Quantitative spinal cord (SC) magnetic resonance imaging (MRI) presents many challenges, including a lack of standardized imaging protocols. Here we present a prospectively harmonized quantitative MRI protocol, which we refer to as the spine generic protocol, for users of 3T MRI systems from the three main manufacturers: GE, Philips and Siemens. The protocol provides guidance for assessing SC macrostructural and microstructural integrity: T1-weighted and T2-weighted imaging for SC cross-sectional area computation, multi-echo gradient echo for gray matter cross-sectional area, and magnetization transfer and diffusion weighted imaging for assessing white matter microstructure. In a companion paper from the same authors, the spine generic protocol was used to acquire data across 42 centers in 260 healthy subjects. The key details of the spine generic protocol are also available in an open-access document that can be found at https://github.com/spine-generic/protocols. The protocol will serve as a starting point for researchers and clinicians implementing new SC imaging initiatives so that, in the future, inclusion of the SC in neuroimaging protocols will be more common. The protocol could be implemented by any trained MR technician or by a researcher/clinician familiar with MRI acquisition.
Bibliographical noteFunding Information:
We thank G. Moran and B. Schraa (Siemens Healthcare), S. Banerjee and N. Takei (GE Healthcare) for sharing proprietary information and for their help with setting up manufacturer-specific protocols, C. Hurst, A. Cyr, A. Boré and P. Bellec (Functional Neuroimaging Unit), C. Tremblay (Polytechnique Montreal), A. Melek and H. Benali (PERFORM center, Concordia University), I. Levesque (McGill University), C. Nguyen (University of Minnesota), Prof. S. Aoki (Juntendo University Hospital) for helping with data acquisitions, Compute Ontario (https:// computeontario.ca/) and Compute Canada (www.computecanada.ca) for providing the supercomputer infrastructure and all the volunteers who participated in the Spinal Cord MRI Public Database. This work was funded by the Canada Research Chair in Quantitative Magnetic Resonance Imaging (950-230815), the Canadian Institute of Health Research (CIHR FDN-143263), the Canada Foundation for Innovation (32454, 34824), the Fonds de Recherche du Québec–Santé (28826), the Fonds de Recherche du Québec–Nature et Technologies (2015-PR-182754), the Natural Sciences and Engineering Research Council of Canada (435897-2013), the Canada First Research Excellence Fund (IVADO and TransMedTech), the Quebec BioImaging Network (5886), Spinal Research (UK), Wings for Life (Austria, #169111) and Craig H. Neilsen Foundation (USA) for the INSPIRED project, the National Institutes of Health (NIH) through grants R00EB016689 (R.L.B.), R01EB027779 (R.L.B.), P41 EB027061 (CMRR) and P30 NS076408 (CMRR), the Instituto Investigación Carlos III (Spain, PI18/00823), the Czech Health Research Council grant no. NV18-04-00159, the Ministry of Health, Czech Republic–conceptual development of research organization (FNBr, 65269705), the National Imaging Facility and Queensland NMR Network (UQ), and SpinalCure Australia (M.J.R.), the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement no. 616905; European Union’s Horizon 2020 research and innovation programme under the grant agreement No 681094, and the Swiss State Secretariat for Education, Research and Innovation (SERI) under contract number 15.0137; BMBF (01EW1711A & B) in the framework of ERA-NET NEURON, the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 634541, the Engineering and Physical Sciences Research Council (R006032/1, M020533/1) and Rosetrees Trust (UK), UK Multiple Sclerosis Society (892/08, 77/2017), NIHR Biomedical Research Centres, UCLH, the Italian Ministry of Health Young Researcher Grant 2013 (GR-2013-02358177), the FISR Project ‘Tecnopolo di nanotecnologia e fotonica per la medicina di precisione’ (funded by MIUR/CNR, CUP B83B17000010001), TECNOMED project (funded by Regione Puglia, CUP B84I18000540002), Million Dollar Bike Ride from the University of Pennsylvania (MDBR-17-123-MPS), investigator-initiated PREdICT study at the Vall d’Hebron Institute of Oncology (Barcelona), funded by AstraZeneca and CRIS Cancer Foundation, the Wellcome Trust (UK) (203139/Z/16/Z), Systems, Technologies and Applications for Radiofrequency and Communications (STARaCOM), Swiss National Science Foundation (PCEFP3_181362/1) and the Max Planck Society and European Research Council (ERC StG 758974). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
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