Reproducible Spinal Cord Quantitative MRI Analysis with the Spinal Cord Toolbox
Jazyk angličtina Země Japonsko Médium print-electronic
Typ dokumentu časopisecké články, přehledy
PubMed
38479843
PubMed Central
PMC11234946
DOI
10.2463/mrms.rev.2023-0159
Knihovny.cz E-zdroje
- Klíčová slova
- quantitative magnetic resonance imaging, reproducibility, spinal cord, spinal cord toolbox,
- MeSH
- lidé MeSH
- magnetická rezonanční tomografie * metody MeSH
- mícha * diagnostické zobrazování MeSH
- počítačové zpracování obrazu metody MeSH
- reprodukovatelnost výsledků MeSH
- software * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- přehledy MeSH
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).
Centre de Recherche du CHU Sainte Justine Université de Montréal Montreal QC Canada
Department of Neurology Faculty of Medicine and Dentistry Palacký University Olomouc Olomouc Czechia
Functional Neuroimaging Unit CRIUGM Université de Montréal Montreal QC Canada
Mila Quebec AI Institute Montreal QC Canada
NeuroPoly Lab Institute of Biomedical Engineering Polytechnique Montreal Montreal QC Canada
Zobrazit více v PubMed
Cohen-Adad J, Wheeler-Kingshott C. Quantitative MRI of the spinal cord. 2014:1–311.
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. PubMed PMC
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. PubMed PMC
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
Badhiwala JH, Ahuja CS, Akbar MA, et al. Degenerative cervical myelopathy — update and future directions. Nat Rev Neurol 2020; 16:108–124. PubMed
Cohen-Adad J. Microstructural imaging in the spinal cord and validation strategies. Neuroimage 2018; 182:169–183. PubMed
Cohen-Adad J, Alonso-Ortiz E, Abramovic M, et al. Generic acquisition protocol for quantitative MRI of the spinal cord. Nat Protoc 2021; 16:4611–4632. PubMed PMC
De Leener B, Lévy S, Dupont SM, et al. SCT: Spinal Cord Toolbox, an open-source software for processing spinal cord MRI data. Neuroimage 2017; 145(Pt A):24–43. PubMed
Song X, Li D, Qiu Z, et al. Correlation between EDSS scores and cervical spinal cord atrophy at 3T MRI in multiple sclerosis: A systematic review and meta-analysis. Mult Scler Relat Disord 2020; 37:101426. PubMed
Trolle C, Goldberg E, Linnman C. Spinal cord atrophy after spinal cord injury — A systematic review and meta-analysis. Neuroimage Clin 2023; 38:103372. PubMed PMC
Martin AR, Tetreault L, Nouri A, et al. Imaging and electrophysiology for degenerative cervical myelopathy., AO Spine RECODE-DCM Research Priority Number 9 Global Spine J 2022; 12(1_suppl):130S–146S. PubMed PMC
Hori M, Maekawa T, Kamiya K, et al. Advanced diffusion MR imaging for multiple sclerosis in the brain and spinal cord. Magn Reson Med Sci 2022; 21:58–70. PubMed PMC
Moccia M, Ruggieri S, Ianniello A, Toosy A, Pozzilli C, Ciccarelli O. Advances in spinal cord imaging in multiple sclerosis. Ther Adv Neurol Disord 2019; 12: 1756286419840593. PubMed PMC
David G, Vallotton K, Hupp M, Curt A, Freund P, Seif M. Extent of cord pathology in the lumbosacral enlargement in non-traumatic versus traumatic spinal cord injury. J Neurotrauma 2022; 39:639-650. PubMed
Karbasforoushan H, Cohen-Adad J, Dewald JPA. Brainstem and spinal cord MRI identifies altered sensorimotor pathways post-stroke. Nat Commun 2019; 10:3524. PubMed PMC
Rasoanandrianina H, Grapperon A-M, Taso M, et al. Region-specific impairment of the cervical spinal cord (SC) in amyotrophic lateral sclerosis: A preliminary study using SC templates and quantitative MRI (diffusion tensor imaging/inhomogeneous magnetization transfer). NMR Biomed 2017; 30:e3801. PubMed
Pisharady PK, Eberly LE, Cheong I, et al. Tract-specific analysis improves sensitivity of spinal cord diffusion MRI to cross-sectional and longitudinal changes in amyotrophic lateral sclerosis. Commun Biol 2020; 3:370. PubMed PMC
Hernandez ALCC, Rezende TJR, Martinez ARM, de Brito MR, França MC Jr. Tract-specific spinal cord diffusion tensor imaging in Friedreich’s ataxia. Mov Disord 2022; 37:354–364. PubMed
Hock A, Henning A, Boesiger P, Kollias SS. (1)H-MR spectroscopy in the human spinal cord. AJNR Am J Neuroradiol 2013; 34:1682–1689. PubMed PMC
Wyss PO, Hock A, Kollias S. The application of human spinal cord magnetic resonance spectroscopy to clinical studies: A review. Semin Ultrasound CT MR 2017; 38:153–162. PubMed
Kinany N, Pirondini E, Micera S, Van De Ville D. Spinal cord fMRI: A new window into the central nervous system. Neuroscientist 2023;29:715-731. PubMed PMC
Summers PE, Brooks JCW, Cohen-Adad J. Chapter 4.1 — spinal cord fMRI. Quantitative MRI of the spinal cord 2014:221–239.
Cohen-Adad J, Alonso-Ortiz E, Abramovic M, et al. Open-access quantitative MRI data of the spinal cord and reproducibility across participants, sites and manufacturers. Sci Data 2021; 8:219. PubMed PMC
Boudreau M, Karakuzu A, Boré A, et al. Longitudinal stability of brain and spinal cord quantitative MRI measures. NeuroLibre Reproducible Preprints 2023:18.
Oh J, Arbour N, Giuliani F, et al. The Canadian prospective cohort study to understand progression in multiple sclerosis (CanProCo): Rationale, aims, and study design. BMC Neurol 2021; 21:418. PubMed PMC
De Stefano N, Battaglini M, Pareto D, et al. MAGNIMS recommendations for harmonization of MRI data in MS multicenter studies. Neuroimage Clin 2022; 34:102972. PubMed PMC
Colamarino E, Lorusso M, Pichiorri F, et al. DiSCIoser: Unlocking recovery potential of arm sensorimotor functions after spinal cord injury by promoting activity-dependent brain plasticity by means of brain-computer interface technology: A randomized controlled trial to test efficacy. BMC Neurol 2023; 23:414. PubMed PMC
Muhammad F, Weber KA, Rohan M, et al. Linking cervical spinal cord white matter magnetization transfer ratio to NIH toolbox based analyses of motor function in degenerative cervical myelopathy. 2023
Georgiou-Karistianis N, Corben LA, Reetz K, et al. A natural history study to track brain and spinal cord changes in individuals with Friedreich’s ataxia: TRACK-FA study protocol. PLoS One 2022; 17:e0269649. PubMed PMC
Horak T, Horakova M, Svatkova A, et al. In vivo molecular signatures of cervical spinal cord pathology in degenerative compression. J Neurotrauma 2021; 38:2999–3010. PubMed PMC
Kinany N, Pirondini E, Mattera L, Martuzzi R, Micera S, Van De Ville D. Towards reliable spinal cord fMRI: assessment of common imaging protocols. Neuroimage 2022; 250:118964. PubMed
Labounek R, Valošek J, Horák T, et al. HARDI-ZOOMit protocol improves specificity to microstructural changes in presymptomatic myelopathy. Sci Rep 2020; 10:17529. PubMed PMC
Hori M, Hagiwara A, Fukunaga I, et al. Application of quantitative microstructural MR imaging with atlas-based analysis for the spinal cord in cervical spondylotic myelopathy. Sci Rep 2018; 8:5213. PubMed PMC
Valošek J, Labounek R, Horák T, et al. Diffusion magnetic resonance imaging reveals tract-specific microstructural correlates of electrophysiological impairments in non-myelopathic and myelopathic spinal cord compression. Eur J Neurol 2021; 28:3784–3797. PubMed PMC
Azad R, Rouhier L, Cohen-Adad J. Stacked hourglass network with a multi-level attention mechanism: Where to look for intervertebral disc labeling. Lect Notes Comput Sci 2021; 12966 LNCS:406–415.
Cohen-Adad J, Alonso-Ortiz E, Alley S, et al. Comparison of multicenter MRI protocols for visualizing the spinal cord gray matter. Magn Reson Med 2022; 88:849–859. PubMed
Schilling KG, Fadnavis S, Batson J, et al. Denoising of diffusion MRI in the cervical spinal cord — effects of denoising strategy and acquisition on intra-cord contrast, signal modeling, and feature conspicuity. Neuroimage 2023; 266:119826. PubMed PMC
Bautin P, Cohen-Adad J. Minimum detectable spinal cord atrophy with automatic segmentation: Investigations using an open-access dataset of healthy participants. Neuroimage Clin 2021; 32:102849. PubMed PMC
Beal E, Cohen-Adad J. Contrast-agnostic deep learning–based registration pipeline: Validation in spinal cord multimodal MRI data. Aperture Neuro 2023; 3:1–21.
Valošek J, Bédard S, Keřkovský M, Rohan T, Cohen-Adad J. A database of the healthy human spinal cord morphometry in the PAM50 template space. Imaging Neuroscience 2024; 2:1–15.
Bozorgpour A, Azad B, Azad R, Velichko Y, Bagci U, Merhof D. HCA-Net: Hierarchical context attention network for intervertebral disc semantic labeling. arXiv [csCV] 2023.
Blanc C, Shahrampour S, Mohamed FB, de Leener B. Combining PropSeg and a convolutional neural network for automatic spinal cord segmentation in pediatric populations and patients with spinal cord injury. Int J Imaging Syst Technol 2023; 33:1396-1405.
Bédard S, Bouthillier M, Cohen-Adad J. Pontomedullary junction as a reference for spinal cord cross-sectional area: Validation across neck positions. Sci Rep 2023; 13:13527. PubMed PMC
Prados F, Ashburner J, Blaiotta C, et al. Spinal cord grey matter segmentation challenge. Neuroimage 2017; 152:312–329. PubMed PMC
Hemmerling KJ, Hoggarth MA, Sandhu MS, Parrish TB, Bright MG. Spatial distribution of hand-grasp motor task activity in spinal cord functional magnetic resonance imaging. Hum Brain Mapp 2023; 44:5567–5581. PubMed PMC
Kaptan M, Horn U, Vannesjo SJ, et al. Reliability of resting-state functional connectivity in the human spinal cord: Assessing the impact of distinct noise sources. Neuroimage 2023; 275:120152. PubMed PMC
Miller KL, Alfaro-Almagro F, Bangerter NK, et al. Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat Neurosci 2016; 19:1523–1536. PubMed PMC
Bédard S, Cohen-Adad J. Automatic measure and normalization of spinal cord cross-sectional area using the pontomedullary junction. Front Neuroimaging. 2022; 1: 1031253. PubMed PMC
Halchenko Y, Meyer K, Poldrack B, et al. DataLad: distributed system for joint management of code, data, and their relationship. J Open Source Softw 2021; 6:3262.
Gorgolewski KJ, Auer T, Calhoun VD, et al. The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Sci Data 2016; 3:160044. PubMed PMC
Karakuzu A, Appelhoff S, Auer T, et al. qMRI-BIDS: An extension to the brain imaging data structure for quantitative magnetic resonance imaging data. Sci Data 2022; 9:517. PubMed PMC
Jenkinson M, Beckmann CF, Behrens TEJ, Woolrich MW, Smith SM. FSL. Neuroimage 2012; 62:782–790. PubMed
Ashburner J. SPM: A history. Neuroimage 2012; 62-248:791-800. PubMed PMC
De Leener B, Fonov VS, Collins DL, Callot V, Stikov N, Cohen-Adad J. PAM50: Unbiased multimodal template of the brainstem and spinal cord aligned with the ICBM152 space. Neuroimage 2018; 165:170–179. PubMed
Lévy S, Benhamou M, Naaman C, Rainville P, Callot V, Cohen-Adad J. White matter atlas of the human spinal cord with estimation of partial volume effect. Neuroimage 2015; 119:262–271. PubMed
Gros C, De Leener B, Badji A, et al. Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks. Neuroimage 2019; 184:901–915. PubMed PMC
Perone CS, Calabrese E, Cohen-Adad J. Spinal cord gray matter segmentation using deep dilated convolutions. Sci Rep 2018; 8:5966. PubMed PMC
De Leener B, Kadoury S, Cohen-Adad J. Robust, accurate and fast automatic segmentation of the spinal cord. Neuroimage 2014; 98:528–536. PubMed
Dupont SM, De Leener B, Taso M, et al. Fully-integrated framework for the segmentation and registration of the spinal cord white and gray matter. Neuroimage 2017; 150:358–372. PubMed
Gros C, Lemay A, Vincent O, et al. ivadomed: A medical imaging deep learning toolbox. J Open Source Softw 2021; 6:2868.
Lemay A, Gros C, Zhuo Z, et al. Automatic multiclass intramedullary spinal cord tumor segmentation on MRI with deep learning. Neuroimage Clin 2021; 31:102766. PubMed PMC
Laines Medina NJ, Gros C, Cohen-Adad J, Callot V, Le Troter A. 2D multi-class model for gray and white matter segmentation of the cervical spinal cord at 7T. arXiv [eessIV] 2021.
Benveniste P-L, Cohen-Adad J, Tournant A, Ni R. Model_seg_mouse-Sc_wm-gm_t1. 2023.
Cohen-Adad J, Tsagkas C, Pravatà E, Granziera C. ivadomed/model_seg_ms_mp2rage: r20230925. 2023.
Bédard S, Enamundram NK, Tsagkas C, et al. Towards contrast-agnostic soft segmentation of the spinal cord. arXiv [eessIV] 2023.
Karthik EN, Kerbrat A, Labauge P, et al. Segmentation of multiple sclerosis lesions across hospitals: Learn continually or train from scratch?. 2022.
Enamundram NK, Valosek J, Smith AC, et al. SCIseg: Automatic segmentation of T2-weighted hyperintense lesions in spinal cord injury. medRxiv 2024:2024.01.03.24300794.
Valosek J, Mathieu T, Schlienger R, Kowalczyk OS, Cohen-Adad J. Automatic Segmentation of the Spinal Cord Nerve Rootlets. arXiv:2402.00724.
Fonov V, Evans AC, Botteron K, Almli CR, McKinstry RC, Collins DL. Unbiased average age-appropriate atlases for pediatric studies. Neuroimage 2011; 54:313–327. PubMed PMC
Finsterbusch J, Sprenger C, Büchel C. Combined T2*-weighted measurements of the human brain and cervical spinal cord with a dynamic shim update. Neuroimage 2013; 79:153–161. PubMed
Kinany N, Khatibi A, Lungu O, et al. Decoding cerebro-spinal signatures of human behavior: Application to motor sequence learning. Neuroimage 2023; 275:120174. PubMed
Kerbrat A, Gros C, Badji A, et al. Multiple sclerosis lesions in motor tracts from brain to cervical cord: Spatial distribution and correlation with disability. Brain 2020; 143:2089–2105. PubMed PMC
Azzarito M, Kyathanahally SP, Balbastre Y, et al. Simultaneous voxel-wise analysis of brain and spinal cord morphometry and microstructure within the SPM framework. Hum Brain Mapp 2021; 42:220–232. PubMed PMC
Smith AC, Weber KA, 2nd, O’Dell DR, Parrish TB, Wasielewski M, Elliott JM. Lateral corticospinal tract damage correlates with motor output in incomplete spinal cord injury. Arch Phys Med Rehabil 2018; 99:660–666. PubMed PMC
Shahrampour S, De Leener B, Alizadeh M, et al. Atlas-based quantification of DTI measures in a typically developing pediatric spinal cord. AJNR Am J Neuroradiol 2021; 42:1727–1734. PubMed PMC
Eden D, Gros C, Badji A, et al. Spatial distribution of multiple sclerosis lesions in the cervical spinal cord. Brain 2019; 142:633–646. PubMed PMC
Scheuren PS, David G, Kramer JLK, et al. Combined neurophysiologic and neuroimaging approach to reveal the structure-function paradox in cervical myelopathy. Neurology 2021; 97:e1512–e1522. PubMed
Duval T, Saliani A, Nami H, et al. Axons morphometry in the human spinal cord. Neuroimage 2019; 185:119–128. PubMed
Le Troter A, Laines Medina NJ, Mchinda S, Cohen-Adad J, Callot V. AMU7T: A 3D qT1 and T2*w high-resolution in vivo template with refined white and gray matter parcellation dedicated to 7T spinal cord MR analyses. 2023.
Bosma RL, Stroman PW. Assessment of data acquisition parameters, and analysis techniques for noise reduction in spinal cord fMRI data. Magn Reson Imaging 2014; 32:473–481. PubMed
Taso M, Le Troter A, Sdika M, et al. Construction of an in vivo human spinal cord atlas based on high-resolution MR images at cervical and thoracic levels: Preliminary results. MAGMA 2014; 27:257–267. PubMed
Martin AR, De Leener B, Cohen-Adad J, et al. Can microstructural MRI detect subclinical tissue injury in subjects with asymptomatic cervical spinal cord compression? A prospective cohort study. BMJ Open 2018; 8:e019809. PubMed PMC
Horáková M, Horák T, Valošek J, et al. Semi-automated detection of cervical spinal cord compression with the Spinal Cord Toolbox. Quant Imaging Med Surg 2022; 12:2261–2279. PubMed PMC
Fehlings MG, Rao SC, Tator CH, et al. The optimal radiologic method for assessing spinal canal compromise and cord compression in patients with cervical spinal cord injury. Part II: Results of a multicenter study. Spine 1999; 24:605–613. PubMed
Bédard S. Normalizing maximum spinal cord compression for robust assessment of spinal cord injury. In Proceedings of the OHBM 2023 Annual Meeting. Montreal, Canada.
Smith AC, Albin SR, O’Dell DR, et al. Axial MRI biomarkers of spinal cord damage to predict future walking and motor function: A retrospective study. Spinal Cord 2021; 59:693–699. PubMed PMC
Fadnavis S, Batson J, Garyfallidis E. Patch2Self: Denoising diffusion MRI with self-supervised learning. Adv Neural Inf Process Syst 2020; 2020-Decem:1–11.
Avants BB, Tustison N, Song G. Advanced Normalization Tools (ANTS). 2011: 1–35.
Lavdas I, Glocker B, Rueckert D, Taylor SA, Aboagye EO, Rockall AG. Machine learning in whole-body MRI: Experiences and challenges from an applied study using multicentre data. Clin Radiol 2019; 74:346–356. PubMed
Yushkevich PA, Piven J, Hazlett HC, et al. User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 2006; 31:1116–1128. PubMed