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Review
. 2024 Jul 1;23(3):307-315.
doi: 10.2463/mrms.rev.2023-0159. Epub 2024 Mar 12.

Reproducible Spinal Cord Quantitative MRI Analysis with the Spinal Cord Toolbox

Affiliations
Review

Reproducible Spinal Cord Quantitative MRI Analysis with the Spinal Cord Toolbox

Jan Valošek et al. Magn Reson Med Sci. .

Abstract

The spinal cord plays a pivotal role in the central nervous system, providing communication between the brain and the body and containing critical motor and sensory networks. Recent advancements in spinal cord MRI data acquisition and image analysis have shown a potential to improve the diagnostics, prognosis, and management of a variety of pathological conditions. In this review, we first discuss the significance of standardized spinal cord MRI acquisition protocol in multi-center and multi-manufacturer studies. Then, we cover open-access spinal cord MRI datasets, which are important for reproducible science and validation of new methods. Finally, we elaborate on the recent advances in spinal cord MRI data analysis techniques implemented in the open-source software package Spinal Cord Toolbox (SCT).

Keywords: quantitative magnetic resonance imaging; reproducibility; spinal cord; spinal cord toolbox.

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Conflict of interest statement

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Figures

Fig. 1
Fig. 1
Structural and microstructure quantitative MRI sequences included in the spine-generic protocol with their possible applications (in red). Adapted from Cohen-Adad et al., 20217. Details of the spine-generic protocol are accessible for each MRI manufacturer/model (with downloadable files) at this address: https://github.com/spine-generic/protocols. ME-GRE, multi-echo gradient-recalled echo; WM, white matter.
Fig. 2
Fig. 2
The number and proportion of studies citing SCT across pathologies (total number of citations from peer-reviewed journals: 224). The figure is current as of November 22nd, 2023. SCT, Spinal Cord Toolbox.
Fig. 3
Fig. 3
Overview of a template-based analysis using SCT. Firstly, structural data (e.g., T1w or T2w scan at 1 mm isotropic resolution or similar) is registered to the template (blue arrows). Additional quantitative MRI data acquired during the same scan session (e.g., magnetization transfer, diffusion MRI, functional MRI) are registered to the structural data, and then template objects are warped to the quantitative data (green arrows). To improve the accuracy of template registration, it is possible to add a step where gray matter is segmented (manually or automatically) and then warped to the gray matter template in order to update the warping fields (purple arrows). Subsequently, those quantitative MRI metrics can be quantified within specific white matter tracts using the white matter atlas (red arrow). Cord and gray matter cross-sectional area can also be computed across vertebral levels. Adapted from Cohen-Adad et al., 20186. SCT, Spinal Cord Toolbox.
Fig. 4
Fig. 4
QC report to review the automatic processing. The expert running the analysis pipeline can conveniently review all processing via a web-based interface, and flag the processes as “pass”, “fail” (process failed and should be manually corrected), or “artifact” (the image has excessive artifacts and should be discarded). QC, quality control.

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References

    1. Cohen-Adad J, Wheeler-Kingshott C. Quantitative MRI of the spinal cord. 2014:1–311.
    1. Combes AJE, Clarke MA, O’Grady KP, Schilling KG, Smith SA. Advanced spinal cord MRI in multiple sclerosis: Current techniques and future directions. Neuroimage Clin 2022; 36:103244. - PMC - PubMed
    1. Barritt AW, Gabel MC, Cercignani M, Leigh PN. Emerging magnetic resonance imaging techniques and analysis methods in amyotrophic lateral sclerosis. Front Neurol 2018; 9:1065. - PMC - PubMed
    1. Freund P, Seif M, Weiskopf N, et al. MRI in traumatic spinal cord injury: From clinical assessment to neuroimaging biomarkers. Lancet Neurol 2019; 18: pp. 1123–1135. - PubMed
    1. Badhiwala JH, Ahuja CS, Akbar MA, et al. Degenerative cervical myelopathy — update and future directions. Nat Rev Neurol 2020; 16:108–124. - PubMed

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