Comparison of multicenter MRI protocols for visualizing the spinal cord gray matter.
MRI
acquisition
gray matter
image quality
protocol
spinal cord
Journal
Magnetic resonance in medicine
ISSN: 1522-2594
Titre abrégé: Magn Reson Med
Pays: United States
ID NLM: 8505245
Informations de publication
Date de publication:
08 2022
08 2022
Historique:
revised:
03
03
2022
received:
28
11
2021
accepted:
07
03
2022
pubmed:
28
4
2022
medline:
24
6
2022
entrez:
27
4
2022
Statut:
ppublish
Résumé
Spinal cord gray-matter imaging is valuable for a number of applications, but remains challenging. The purpose of this work was to compare various MRI protocols at 1.5 T, 3 T, and 7 T for visualizing the gray matter. In vivo data of the cervical spinal cord were collected from nine different imaging centers. Data processing consisted of automatically segmenting the spinal cord and its gray matter and co-registering back-to-back scans. We computed the SNR using two methods (SNR_single using a single scan and SNR_diff using the difference between back-to-back scans) and the white/gray matter contrast-to-noise ratio per unit time. Synthetic phantom data were generated to evaluate the metrics performance. Experienced radiologists qualitatively scored the images. We ran the same processing on an open-access multicenter data set of the spinal cord MRI (N = 267 participants). Qualitative assessments indicated comparable image quality for 3T and 7T scans. Spatial resolution was higher at higher field strength, and image quality at 1.5 T was found to be moderate to low. The proposed quantitative metrics were found to be robust to underlying changes to the SNR and contrast; however, the SNR_single method lacked accuracy when there were excessive partial-volume effects. We propose quality assessment criteria and metrics for gray-matter visualization and apply them to different protocols. The proposed criteria and metrics, the analyzed protocols, and our open-source code can serve as a benchmark for future optimization of spinal cord gray-matter imaging protocols.
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
849-859Subventions
Organisme : CIHR
ID : FDN-143263
Pays : Canada
Organisme : NINDS NIH HHS
ID : K01 NS105160
Pays : United States
Organisme : Department of Health
ID : R&D 03/10/RAG0449
Pays : United Kingdom
Informations de copyright
© 2022 International Society for Magnetic Resonance in Medicine.
Références
Paquin M-Ê, El Mendili MM, Gros C, Dupont SM, Cohen-Adad J, Pradat P-F. Spinal cord gray matter atrophy in amyotrophic lateral sclerosis. AJNR Am J Neuroradiol. 2018;39:184-192.
Jutzeler CR, Huber E, Callaghan MF, et al. Association of pain and CNS structural changes after spinal cord injury. Sci Rep. 2016;6:18534.
Calabrese M, Favaretto A, Martini V, Gallo P. Grey matter lesions in MS: from histology to clinical implications. Prion. 2013;7:20-27.
Agosta F, Pagani E, Caputo D, Filippi M. Associations between cervical cord gray matter damage and disability in patients with multiple sclerosis. Arch Neurol. 2007;64:1302-1305.
Kornelsen J, Mackey S. Potential clinical applications for spinal functional MRI. Curr Pain Headache Rep. 2007;11:165-170.
Wheeler-Kingshott CA, Stroman PW, Schwab JM, et al. The current state-of-the-art of spinal cord imaging: applications. Neuroimage. 2014;84:1082-1093.
By S, Smith AK, Dethrage LM, et al. Quantifying the impact of underlying measurement error on cervical spinal cord diffusion tensor imaging at 3T. J Magn Reson Imaging. 2016;44:1608-1618.
Massire A, Rasoanandrianina H, Taso M, et al. Feasibility of single-shot multi-level multi-angle diffusion tensor imaging of the human cervical spinal cord at 7T. Magn Reson Med. 2018;80:947-957.
Labounek R, Valošek J, Horák T, et al. HARDI-ZOOMit protocol improves specificity to microstructural changes in presymptomatic myelopathy. Sci Rep. 2020;10:17529.
Cohen-Adad J, Wheeler-Kingshott C. Quantitative MRI of the Spinal Cord. Cambridge, Massachusetts: Academic Press; 2014.
Verma T, Cohen-Adad J. Effect of respiration on the B0 field in the human spinal cord at 3T. Magn Reson Med. 2014;72:1629-1636.
Vannesjo SJ, Miller KL, Clare S, Tracey I. Spatiotemporal characterization of breathing-induced B0 field fluctuations in the cervical spinal cord at 7T. Neuroimage. 2018;167:191-202.
Papinutto N, Henry RG. Evaluation of intra- and Interscanner reliability of MRI protocols for spinal cord gray matter and total cross-sectional area measurements. J Magn Reson Imaging. 2019;49:1078-1090.
Papinutto N, Schlaeger R, Panara V, et al. Age, gender and normalization covariates for spinal cord gray matter and total cross-sectional areas at cervical and thoracic levels: a 2D phase sensitive inversion recovery imaging study. PLoS One. 2015;10:e0118576.
Papinutto N, Datta E, Zhu AH, et al. Multisite feasibility study of spinal cord gray matter and total cord areas measurements on 2D phase sensitive inversion recovery images. Proceedings of the 24th Annual Meeting ISMRM. Singapore; 2016; 698-708.
Papinutto N, Schlaeger R, Panara V, et al. 2D phase-sensitive inversion recovery imaging to measure in vivo spinal cord gray and white matter areas in clinically feasible acquisition times. J Magn Reson Imaging. 2015;42:698-708.
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.
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.
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;145:24-43.
Dietrich O, Raya JG, Reeder SB, Reiser MF, Schoenberg SO. Measurement of signal-to-noise ratios in MR images: influence of multichannel coils, parallel imaging, and reconstruction filters. J Magn Reson Imaging. 2007;26:375-385.
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.
Perone CS, Calabrese E, Cohen-Adad J. Spinal cord gray matter segmentation using deep dilated convolutions. Sci Rep. 2018;8:5966.
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.
Levy 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.
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.
Cohen-Adad J, Polimeni JR, Helmer KG, et al. T2* mapping and B0 orientation-dependence at 7T reveal cyto- and myeloarchitecture organization of the human cortex. Neuroimage. 2012;60:1006-1014.
Van de Moortele P-F, Ugurbil K, Lehericy S. Is T2* always the optimum echo time in BOLD fMRI? Challenging a common concept with a new contrast to noise ratio BOLD model. Proceedings of the 16th Annual Meeting of ISMRM; ISMRM; 2008; 133-139.