An open-access lumbosacral spine MRI dataset with enhanced spinal nerve root structure resolution.
Journal
Scientific data
ISSN: 2052-4463
Titre abrégé: Sci Data
Pays: England
ID NLM: 101640192
Informations de publication
Date de publication:
15 Oct 2024
15 Oct 2024
Historique:
received:
17
04
2024
accepted:
23
09
2024
medline:
16
10
2024
pubmed:
16
10
2024
entrez:
15
10
2024
Statut:
epublish
Résumé
Spinal cord injury (SCI) profoundly affects an individual's ability to move. Fortunately, recent advancements in neuromodulation, particularly the spatio-temporal epidural electrical stimulation (EES) targeting the spinal nerve roots, promoted rapid rehabilitation of SCI patients. Such neuromodulation techniques require precise anatomical modelling of spinal cord. However, the lack of spine imaging datasets, especially high-quality magnetic resonance imaging (MRI) datasets highlighting nerve roots, hinders the translation of EES into medical practice. To address this problem, we introduce an open-access lumbosacral spine MRI dataset acquired in 14 healthy adults, using constructive interference in steady state (CISS) sequence, double echo steady state (DESS) sequence, and T2-weight turbo spin echo (T2-TSE) sequence, with enhanced nerve root resolution. The dataset also includes the corresponding anatomical annotations of nerve roots and the final reconstructed 3D spinal cord models. The quality of our dataset is assessed using image quality metrics implemented in MRI quality control tool (MRIQC). Our dataset provides a valuable platform to promote a wide range of spinal cord neuromodulation research and collaboration among neurorehabilitation engineers.
Identifiants
pubmed: 39406785
doi: 10.1038/s41597-024-03919-4
pii: 10.1038/s41597-024-03919-4
doi:
Types de publication
Dataset
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1131Informations de copyright
© 2024. The Author(s).
Références
Musienko, P., Heutschi, J., Friedli, L., Van Den Brand, R. & Courtine, G. Multi-system neurorehabilitative strategies to restore motor functions following severe spinal cord injury. Experimental neurology 235, 100–109 (2012).
doi: 10.1016/j.expneurol.2011.08.025
pubmed: 21925172
Won, S. M., Song, E., Reeder, J. T. & Rogers, J. A. Emerging modalities and implantable technologies for neuromodulation. Cell 181, 115–135 (2020).
doi: 10.1016/j.cell.2020.02.054
pubmed: 32220309
Bizzi, E., Tresch, M. C., Saltiel, P. & d’Avella, A. New perspectives on spinal motor systems. Nature Reviews Neuroscience 1, 101–108 (2000).
doi: 10.1038/35039000
pubmed: 11252772
Angeli, C. A., Edgerton, V. R., Gerasimenko, Y. P. & Harkema, S. J. Altering spinal cord excitability enables voluntary movements after chronic complete paralysis in humans. Brain 137, 1394–1409 (2014).
doi: 10.1093/brain/awu038
pubmed: 24713270
pmcid: 3999714
Harkema, S. et al. Effect of epidural stimulation of the lumbosacral spinal cord on voluntary movement, standing, and assisted stepping after motor complete paraplegia: a case study. The Lancet 377, 1938–1947 (2011).
doi: 10.1016/S0140-6736(11)60547-3
Rowald, A. et al. Activity-dependent spinal cord neuromodulation rapidly restores trunk and leg motor functions after complete paralysis. Nature medicine 28, 260–271 (2022).
doi: 10.1038/s41591-021-01663-5
pubmed: 35132264
Wenger, N. et al. Spatiotemporal neuromodulation therapies engaging muscle synergies improve motor control after spinal cord injury. Nature medicine 22, 138–145 (2016).
doi: 10.1038/nm.4025
pubmed: 26779815
pmcid: 5061079
Wagner, F. B. et al. Targeted neurotechnology restores walking in humans with spinal cord injury. Nature 563, 65–71 (2018).
doi: 10.1038/s41586-018-0649-2
pubmed: 30382197
Angeli, C. A. et al. Recovery of over-ground walking after chronic motor complete spinal cord injury. New England Journal of Medicine 379, 1244–1250 (2018).
doi: 10.1056/NEJMoa1803588
pubmed: 30247091
Lorach, H. et al. Walking naturally after spinal cord injury using a brain–spine interface. Nature 1–8 (2023).
Canbay, S. et al. Anatomical relationship and positions of the lumbar and sacral segments of the spinal cord according to the vertebral bodies and the spinal roots. Clinical anatomy 27, 227–233 (2014).
doi: 10.1002/ca.22253
pubmed: 23649511
Pham, H. H., Trung, H. N. & Nguyen, H. Q. Vindr-spinexr: A large annotated medical image dataset for spinal lesions detection and classification from radiographs. PhysioNet (2021).
Liebl, H. et al. A computed tomography vertebral segmentation dataset with anatomical variations and multi-vendor scanner data. Scientific data 8, 284 (2021).
doi: 10.1038/s41597-021-01060-0
pubmed: 34711848
pmcid: 8553749
Löffler, M. T. et al. A vertebral segmentation dataset with fracture grading. Radiology: Artificial Intelligence 2, e190138 (2020).
pubmed: 33937831
pmcid: 8082364
van der Graaf, J. W. et al. Lumbar spine segmentation in mr images: a dataset and a public benchmark. Scientific Data 11, 264 (2024).
doi: 10.1038/s41597-024-03090-w
pubmed: 38431692
pmcid: 10908819
Sudirman, S. et al. Lumbar spine mri dataset. Mendeley Data 2, 2019 (2019).
Zhang, X. et al. Seuneter: Channel attentive u-net for instance segmentation of the cervical spine mri medical image. Frontiers in Physiology 13, 2564 (2022).
doi: 10.3389/fphys.2022.1081441
Cohen-Adad, J. et al. Open-access quantitative mri data of the spinal cord and reproducibility across participants, sites and manufacturers. Scientific data 8, 219 (2021).
doi: 10.1038/s41597-021-00941-8
pubmed: 34400655
pmcid: 8368310
Esteban, O. et al. Mriqc: Advancing the automatic prediction of image quality in mri from unseen sites. PloS one 12, e0184661 (2017).
doi: 10.1371/journal.pone.0184661
pubmed: 28945803
pmcid: 5612458
Li, X., Morgan, P. S., Ashburner, J., Smith, J. & Rorden, C. The first step for neuroimaging data analysis: Dicom to nifti conversion. Journal of neuroscience methods 264, 47–56 (2016).
doi: 10.1016/j.jneumeth.2016.03.001
pubmed: 26945974
Gorgolewski, K. J. et al. The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Scientific Data 3, 160044 (2016).
doi: 10.1038/sdata.2016.44
pubmed: 27326542
pmcid: 4978148
Boré, A., Guay, S., Bedetti, C., Meisler, S. & GuenTher, N. Dcm2Bids, https://doi.org/10.5281/zenodo.8436509 (2023).
Rigoard, P. et al. Multicolumn spinal cord stimulation lead implantation using an optic transligamentar minimally invasive technique. Neurosurgery 73, 550–553 (2013).
doi: 10.1227/NEU.0000000000000008
pubmed: 23756742
Liu, J. et al. An open-access lumbosacral spine mri dataset with enhanced spinal nerve root structure resolution. figshare https://doi.org/10.6084/m9.figshare.c.7372564 (2024).
Magnotta, V. A., Friedman, L. & BIRN, F. Measurement of signal-to-noise and contrast-to-noise in the fbirn multicenter imaging study. Journal of digital imaging 19, 140–147 (2006).
doi: 10.1007/s10278-006-0264-x
pubmed: 16598643
pmcid: 3045184
Mesbah, S. et al. Neuroanatomical mapping of the lumbosacral spinal cord in individuals with chronic spinal cord injury. Brain Communications 5, fcac330 (2023).
doi: 10.1093/braincomms/fcac330
pubmed: 36632181