary Vavasour

Dimitri Van De Ville

Kenneth A. Weber

Sarath Chandar

2025-05-02

ArXiv (preprint)

Impact of through‐slice gradient optimization for dynamic slice‐wise shimming in the cervico‐thoracic spinal cord

Arnaud Breheret

Alexandre D'Astous

Yixin Ma

Jason P. Stockmann

2025-05-01

Magnetic Resonance in Medicine (published)

Rootlets-based registration to the spinal cord PAM50 template

Sandrine B'edard

Jan Valovsek

Valeria Oliva

Kenneth A. Weber

2025-04-30

ArXiv (preprint)

Spinal Cord Tract Integrity in Degenerative Cervical Myelopathy.

Newton Cho

Abdul Al-Shawwa

W. Bradley Jacobs

Nathan Evaniew

Jacques Bouchard

Steven Casha

Stephan duPlessis

Peter Lewkonia

Fred Nicholls

Alex Soroceanu

Ganesh Swamy

Kenneth C. Thomas

Michael M.H. Yang

David W. Cadotte

2025-04-03

Neurosurgery (published)

Addressing Missing Modality Challenges in MRI Images: A Comprehensive Review

Reza Azad

Mohammad Dehghanmanshadi

Nika Khosravi

Dorit Merhof

2025-04-01

Computational Visual Media (published)

Normalizing Spinal Cord Compression Measures in Degenerative Cervical Myelopathy.

Sandrine Bédard

Jan Valošek

Maryam Seif

Armin Curt

Simon Schading-Sassenhausen

Nikolai Pfender

P. Freund

Markus Hupp

2025-03-01

The spine journal (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

Warda Syeda

Nian Wang

Jelle Veraart

Alard Roebroeck

Andrew F. Bagdasarian

Cornelius Eichner

Farshid Sepehrband

Jan Zimmermann

Lucas Soustelle

Christien Bowman

Benjamin C. Tendler … (see 38 more)

Andreea Hertanu

Ben Jeurissen

Marleen Verhoye

Lucio Frydman

Yohan van de Looij

David Hike

Jeff F. Dunn

Karla Miller

Bennett 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

Matthew D. Budde

Ileana O. Jelescu

2025-02-26

Magnetic Resonance in Medicine (published)

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

Enamundram Naga Karthik

J. McGinnis

R. Wurm

S. Ruehling

R. Graf

Jan Valošek

Pierre-Louis Benveniste

M. Lauerer

J. Talbott

R. Bakshi

S. Tauhid

T. Shepherd

A. Berthele

C. Zimmer

B. Hemmer

D. Rueckert

B. Wiestler

J. Kirschke

M. Muehlau

Deep learning models have achieved remarkable success in segmenting brain white matter lesions in multiple sclerosis (MS), becoming integral… (see more) 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 -task seg_sc_ms_lesion_axial_t2w -i .

2025-01-23

medRxiv (preprint)

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

Jan Valošek

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… (see more) 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 -task seg_sc_ms_lesion_axial_t2w -i .

2025-01-23

medRxiv (preprint)

Multi-center benchmarking of cervical spinal cord RF coils for 7 T MRI: A traveling spines study

Eva Alonso‐Ortiz

Daniel Papp

Robert L. Barry

Kyota Poëti

Alan C. Seifert

Kyle M. Gilbert

Nibardo Lopez‐Rios

Jan Paska

Falk Eippert

Nikolaus Weiskopf

Laura Beghini

Nadine Graedel

Robert Trampel

Martina F Callaghan

Christoph S. Aigner

Patrick Freund

Maryam Seif

Aurélien Destruel

Virginie Callot

Johanna Vannesjo … (see 1 more)

2025-01-17

bioRxiv (preprint)

Multi-center benchmarking of cervical spinal cord RF coils for 7 T MRI: A traveling spines study

Eva Alonso‐Ortiz

Daniel Papp

Robert L. Barry

Kyota Poëti

Alan C. Seifert

Kyle M. Gilbert

Nibardo Lopez‐Rios

Jan Paska

Falk Eippert

N. Weiskopf

Laura Beghini

Nadine Graedel

Robert Trampel

M. F. Callaghan

Christoph S Aigner

Patrick Freund

Maryam Seif

A. Destruel

Virginie Callot

Johanna Vannesjo … (see 1 more)

Purpose The depth within the body, small diameter, long length, and varying tissue surrounding the spinal cord impose specific consideration… (see more)s when designing radiofrequency coils. The optimal coil configuration for 7 T cervical spinal cord MRI is unknown and, currently, there are very few coil options. The purpose of this work was (1) to establish a quality control protocol for evaluating 7 T cervical spinal cord coils and (2) to use that protocol to evaluate the performance of 4 different coil designs. Methods Three healthy volunteers and a custom anthropomorphic phantom (the traveling spines cohort) were scanned at seven 7 T imaging centers using a common protocol and each center’s specific cervical spinal cord coil. Four different coil designs were tested (two in-house, one Rapid Biomedical, and one MRI.TOOLS design). Results The Rapid Biomedical coil was found to have the highest B1+ efficiency, whereas one of the in-house designs (NeuroPoly Lab) had the highest SNR and the largest spinal cord coverage. The MRI.TOOLS coil had the most uniform B1+ profile along the cervical spinal cord; however, it was limited in its ability to provide the requested flip angles (especially for larger individuals). The latter was also the case for the second in-house coil (MSSM). Conclusion The results of this study serve as a guide for the spinal cord MRI community in selecting the most suitable coil based on specific requirements and offer a standardized protocol for assessing future coils.

2025-01-17

bioRxiv (preprint)