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1. Comparison of multicenter <scp>MRI</scp> protocols for visualizing the spinal cord gray matter

Author

Julien Cohen‐Adad, Eva Alonso‐Ortiz, Stephanie Alley, Maria Marcella Lagana, Francesca Baglio, Signe Johanna Vannesjo, Haleh Karbasforoushan, Maryam Seif, Alan C. Seifert, Junqian Xu, Joo‐Won Kim, René Labounek, Lubomír Vojtíšek, Marek Dostál, Jan Valošek, Rebecca S. Samson, Francesco Grussu, Marco Battiston, Claudia A. M. Gandini Wheeler‐Kingshott, Marios C. Yiannakas, Guillaume Gilbert, Torben Schneider, Brian Johnson, and Ferran Prados

Subjects
Spinal Cord, Image Processing, Computer-Assisted, Cervical Cord, Humans, Multicenter Studies as Topic, Radiology, Nuclear Medicine and imaging, Gray Matter, Magnetic Resonance Imaging, White Matter
Abstract

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.

Published
2022
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2. SENSE EPI reconstruction with 2D phase error correction and channel-wise noise removal

Author

Elizabeth Powell, Torben Schneider, Marco Battiston, Francesco Grussu, Ahmed Toosy, Jonathan D. Clayden, Claudia A. M. Gandini Wheeler‐Kingshott, Institut Català de la Salut, [Powell E] Queen Square MS Centre, UCL Institute of Neurology, University College London, London, UK. Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK. [Schneider T] Philips Healthcare UK, Guildford, UK. [Battiston M, Toosy A] Queen Square MS Centre, UCL Institute of Neurology, University College London, London, UK. [Grussu F] Queen Square MS Centre, UCL Institute of Neurology, University College London, London, UK. Radiomics Group, Vall d’Hebron Institute of Oncology (VHIO), Barcelona, Spain. [Clayden JD] Developmental Imaging and Biophysics Section, Great Ormond Street Institute of Child Health, University College London, London, UK. [Gandini Wheeler-Kingshott CAM] Queen Square MS Centre, UCL Institute of Neurology, University College London, London, UK. Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy. Brain MRI 3T Center, IRCCS Mondino Foundation, Pavia, Italy, and Vall d'Hebron Barcelona Hospital Campus

Subjects
Echo-Planar Imaging, Phantoms, Imaging, Ciencias de la información::metodologías computacionales::procesamiento de imágenes asistido por ordenador [CIENCIA DE LA INFORMACIÓN], Equipment and Supplies::Phantoms, Imaging [ANALYTICAL, DIAGNOSTIC AND THERAPEUTIC TECHNIQUES, AND EQUIPMENT], Brain, Algorismes, Imatges - Processament - Tècniques digitals, equipos y suministros::modelos en imaginología [TÉCNICAS Y EQUIPOS ANALÍTICOS, DIAGNÓSTICOS Y TERAPÉUTICOS], Information Science::Computing Methodologies::Image Processing, Computer-Assisted [INFORMATION SCIENCE], técnicas de investigación::artefactos [TÉCNICAS Y EQUIPOS ANALÍTICOS, DIAGNÓSTICOS Y TERAPÉUTICOS], Image Processing, Computer-Assisted, Imatgeria mèdica, Humans, Radiology, Nuclear Medicine and imaging, Investigative Techniques::Artifacts [ANALYTICAL, DIAGNOSTIC AND THERAPEUTIC TECHNIQUES, AND EQUIPMENT], Artifacts, Algorithms
Abstract

Nyquist ghost; Denoising; Diffusion Fantasma de Nyquist; Eliminación de ruido; Difusión Fantasma de Nyquist; Eliminació de soroll; Difusió Purpose To develop a robust reconstruction pipeline for EPI data that enables 2D Nyquist phase error correction using sensitivity encoding without incurring major noise artifacts in low SNR data. Methods SENSE with 2D phase error correction (PEC-SENSE) was combined with channel-wise noise removal using Marcenko–Pastur principal component analysis (MPPCA) to simultaneously eliminate Nyquist ghost artifacts in EPI data and mitigate the noise amplification associated with phase correction using parallel imaging. The proposed pipeline (coined SPECTRE) was validated in phantom DW-EPI data using the accuracy and precision of diffusion metrics; ground truth values were obtained from data acquired with a spin echo readout. Results from the SPECTRE pipeline were compared against PEC-SENSE reconstructions with three alternate denoising strategies: (i) no denoising; (ii) denoising of magnitude data after image formation; (iii) denoising of complex data after image formation. SPECTRE was then tested using high -value (i.e., low SNR) diffusion data (up to s/mm ) in four healthy subjects. Results Noise amplification associated with phase error correction incurred a 23% bias in phantom mean diffusivity (MD) measurements. Phantom MD estimates using the SPECTRE pipeline were within 8% of the ground truth value. In healthy volunteers, the SPECTRE pipeline visibly corrected Nyquist ghost artifacts and reduced associated noise amplification in high -value data. Conclusion The proposed reconstruction pipeline is effective in correcting low SNR data, and improves the accuracy and precision of derived diffusion metrics. EPSRC-funded UCL Centre for Doctoral Training in Medical Imaging, Grant/Award Number: EP/L016478/1

Published
2022

3. Author Correction: Open-access quantitative MRI data of the spinal cord and reproducibility across participants, sites and manufacturers

Author

Ferran Prados, Alex Rovira, Ali Khatibi, Nico Papinutto, George Tackley, Maria Marcella Laganà, Akifumi Hagiwara, Kristin P. O’Grady, René Labounek, Nawal Kinany, Joo Won Kim, Loan Mattera, Christine S. Law, Seth A. Smith, Daniel Papp, Todd B. Parrish, Julien Cohen-Adad, Robert L. Barry, Nyoman D. Kurniawan, Sara Llufriu, Giancarlo Germani, Slawomir Kusmia, Miloš Keřkovský, Eloy Martinez-Heras, Matthew D. Budde, Richard G. Wise, Claudia A. M. Wheeler-Kingshott, P Wyss, Rebecca S. Samson, Karla R. Epperson, Y. Suzuki, Haleh Karbasforoushan, Issei Fukunaga, Igor Nestrasil, Virginie Callot, Maxime Descoteaux, Yazhuo Kong, Paul Kuntke, Kenneth A. Weber, Alexandru Foias, Marco Battiston, Hagen H. Kitzler, Charley Gros, Giovanni Savini, Michela Fratini, Guillaume Gilbert, Alan C. Seifert, Anna J.E. Combes, Carina Arneitz, Julien Doyon, Tobias Leutritz, Zachary A. Smith, Laura Barlow, James M. Joers, Kevin S. Epperson, Pierre-Gilles Henry, Markus Barth, Francesco Grussu, Deborah Pareto, Christophe Lenglet, Jürgen Finsterbusch, Shannon H. Kolind, Christian Büchel, Sean Mackey, Marios C. Yiannakas, Petr Kudlička, Mihael Abramovic, Benjamin De Leener, Elisabeth Solana, Anna Pichiecchio, Eva Alonso-Ortiz, Nikolaus Weiskopf, Kouhei Kamiya, Alexandra Tinnermann, Jan Valošek, Patrick Freund, Junqian Xu, Marek Dostál, Dimitri Van De Ville, Alex K. Smith, Federico Giove, Marc J. Ruitenberg, Falk Eippert, Nicole Atcheson, Paulo Loureiro de Sousa, Tomáš Horák, Masaaki Hori, Yaou Liu, Maryam Seif, Adam V. Dvorak, and Cornelia Laule

Subjects
Adult, Male, Statistics and Probability, Data descriptor, Science, MEDLINE, Spinal cord diseases, Neuroimaging, Spinal Cord Diseases, Imaging techniques, Library and Information Sciences, GeneralLiterature_MISCELLANEOUS, Education, Databases, Text mining, Image Processing, Computer-Assisted, Humans, Medicine, Author Correction, Reproducibility, business.industry, MT protocol, Reproducibility of Results, Spinal cord, Magnetic Resonance Imaging, Computer Science Applications, medicine.anatomical_structure, Spinal Cord, Female, Statistics, Probability and Uncertainty, business, Cartography, Information Systems
Abstract

In a companion paper by Cohen-Adad et al. we introduce the spine generic quantitative MRI protocol that provides valuable metrics for assessing spinal cord macrostructural and microstructural integrity. This protocol was used to acquire a single subject dataset across 19 centers and a multi-subject dataset across 42 centers (for a total of 260 participants), spanning the three main MRI manufacturers: GE, Philips and Siemens. Both datasets are publicly available via git-annex. Data were analysed using the Spinal Cord Toolbox to produce normative values as well as inter/intra-site and inter/intra-manufacturer statistics. Reproducibility for the spine generic protocol was high across sites and manufacturers, with an average inter-site coefficient of variation of less than 5% for all the metrics. Full documentation and results can be found at https://spine-generic.rtfd.io/ . The datasets and analysis pipeline will help pave the way towards accessible and reproducible quantitative MRI in the spinal cord.

Published
2021

4. Multi-parametric quantitative in vivo spinal cord MRI with unified signal readout and image denoising

Author

Daniel C. Alexander, Francesco Grussu, Julien Cohen-Adad, Timothy M. Shepherd, Jelle Veraart, Claudia A. M. Wheeler-Kingshott, Els Fieremans, Torben Schneider, Marco Battiston, and Dmitry S. Novikov

Subjects
Relaxometry, Computer science, Cognitive Neuroscience, Noise reduction, Physics::Medical Physics, Signal-To-Noise Ratio, Signal, Noise (electronics), 050105 experimental psychology, Article, Marchenko-Pastur PCA denoising, lcsh:RC321-571, 03 medical and health sciences, 0302 clinical medicine, Image Interpretation, Computer-Assisted, medicine, Humans, 0501 psychology and cognitive sciences, Computer Simulation, Signal readout, lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry, Myelin Sheath, Parametric statistics, Echo-planar imaging, Principal Component Analysis, Spinal cord, business.industry, Echo-Planar Imaging, 05 social sciences, Pattern recognition, Quantitative MRI, Image Enhancement, medicine.anatomical_structure, Diffusion Tensor Imaging, Neurology, Multi-parametric MRI, Artificial intelligence, business, 030217 neurology & neurosurgery, Algorithms, Diffusion MRI
Abstract

Multi-parametric quantitative MRI (qMRI) of the spinal cord is a promising non-invasive tool to probe early microstructural damage in neurological disorders. It is usually performed in vivo by combining acquisitions with multiple signal readouts, which exhibit different thermal noise levels, geometrical distortions and susceptibility to physiological noise. This ultimately hinders joint multi-contrast modelling and makes the geometric correspondence of parametric maps challenging. We propose an approach to overcome these limitations, by implementing state-of-the-art microstructural MRI of the spinal cord with a unified signal readout in vivo (i.e. with matched spatial encoding parameters across a range of imaging contrasts). We base our acquisition on single-shot echo planar imaging with reduced field-of-view, and obtain data from two different vendors (vendor 1: Philips Achieva; vendor 2: Siemens Prisma). Importantly, the unified acquisition allows us to compare signal and noise across contrasts, thus enabling overall quality enhancement via multi-contrast image denoising methods. As a proof-of-concept, here we provide a demonstration with one such method, known as Marchenko-Pastur (MP) Principal Component Analysis (PCA) denoising. MP-PCA is a singular value (SV) decomposition truncation approach that relies on redundant acquisitions, i.e. such that the number of measurements is large compared to the number of components that are maintained in the truncated SV decomposition. Here we used in vivo and synthetic data to test whether a unified readout enables more efficient MP-PCA denoising of less redundant acquisitions, since these can be denoised jointly with more redundant ones. We demonstrate that a unified readout provides robust multi-parametric maps, including diffusion and kurtosis tensors from diffusion MRI, myelin metrics from two-pool magnetisation transfer, and T1 and T2 from relaxometry. Moreover, we show that MP-PCA improves the quality of our multi-contrast acquisitions, since it reduces the coefficient of variation (i.e. variability) by up to 17% for mean kurtosis, 8% for bound pool fraction (BPF, myelin-sensitive), and 13% for T1, while enabling more efficient denoising of modalities limited in redundancy (e.g. relaxometry). In conclusion, multi-parametric spinal cord qMRI with unified readout is feasible and provides robust microstructural metrics with matched resolution and distortions, whose quality benefits from multi-contrast denoising methods such as MP-PCA.

Published
2020
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5. Translating pH-sensitive PROgressive saturation for QUantifying Exchange rates using Saturation Times (PRO-QUEST) MRI to a 3T clinical scanner

Author

Sachi Okuchi, Vincent Evans, David Atkinson, Torben Schneider, Marco Battiston, Eleni Demetriou, Claudia A. M. Wheeler-Kingshott, Carmen Tur, Sara Collorone, Mina Kim, Aaron Kujawa, and Xavier Golay

Subjects
Scanner, Chemistry, Significant difference, Brain, Grey matter, Hydrogen-Ion Concentration, Magnetic Resonance Imaging, White Matter, Hyperintensity, 030218 nuclear medicine & medical imaging, Lesion, White matter, 03 medical and health sciences, 0302 clinical medicine, Nuclear magnetic resonance, medicine.anatomical_structure, Relaxation rate, medicine, Humans, Radiology, Nuclear Medicine and imaging, medicine.symptom, Gray Matter, Saturation (magnetic), 030217 neurology & neurosurgery
Abstract

Purpose: To translate the recently developed PRO‐QUEST (Progressive saturation for quantifying exchange rates using saturation times) sequence from preclinical 9.4T to 3T clinical magnetic field strength. Methods: Numerical simulations were performed to define the optimal saturation flip angles for PRO‐QUEST saturation pulses at 3T and demonstrate the effect of a ∆T 2 error on the exchange rate (k ex) estimation at various field strengths. Exchange‐dependent relaxation rate (R ex) was measured for glutamate solutions in various pH, healthy volunteers and patients with multiple sclerosis (MS). Additionally, concentration‐independent ratiometric R ex maps were produced to evaluate regional signal variations across the brain of human volunteers. Results: The calculated R ex significantly correlates with pH in glutamate samples, however, k ex values are underestimated as compared to those previously obtained at 9.4T. In the ratiometric R ex map of healthy volunteers, no significant differences are found between grey matter, white matter, and basal ganglia. In patients with MS, white matter lesions are visible in single saturation power R ex maps whereas only a periventricular lesion is apparent in the ratiometric R ex map. Conclusion: We demonstrate that quantification of pH sensitive indices using PRO‐QUEST is feasible at 3T within clinically acceptable acquisition times. Our initial findings in patients with MS show that pH sensitive indices varied with the type of lesion examined whereas no significant difference was found in healthy volunteers between tissue types, suggesting that it would be worthwhile to apply PRO‐QUEST in a larger cohort of patients to better understand its distinct imaging features relative to conventional techniques.

Published
2019

6. Fast bound pool fraction mapping via steady‐state magnetization transfer saturation using single‐shot EPI

Author

Rebecca S. Samson, Marios C. Yiannakas, Claudia A. M. Wheeler-Kingshott, Torben Schneider, Francesco Grussu, Marco Battiston, Ferran Prados, and Floriana De Angelis

Subjects
Physics, Multiple Sclerosis, Echo-Planar Imaging, Healthy subjects, Single shot, Brain, Inversion recovery, 030218 nuclear medicine & medical imaging, Scan time, 03 medical and health sciences, 0302 clinical medicine, Transverse Relaxation Time, Image Interpretation, Computer-Assisted, Linear regression, Humans, Radiology, Nuclear Medicine and imaging, Magnetization transfer, Saturation (magnetic), Algorithm, Myelin Sheath, 030217 neurology & neurosurgery
Abstract

PURPOSE: To enable clinical applications of quantitative magnetization transfer (qMT) imaging by developing a fast method to map one of its fundamental model parameters, the bound pool fraction (BPF), in the human brain. THEORY AND METHODS: The theory of steady-state MT in the fast-exchange approximation is used to provide measurements of BPF, and bound pool transverse relaxation time ( T 2 B ). A sequence that allows sampling of the signal during steady-state MT saturation is used to perform BPF mapping with a 10-min-long fully echo planar imaging-based MRI protocol, including inversion recovery T1 mapping and B1 error mapping. The approach is applied in 6 healthy subjects and 1 multiple sclerosis patient, and validated against a single-slice full qMT reference acquisition. RESULTS: BPF measurements are in agreement with literature values using off-resonance MT, with average BPF of 0.114(0.100-0.128) in white matter and 0.068(0.054-0.085) in gray matter. Median voxel-wise percentage error compared with standard single slice qMT is 4.6%. Slope and intercept of linear regression between new and reference BPF are 0.83(0.81-0.85) and 0.013(0.11-0.16). Bland-Altman plot mean bias is 0.005. In the multiple sclerosis case, the BPF is sensitive to pathological changes in lesions. CONCLUSION: The method developed provides accurate BPF estimates and enables shorter scan time compared with currently available approaches, demonstrating the potential of bringing myelin sensitive measurement closer to the clinic.

Published
2019
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7. Multi‐echo quantitative susceptibility mapping: how to combine echoes for accuracy and precision at 3 Tesla

Author

Emma Biondetti, Anita Karsa, Francesco Grussu, Marco Battiston, Marios C. Yiannakas, David L. Thomas, Karin Shmueli, Institut Català de la Salut, [Biondetti E] Institute for Advanced Biomedical Technologies, Department of Neuroscience, Imaging and Clinical Sciences, 'D’Annunzio University' of Chieti-Pescara, Chieti, Italy. Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom. [Karsa A, Shmueli K] Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom. [Grussu F] NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom. Radiomics Group, Vall d’Hebron Institute of Oncology (VHIO), Barcelona, Spain. [Battiston M, Yiannakas MC] NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom. [Thomas DL] Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom. Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom, and Vall d'Hebron Barcelona Hospital Campus

Subjects
Brain Mapping, Diagnosis::Diagnostic Techniques and Procedures::Diagnostic Imaging::Neuroimaging::Functional Neuroimaging::Brain Mapping [ANALYTICAL, DIAGNOSTIC AND THERAPEUTIC TECHNIQUES, AND EQUIPMENT], Phantoms, Imaging, Brain, Otros calificadores::Otros calificadores::/diagnóstico por imagen [Otros calificadores], Cervell - Imatgeria, diagnóstico::técnicas y procedimientos diagnósticos::diagnóstico por imagen::neuroimágenes::neuroimágenes funcionales::mapeo encefálico [TÉCNICAS Y EQUIPOS ANALÍTICOS, DIAGNÓSTICOS Y TERAPÉUTICOS], Magnetic Resonance Imaging, White Matter, Nervous System::Central Nervous System::Brain [ANATOMY], sistema nervioso::sistema nervioso central::encéfalo [ANATOMÍA], Cartografia cerebral, Diagnosis::Diagnostic Techniques and Procedures::Diagnostic Imaging::Tomography::Magnetic Resonance Imaging [ANALYTICAL, DIAGNOSTIC AND THERAPEUTIC TECHNIQUES, AND EQUIPMENT], Image Processing, Computer-Assisted, Humans, Imatgeria per ressonància magnètica, Radiology, Nuclear Medicine and imaging, diagnóstico::técnicas y procedimientos diagnósticos::diagnóstico por imagen::tomografía::imagen por resonancia magnética [TÉCNICAS Y EQUIPOS ANALÍTICOS, DIAGNÓSTICOS Y TERAPÉUTICOS], Other subheadings::Other subheadings::/diagnostic imaging [Other subheadings]
Abstract

MRI; Multi-echo QSM; Quantitative susceptibility mapping Imágen por resonancia magnética; QSM de ecos múltiples; Mapeo cuantitativo de susceptibilidad Imatge per ressonància magnètica; QSM de ressò múltiple; Mapeig quantitatiu de susceptibilitat Purpose To compare different multi-echo combination methods for MRI QSM. Given the current lack of consensus, we aimed to elucidate how to optimally combine multi-echo gradient-recalled echo signal phase information, either before or after applying Laplacian-base methods (LBMs) for phase unwrapping or background field removal. Methods Multi-echo gradient-recalled echo data were simulated in a numerical head phantom, and multi-echo gradient-recalled echo images were acquired at 3 Tesla in 10 healthy volunteers. To enable image-based estimation of gradient-recalled echo signal noise, 5 volunteers were scanned twice in the same session without repositioning. Five QSM processing pipelines were designed: 1 applied nonlinear phase fitting over TEs before LBMs; 2 applied LBMs to the TE-dependent phase and then combined multiple TEs via either TE-weighted or SNR-weighted averaging; and 2 calculated TE-dependent susceptibility maps via either multi-step or single-step QSM and then combined multiple TEs via magnitude-weighted averaging. Results from different pipelines were compared using visual inspection; summary statistics of susceptibility in deep gray matter, white matter, and venous regions; phase noise maps (error propagation theory); and, in the healthy volunteers, regional fixed bias analysis (Bland–Altman) and regional differences between the means (nonparametric tests). Results Nonlinearly fitting the multi-echo phase over TEs before applying LBMs provided the highest regional accuracy of and the lowest phase noise propagation compared to averaging the LBM-processed TE-dependent phase. This result was especially pertinent in high-susceptibility venous regions. Conclusion For multi-echo QSM, we recommend combining the signal phase by nonlinear fitting before applying LBMs. Supported by the UK Engineering and Physical Sciences Research Council (EPSRC), award number: 1489882 (e.b.); by the EPSRC-funded UCL Centre for Doctoral Training in Medical Imaging, grant EP/L016478/1 (a.k.), and the Department of Health's National Institute for Health Research funded Biomedical Research Centre at University College London Hospitals (a.k.); by the UCL Leonard Wolfson Experimental Neurology Centre, grant PR/ylr/18575 (d.l.t) The Queen Square MS Centre, where part of the MRI scans for this work were performed, is supported by grants from the UK MS Society and by the National Institute for Health Research University College London Hospitals Biomedical Research Centre (UCLH/BRC). F. Grussu was supported by PREdICT, a study at the Vall d'Hebron Institute of Oncology in Barcelona funded by AstraZeneca (f.g.), and funding from the postdoctoral fellowships program Beatriu de Pinós (2020 BP 00117), funded by the Secretary of Universities and Research, Government of Catalonia (f.g.)

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8. Generic acquisition protocol for quantitative MRI of the spinal cord

Author

René Labounek, Nawal Kinany, Michela Fratini, Laura Barlow, Markus Barth, P Wyss, Marco Battiston, Sean Mackey, Nyoman D. Kurniawan, Deborah Pareto, Kenneth A. Weber, Guillaume Gilbert, Robert L. Barry, Anna Pichiecchio, Eva Alonso-Ortiz, Matthew D. Budde, Mihael Abramovic, Alexandra Tinnermann, Giancarlo Germani, Dimitri Van De Ville, Falk Eippert, Anna J.E. Combes, Igor Nestrasil, Marios C. Yiannakas, Haleh Karbasforoushan, Julien Doyon, Cornelia Laule, Nicole Atcheson, Ali Khatibi, Nico Papinutto, Karla R. Epperson, Marek Dostál, Yazhuo Kong, Loan Mattera, Charley Gros, Paulo Loureiro de Sousa, Pierre-Gilles Henry, Alan C. Seifert, Richard G. Wise, Issei Fukunaga, Alexandru Foias, Shannon H. Kolind, Todd B. Parrish, Akifumi Hagiwara, Daniel Papp, Carina Arneitz, Virginie Callot, Christian Büchel, Maria Marcella Laganà, Tobias Leutritz, Slawomir Kusmia, Ferran Prados, Kevin S. Epperson, Marc J. Ruitenberg, Nikolaus Weiskopf, Kouhei Kamiya, Benjamin De Leener, Adam V. Dvorak, Giovanni Savini, Christine S. Law, Petr Kudlička, Paul Kuntke, Julien Cohen-Adad, Alex Rovira, Jan Valošek, Patrick Freund, George Tackley, Zachary A. Smith, Eloy Martinez-Heras, Maxime Descoteaux, Yaou Liu, Miloš Keřkovský, Joo Won Kim, Kristin P. O’Grady, Elisabeth Solana, Rebecca S. Samson, Junqian Xu, Alex K. Smith, Federico Giove, Maryam Seif, Claudia A. M. Wheeler-Kingshott, Tomáš Horák, James M. Joers, Francesco Grussu, Christophe Lenglet, Jürgen Finsterbusch, Sara Llufriu, Hagen H. Kitzler, Masaaki Hori, Yuichi Suzuki, Seth A. Smith, École Polytechnique de Montréal (EPM), Université du Québec à Montréal = University of Québec in Montréal (UQAM), Mila - Quebec AI Institute, Swiss Paraplegic Research = Schweizer Paraplegiker-Stiftung, University of Queensland [Brisbane], University of British Columbia (UBC), Massachusetts General Hospital [Boston], Harvard Medical School [Boston] (HMS), Massachusetts Institute of Technology (MIT), University College of London [London] (UCL), Universitaetsklinikum Hamburg-Eppendorf = University Medical Center Hamburg-Eppendorf [Hamburg] (UKE), Medical College of Wisconsin [Milwaukee] (MCW), Centre de résonance magnétique biologique et médicale (CRMBM), Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)-Centre National de la Recherche Scientifique (CNRS), Centre d'Exploration Métabolique par Résonance Magnétique [Hôpital de la Timone - AP-HM] (CEMEREM), Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)-Centre National de la Recherche Scientifique (CNRS)- Hôpital de la Timone [CHU - APHM] (TIMONE), Vanderbilt University Medical Center [Nashville], Vanderbilt University [Nashville], CHU Sainte Justine [Montréal], Université de Sherbrooke (UdeS), Centre d'Imagerie Moléculaire de Sherbrooke, Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie (ICube), École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et nanosciences d'Alsace (FMNGE), Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA), Masaryk University and University Hospital Brno, McGill University = Université McGill [Montréal, Canada], Max Planck Institute for Human Cognitive and Brain Sciences [Leipzig] (IMPNSC), Max-Planck-Gesellschaft, Stanford University School of Medicine [CA, USA], Universität Zürich [Zürich] = University of Zurich (UZH), IRCCS Santa Lucia Foundation, Juntendo University School of Medicine [Tokyo, Japan], University of Pavia, IRCCS Mondino Foundation, Philips Healthcare [Markham], Museo Storico della Fisica e Centro di Studi e Ricerche 'Enrico Fermi', Roma, Vall d'Hebron University Hospital [Barcelona], Center for Magnetic Resonance Research [Minneapolis] (CMRR), University of Minnesota Medical School, University of Minnesota System-University of Minnesota System, Central European Institute of Technology [Brno] (CEITEC MU), Brno University of Technology [Brno] (BUT), Toho University Omori Medical Center [Tokyo], The University of Tokyo (UTokyo), Northwestern University Feinberg School of Medicine, Stanford University, University of Birmingham [Birmingham], Icahn School of Medicine at Mount Sinai [New York] (MSSM), Ecole Polytechnique Fédérale de Lausanne (EPFL), University of Geneva [Switzerland], Technische Universität Dresden = Dresden University of Technology (TU Dresden), Chinese Academy of Sciences [Beijing] (CAS), University of Chinese Academy of Sciences [Beijing] (UCAS), University of Oxford [Oxford], University of Southern Queensland (USQ), Cardiff University, Epilepsy Society [Buckinghamshire] (MRI Unit), Departments of Ophthalmology and Pediatrics, University of Minnesota, Minneapolis, MN, University of Minnesota [Twin Cities] (UMN), University Hospital Olomouc [Czech Republic], Capital University of Medical Sciences [Beijing] (CUMS), Universitat de Barcelona (UB), University of California [San Francisco] (UCSF), University of California, Feinberg School of Medicine, Northwestern University [Evanston], Universitat Oberta de Catalunya [Barcelona] (UOC), University of Oklahoma Health Sciences Center (OUHSC), Palacky University Olomouc, Leipzig University, University 'G. d'Annunzio' of Chieti-Pescara [Chieti and Pescara, Italy], Centre d'Exploration Métabolique par Résonance Magnétique [Hôpital de la Timone - APHM] (CEMEREM), Hôpital de la Timone [CHU - APHM] (TIMONE)-Centre de résonance magnétique biologique et médicale (CRMBM), Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)-Centre National de la Recherche Scientifique (CNRS), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Les Hôpitaux Universitaires de Strasbourg (HUS)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et Nanosciences Grand-Est (MNGE), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), Masaryk University [Brno] (MUNI), Università degli Studi di Pavia = University of Pavia (UNIPV), Enrico Fermi Center for Study and Research | Museo Storico della Fisica e Centro Studi e Ricerche Enrico Fermi, Université de Genève = University of Geneva (UNIGE), University of Oxford, University of California [San Francisco] (UC San Francisco), University of California (UC), Assistance Publique - Hôpitaux de Marseille (APHM)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Assistance Publique - Hôpitaux de Marseille (APHM)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-Assistance Publique - Hôpitaux de Marseille (APHM)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)- Hôpital de la Timone [CHU - APHM] (TIMONE), Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS)-École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Réseau nanophotonique et optique, Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Matériaux et nanosciences d'Alsace (FMNGE), and Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Adult, Male, medicine.medical_specialty, Computer science, Neuroimaging, Article, General Biochemistry, Genetics and Molecular Biology, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, Image Processing, Computer-Assisted, medicine, Humans, NeuroPoly, Medical physics, health care economics and organizations, Protocol (science), medicine.diagnostic_test, technology, industry, and agriculture, Healthy subjects, Magnetic resonance imaging, Spinal cord, Magnetic Resonance Imaging, White matter microstructure, humanities, 3. Good health, Acquisition Protocol, medicine.anatomical_structure, Spinal Cord, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], 030217 neurology & neurosurgery, Diffusion MRI
Abstract

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.

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