Author Correction: Open-access quantitative MRI data of the spinal cord and reproducibility across participants, sites and manufacturers

Cohen‐Adad, Julien; Alonso‐Ortiz, Eva; Abramovic, Mihael; Arneitz, Carina; Atcheson, Nicole; Barlow, Laura; Barry, Robert; Barth, Markus; Battiston, Marco; Büchel, Christian; Budde, Matthew D.; Callot, Virginie; Combes, Anna J.E.; Leener, Benjamin De; Descoteaux, Maxime; Sousa, Paulo Loureiro de; Dostál, Marek; Doyon, Julien; Dvorak, Adam; Eippert, Falk; Epperson, Karla R.; Epperson, Kevin S.; Freund, Patrick; Finsterbusch, Jürgen; Foias, Alexandru; Fratini, Michela; Fukunaga, Issei; Wheeler-Kingshott, C; Germani, Giancarlo; Gilbert, Guillaume; Giove, Federico; Gros, Charley; Grussu, Francesco; Hagiwara, Akifumi; Henry, Pierre Gilles; Horák, Tomáš; Hori, Masaaki; Joers, James M.; Kamiya, Kazutaka; Karbasforoushan, Haleh; Keřkovský, Miloš; Khatibi, Ali; Kim, Joo Won; Kinany, Nawal; Kitzler, Hagen H.; Kolind, Shannon; Kong, Yazhuo; Kudlička, Petr; Kuntke, Paul; Kurniawan, Nyoman D.; Kusmia, Slawomir; Labounek, René; Laganà, Maria Marcella; Laule, Cornelia; Law, Christine S.; Lenglet, Christophe; Leutritz, Tobias; Llufriú, Sara; Mackey, Sean; Martínez‐Heras, Eloy; Mattera, Loan; Nestrašil, Igor; O’Grady, Kristin P.; Papinutto, Nico; Papp, Daniel S.; Pareto, Deborah; Parrish, Todd B.; Pichiecchio, Anna; Prados, Ferrán; Ruitenberg, Marc J.; Samson, R; Savini, Giovanni; Seif, Maryam; Seifert, Alan C.; Smith, Alex K.; Smith, Seth A.; Smith, Zachary A.; Solana, Elisabeth; Suzuki, Yuichi; Tackley, George; Tinnermann, Alexandra; Valošek, Jan; Ville, Dimitri Van De; Yiannakas, Marios C.; Weber, Kenneth A.; Weiskopf, Nikolaus; Wise, Richard G.; Wyss, Patrik O.; Xu, Junqian

doi:10.1038/s41597-021-01026-2

Cited by 3 publications

(5 citation statements)

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“…Here, we found that the inter-site CoV of CSA ranged between 2.90% and 5.76%, which is reasonably low. Comparing these results with reliability of directly acquired MRI, the inter-site repeatability of cord segmentation across different MRI systems and protocols was recently addressed in a multicenter study including 42 sites across the world ( Cohen-Adad et al, 2021 ). This study reported inter-site CoV of CSA based on acquired T 1 -w images (including MPRAGE) separately for different MRI vendors, which ranged between 3.08% and 4.41% being comparable to the results obtained from synT 1 -w in our study.…”

Section: Intra-and Inter-site Repeatabilitymentioning

confidence: 99%

“…This study reported inter-site CoV of CSA based on acquired T 1 -w images (including MPRAGE) separately for different MRI vendors, which ranged between 3.08% and 4.41% being comparable to the results obtained from synT 1 -w in our study. However, we neither separated the different MRI systems nor averaged the CSA measurements across sev-eral vertebral levels in our analysis which needs to be considered when comparing these studies ( Cohen-Adad et al, 2021 ;Weeda et al, 2019 ).…”

Section: Intra-and Inter-site Repeatabilitymentioning

confidence: 99%

“…at early time points post-injury or in different diseases with slower atrophy rates). Therefore, it is recommended to use the same MR protocols in longitudinal studies ( Cohen-Adad et al, 2021 ).…”

Section: Agreement Between Csa From Mprage and Synt 1 -Wmentioning

confidence: 99%

“…One of the most common magnetic resonance imaging (MRI) modalities is T 1 -weighted (T 1 -w) MRI which is routinely applied in the clinics and research to assess tissue structures and determine volumetric changes in the brain and spinal cord. For instance, spinal cord geometry measures such as the cross-sectional area (CSA) can be determined on T 1 -w images ( Chien et al, 2020 ;Cohen-Adad et al, 2021 ;Freund et al, 2010 ;Lundell et al, 2011 ). Measuring the CSA has been suggested as a marker for pathologic effects in different diseases such as spinal cord injury (SCI) and multiple sclerosis, and as a potential outcome measure in clinical trials for which reliable MRI protocols are required ( Casserly et al, 2018 ;Freund et al, 2013a ;Kearney et al, 2015 ;Schmierer and Miquel, 2018 ;Seif et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Reliability of spinal cord measures based on synthetic T1-weighted MRI derived from multiparametric mapping (MPM)

et al. 2023

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Section: Intra-and Inter-site Repeatabilitymentioning

confidence: 99%

Section: Intra-and Inter-site Repeatabilitymentioning

confidence: 99%

Section: Agreement Between Csa From Mprage and Synt 1 -Wmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Reliability of spinal cord measures based on synthetic T1-weighted MRI derived from multiparametric mapping (MPM)

et al. 2023

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“…The MRI scanner acquisition parameters, including voxel size and slice thickness for the T2-weighted axial sequence, were collected and the aT2w sequence for each patient was converted into a series of JPEG images (Cervical SCI Dataset). All images from Spine Generic Dataset [15] were resized into 320x320 pixels to standardize input size and computational e ciency. We standardized the color range between various MRI images with contrast stretching.…”

Section: Data Pre-processingmentioning

confidence: 99%

A Deep Learning Approach for Spine Cervical Injury Severity Determination through Axial and Sagittal Magnetic Resonance Imaging Segmentation and Classification

Wiguna,

Kristian,

Deslivia

et al. 2023

Preprint

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Objectives: Spinal cord injuries (SCI) require extensive efforts to predict the outcome of patients. While the ASIA Impairment Scale is the gold standard to assess patients with SCI, it has some limitations due to the subjectivity and impracticality in certain cases. Recent advances in machine learning (ML) and image recognition have prompted research into using these tools to predict outcomes. The aim of this study is to present a comprehensive analysis using deep learning techniques to evaluate and predict cervical spine injuries from MRI scans. Materials & Method: This is a cross-sectional database study, with patients admitted with traumatic and nontraumatic cervical SCI from 2019 to 2022 were included in our study. MRI images were labelled by four senior resident physicians. We trained a deep convolutional neural network using axial and sagittal cervical MRI images from our dataset and assessed model performance. Result: In the axial spinal cord segmentation, we achieved a dice score of 0.94 for and IoU score of 0.89. In the sagittal spinal cord segmentation, we obtained a dice scores up to 0.9201 and IoU scores up to 0.8541. The model for axial image score classification gave a satisfactory result with an f1 score of 0.72 and AUC of 0.79. Conclusion: Deep learning has been used in automated diagnostic tools, showing promise for significant future advancement. Our models were effectively able to identify cervical spinal cord injury on T2-weighted MR images with satisfactory performance. Further research is necessary to create an even more advanced model for predicting patient outcomes in spinal cord injury cases.

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Functional magnetic resonance imaging of the lumbosacral cord during a lower extremity motor task

Kündig,

Finsterbusch,

Freund

et al. 2024

Imaging Neuroscience

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Blood-oxygen-level dependent (BOLD) functional magnetic resonance imaging (fMRI) can be used to map neuronal function in the cervical cord, yet conclusive evidence supporting its applicability in the lumbosacral cord is still lacking. This study aimed to (i) demonstrate the feasibility of BOLD fMRI for indirectly mapping neural activity in the lumbosacral cord during a unilateral lower extremity motor task and (ii) investigate the impact of echo time (TE) on the BOLD effect size. Twelve healthy volunteers underwent BOLD fMRI using four reduced-field-of-view single-shot gradient-echo echo planar imaging sequences, all with the same geometry but different TE values ranging from 20 to 42 ms. Each sequence was employed to acquire a single 6-minute rest run and two 10-minute task runs, which included alternating 15-second blocks of rest and unilateral ankle dorsi- and plantar flexion. We detected lateralized task-related BOLD activity at neurological levels S4 to L1, centered at the ipsilateral (right) ventral spinal cord but also extending into the ipsilateral dorsal spinal cord. This pattern of activation is consistent with our current understanding of spinal cord organization, wherein lower motor neurons are located in the ventral gray matter horn, while sensory neurons of the proprioceptive pathway, activated during the movement, are located in the dorsal horns. At the subject level, BOLD activity showed considerable variability but was lateralized in all participants. The highest BOLD effect size within the ipsilateral ventral spinal cord, as well as the highest split-half reliability, was observed at a TE of 42 ms. Sequences with a shorter TE (20 and 28 ms) also detected activity in the medioventral part of the spinal cord, likely representing large vein effects. In summary, our results demonstrate the feasibility of detecting task-related BOLD activity in the lumbosacral cord induced by voluntary lower limb movements. BOLD fMRI in the lumbosacral cord has significant implications for assessing motor function and its alterations in disease or after spinal cord injury.

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Author Correction: Open-access quantitative MRI data of the spinal cord and reproducibility across participants, sites and manufacturers

Cited by 3 publications

References 0 publications

Reliability of spinal cord measures based on synthetic T1-weighted MRI derived from multiparametric mapping (MPM)

Reliability of spinal cord measures based on synthetic T1-weighted MRI derived from multiparametric mapping (MPM)

A Deep Learning Approach for Spine Cervical Injury Severity Determination through Axial and Sagittal Magnetic Resonance Imaging Segmentation and Classification

Functional magnetic resonance imaging of the lumbosacral cord during a lower extremity motor task

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