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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. [PMID: 37598229 PMCID: PMC10439961 DOI: 10.1038/s41598-023-40731-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 08/16/2023] [Indexed: 08/21/2023] Open
Abstract
Spinal cord cross-sectional area (CSA) is an important MRI biomarker to assess spinal cord atrophy in various neurodegenerative and traumatic spinal cord diseases. However, the conventional method of computing CSA based on vertebral levels is inherently flawed, as the prediction of spinal levels from vertebral levels lacks reliability, leading to considerable variability in CSA measurements. Computing CSA from an intrinsic neuroanatomical reference, the pontomedullary junction (PMJ), has been proposed in previous work to overcome limitations associated with using a vertebral reference. However, the validation of this alternative approach, along with its variability across and within participants under variable neck extensions, remains unexplored. The goal of this study was to determine if the variability of CSA across neck flexions/extensions is reduced when using the PMJ, compared to vertebral levels. Ten participants underwent a 3T MRI T2w isotropic scan at 0.6 mm3 for 3 neck positions: extension, neutral and flexion. Spinal cord segmentation, vertebral labeling, PMJ labeling, and CSA were computed automatically while spinal segments were labeled manually. Mean coefficient of variation for CSA across neck positions was 3.99 ± 2.96% for the PMJ method vs. 4.02 ± 3.01% for manual spinal segment method vs. 4.46 ± 3.10% for the disc method. These differences were not statistically significant. The PMJ method was slightly more reliable than the disc-based method to compute CSA at specific spinal segments, although the difference was not statistically significant. This suggests that the PMJ can serve as a valuable alternative and reliable method for estimating CSA when a disc-based approach is challenging or not feasible, such as in cases involving fused discs in individuals with spinal cord injuries.
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Affiliation(s)
- Sandrine Bédard
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada.
| | - Maxime Bouthillier
- Centre Hospitalier de l'Université de Montréal, University of Montreal, Montreal, QC, Canada
| | - Julien Cohen-Adad
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
- Functional Neuroimaging Unit, CRIUGM, University of Montreal, Montreal, QC, Canada
- Mila - Quebec AI Institute, Montreal, QC, Canada
- Centre de Recherche du CHU Sainte-Justine, Université de Montréal, Montréal, QC, Canada
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2
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Nigri A, Dalla Bella E, Ferraro S, Medina Carrion JP, Demichelis G, Bersano E, Consonni M, Bischof A, Stanziano M, Palermo S, Lauria G, Bruzzone MG, Papinutto N. Cervical spinal cord atrophy in amyotrophic lateral sclerosis across disease stages. Ann Clin Transl Neurol 2023; 10:213-224. [PMID: 36599092 PMCID: PMC9930423 DOI: 10.1002/acn3.51712] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 11/11/2022] [Accepted: 11/21/2022] [Indexed: 01/06/2023] Open
Abstract
OBJECTIVE Spinal cord degeneration is a hallmark of amyotrophic lateral sclerosis. The assessment of gray matter and white matter cervical spinal cord atrophy across clinical stages defined using the King's staging system could advance the understanding of amyotrophic lateral sclerosis progression. METHODS We assessed the in vivo spatial pattern of gray and white matter atrophy along cervical spinal cord (C2 to C6 segments) using 2D phase-sensitive inversion recovery imaging in a cohort of 44 amyotrophic lateral sclerosis patients, evaluating its change across the King's stages and the correlation with disability scored by the amyotrophic lateral sclerosis functional rating scale revised (ALSFRS-R) and disease duration. A mathematical model inferring the potential onset of cervical gray matter atrophy was developed. RESULTS In amyotrophic lateral sclerosis patients at King's stage 1, significant cervical spinal cord alterations were mainly identified in gray matter, whereas they involved both gray and white matter in patients at King's stage ≥ 2. Gray and white matter areas correlated with clinical disability at all cervical segments. C3-C4 level was the segment showing early gray matter atrophy starting about 7 to 20 months before symptom onset according to our model. INTERPRETATION Our findings suggest that cervical spinal cord atrophy spreads from gray to white matter across King's stages in amyotrophic lateral sclerosis, making spinal cord magnetic resonance imaging an in vivo assessment tool to measure the progression of the disease.
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Affiliation(s)
- Anna Nigri
- Neuroradiology UnitFondazione IRCCS Istituto Neurologico Carlo BestaMilanItaly
| | - Eleonora Dalla Bella
- 3rd Neurology Unit and Motor Neuron Disease CentreFondazione IRCCS Istituto Neurologico Carlo BestaMilanItaly
| | - Stefania Ferraro
- Neuroradiology UnitFondazione IRCCS Istituto Neurologico Carlo BestaMilanItaly,School of Life Science and Technology, MOE Key Laboratory for NeuroinformationUniversity of Electronic Science and Technology of ChinaChengduChina
| | | | - Greta Demichelis
- Neuroradiology UnitFondazione IRCCS Istituto Neurologico Carlo BestaMilanItaly
| | - Enrica Bersano
- 3rd Neurology Unit and Motor Neuron Disease CentreFondazione IRCCS Istituto Neurologico Carlo BestaMilanItaly,Department of Medical Biotechnology and Translational MedicineUniversity of MilanMilanItaly
| | - Monica Consonni
- 3rd Neurology Unit and Motor Neuron Disease CentreFondazione IRCCS Istituto Neurologico Carlo BestaMilanItaly
| | - Antje Bischof
- Weill Institute for Neurosciences, Department of NeurologyUniversity of CaliforniaSan FranciscoCaliforniaUSA,Department of Neurology with Institute for Translational NeurologyUniversity Hospital MünsterMünsterGermany
| | - Mario Stanziano
- Neuroradiology UnitFondazione IRCCS Istituto Neurologico Carlo BestaMilanItaly,ALS Centre, “Rita Levi Montalcini” Department of NeuroscienceUniversity of TurinTurinItaly
| | - Sara Palermo
- Neuroradiology UnitFondazione IRCCS Istituto Neurologico Carlo BestaMilanItaly
| | - Giuseppe Lauria
- 3rd Neurology Unit and Motor Neuron Disease CentreFondazione IRCCS Istituto Neurologico Carlo BestaMilanItaly,Department of Medical Biotechnology and Translational MedicineUniversity of MilanMilanItaly
| | | | - Nico Papinutto
- Weill Institute for Neurosciences, Department of NeurologyUniversity of CaliforniaSan FranciscoCaliforniaUSA
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Bédard S, Cohen-Adad J. Automatic measure and normalization of spinal cord cross-sectional area using the pontomedullary junction. FRONTIERS IN NEUROIMAGING 2022; 1:1031253. [PMID: 37555172 PMCID: PMC10406309 DOI: 10.3389/fnimg.2022.1031253] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 10/04/2022] [Indexed: 08/10/2023]
Abstract
Spinal cord cross-sectional area (CSA) is a relevant biomarker to assess spinal cord atrophy in neurodegenerative diseases. However, the considerable inter-subject variability among healthy participants currently limits its usage. Previous studies explored factors contributing to the variability, yet the normalization models required manual intervention and used vertebral levels as a reference, which is an imprecise prediction of the spinal levels. In this study we implemented a method to measure CSA automatically from a spatial reference based on the central nervous system (the pontomedullary junction, PMJ), we investigated factors to explain variability, and developed normalization strategies on a large cohort (N = 804). Following automatic spinal cord segmentation, vertebral labeling and PMJ labeling, the spinal cord CSA was computed on T1w MRI scans from the UK Biobank database. The CSA was computed using two methods. For the first method, the CSA was computed at the level of the C2-C3 intervertebral disc. For the second method, the CSA was computed at 64 mm caudally from the PMJ, this distance corresponding to the average distance between the PMJ and the C2-C3 disc across all participants. The effect of various demographic and anatomical factors was explored, and a stepwise regression found significant predictors; the coefficients of the best fit model were used to normalize CSA. CSA measured at C2-C3 disc and using the PMJ differed significantly (paired t-test, p-value = 0.0002). The best normalization model included thalamus, brain volume, sex and the interaction between brain volume and sex. The coefficient of variation went down for PMJ CSA from 10.09 (without normalization) to 8.59%, a reduction of 14.85%. For CSA at C2-C3, it went down from 9.96 to 8.42%, a reduction of 15.13 %. This study introduces an end-to-end automatic pipeline to measure and normalize cord CSA from a neurological reference. This approach requires further validation to assess atrophy in longitudinal studies. The inter-subject variability of CSA can be partly accounted for by demographics and anatomical factors.
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Affiliation(s)
- Sandrine Bédard
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
| | - Julien Cohen-Adad
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
- Functional Neuroimaging Unit, Centre de recherche de l'Institut universitaire de gériatrie de Montréal (CRIUGM), University of Montreal, Montreal, QC, Canada
- Mila - Quebec AI Institute, Montreal, QC, Canada
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4
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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. [PMID: 36306717 PMCID: PMC9668663 DOI: 10.1016/j.nicl.2022.103244] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 09/02/2022] [Accepted: 10/19/2022] [Indexed: 11/11/2022]
Abstract
Spinal cord magnetic resonance imaging (MRI) has a central role in multiple sclerosis (MS) clinical practice for diagnosis and disease monitoring. Advanced MRI sequences capable of visualizing and quantifying tissue macro- and microstructure and reflecting different pathological disease processes have been used in MS research; however, the spinal cord remains under-explored, partly due to technical obstacles inherent to imaging this structure. We propose that the study of the spinal cord merits equal ambition in overcoming technical challenges, and that there is much information to be exploited to make valuable contributions to our understanding of MS. We present a narrative review on the latest progress in advanced spinal cord MRI in MS, covering in the first part structural, functional, metabolic and vascular imaging methods. We focus on recent studies of MS and those making significant technical steps, noting the challenges that remain to be addressed and what stands to be gained from such advances. Throughout we also refer to other works that presend more in-depth review on specific themes. In the second part, we present several topics that, in our view, hold particular potential. The need for better imaging of gray matter is discussed. We stress the importance of developing imaging beyond the cervical spinal cord, and explore the use of ultra-high field MRI. Finally, some recommendations are given for future research, from study design to newer developments in analysis, and the need for harmonization of sequences and methods within the field. This review is aimed at researchers and clinicians with an interest in gaining an overview of the current state of advanced MRI research in this field and what is primed to be the future of spinal cord imaging in MS research.
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Affiliation(s)
- Anna J E Combes
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, 1161 21st Avenue South, Medical Center North, AA-1105, Nashville, TN 37232-2310, United States; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Medical Center North, 1161 21st Ave. South, Nashville, TN 37232, United States.
| | - Margareta A Clarke
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, 1161 21st Avenue South, Medical Center North, AA-1105, Nashville, TN 37232-2310, United States
| | - Kristin P O'Grady
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, 1161 21st Avenue South, Medical Center North, AA-1105, Nashville, TN 37232-2310, United States; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Medical Center North, 1161 21st Ave. South, Nashville, TN 37232, United States; Department of Biomedical Engineering, Vanderbilt University, 2301 Vanderbilt Place, PMB 351826, Nashville, TN 37235-1826, United States
| | - Kurt G Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, 1161 21st Avenue South, Medical Center North, AA-1105, Nashville, TN 37232-2310, United States; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Medical Center North, 1161 21st Ave. South, Nashville, TN 37232, United States
| | - Seth A Smith
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, 1161 21st Avenue South, Medical Center North, AA-1105, Nashville, TN 37232-2310, United States; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Medical Center North, 1161 21st Ave. South, Nashville, TN 37232, United States; Department of Biomedical Engineering, Vanderbilt University, 2301 Vanderbilt Place, PMB 351826, Nashville, TN 37235-1826, United States
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5
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Optimized multi-echo gradient-echo magnetic resonance imaging for gray and white matter segmentation in the lumbosacral cord at 3 T. Sci Rep 2022; 12:16498. [PMID: 36192560 PMCID: PMC9530158 DOI: 10.1038/s41598-022-20395-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Accepted: 09/13/2022] [Indexed: 11/09/2022] Open
Abstract
Atrophy in the spinal cord (SC), gray (GM) and white matter (WM) is typically measured in-vivo by image segmentation on multi-echo gradient-echo magnetic resonance images. The aim of this study was to establish an acquisition and analysis protocol for optimal SC and GM segmentation in the lumbosacral cord at 3 T. Ten healthy volunteers underwent imaging of the lumbosacral cord using a 3D spoiled multi-echo gradient-echo sequence (Siemens FLASH, with 5 echoes and 8 repetitions) on a Siemens Prisma 3 T scanner. Optimal numbers of successive echoes and signal averages were investigated comparing signal-to-noise (SNR) and contrast-to-noise ratio (CNR) values as well as qualitative ratings for segmentability by experts. The combination of 5 successive echoes yielded the highest CNR between WM and cerebrospinal fluid and the highest rating for SC segmentability. The combination of 3 and 4 successive echoes yielded the highest CNR between GM and WM and the highest rating for GM segmentability in the lumbosacral enlargement and conus medullaris, respectively. For segmenting the SC and GM in the same image, we suggest combining 3 successive echoes. For SC or GM segmentation only, we recommend combining 5 or 3 successive echoes, respectively. Six signal averages yielded good contrast for reliable SC and GM segmentation in all subjects. Clinical applications could benefit from these recommendations as they allow for accurate SC and GM segmentation in the lumbosacral cord.
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6
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De Stefano N, Battaglini M, Pareto D, Cortese R, Zhang J, Oesingmann N, Prados F, Rocca MA, Valsasina P, Vrenken H, Gandini Wheeler-Kingshott CAM, Filippi M, Barkhof F, Rovira À. MAGNIMS recommendations for harmonization of MRI data in MS multicenter studies. Neuroimage Clin 2022; 34:102972. [PMID: 35245791 PMCID: PMC8892169 DOI: 10.1016/j.nicl.2022.102972] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 02/22/2022] [Accepted: 02/23/2022] [Indexed: 11/24/2022]
Abstract
Sharing data from cooperative studies is essential to develop new biomarkers in MS. Differences in MRI acquisition, analysis, storage represent a substantial constraint. We review the state of the art and developments in the harmonization of MRI. We provide recommendations to harmonize large MRI datasets in the MS field.
There is an increasing need of sharing harmonized data from large, cooperative studies as this is essential to develop new diagnostic and prognostic biomarkers. In the field of multiple sclerosis (MS), the issue has become of paramount importance due to the need to translate into the clinical setting some of the most recent MRI achievements. However, differences in MRI acquisition parameters, image analysis and data storage across sites, with their potential bias, represent a substantial constraint. This review focuses on the state of the art, recent technical advances, and desirable future developments of the harmonization of acquisition, analysis and storage of large-scale multicentre MRI data of MS cohorts. Huge efforts are currently being made to achieve all the requirements needed to provide harmonized MRI datasets in the MS field, as proper management of large imaging datasets is one of our greatest opportunities and challenges in the coming years. Recommendations based on these achievements will be provided here. Despite the advances that have been made, the complexity of these tasks requires further research by specialized academical centres, with dedicated technical and human resources. Such collective efforts involving different professional figures are of crucial importance to offer to MS patients a personalised management while minimizing consumption of resources.
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Affiliation(s)
- Nicola De Stefano
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy.
| | - Marco Battaglini
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Deborah Pareto
- Section of Neuroradiology, Department of Radiology, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Rosa Cortese
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Jian Zhang
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | | | - Ferran Prados
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; Center for Medical Imaging Computing, Medical Physics and Biomedical Engineering, UCL, London, WC1V 6LJ, United Kingdom; e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
| | - Maria A Rocca
- Neuroimaging Research Unit, Division of Neuroscience, and Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy; Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy
| | - Paola Valsasina
- Neuroimaging Research Unit, Division of Neuroscience, and Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Hugo Vrenken
- Amsterdam Neuroscience, MS Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Amsterdam, Netherlands
| | - Claudia A M Gandini Wheeler-Kingshott
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; Brain MRI 3T Research Center, C. Mondino National Neurological Institute, Pavia, Italy; Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, and Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy; Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy; Neurorehabilitation Unit, and Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy; Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Frederik Barkhof
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; Center for Medical Imaging Computing, Medical Physics and Biomedical Engineering, UCL, London, WC1V 6LJ, United Kingdom; Amsterdam Neuroscience, MS Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Amsterdam, Netherlands
| | - Àlex Rovira
- Section of Neuroradiology, Department of Radiology, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
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Cohen‐Adad J, Alonso‐Ortiz E, Alley S, Lagana MM, Baglio F, Vannesjo SJ, Karbasforoushan H, Seif M, Seifert AC, Xu J, Kim J, Labounek R, Vojtíšek L, Dostál M, Valošek J, Samson RS, Grussu F, Battiston M, Gandini Wheeler‐Kingshott CAM, Yiannakas MC, Gilbert G, Schneider T, Johnson B, Prados F. Comparison of multicenter
MRI
protocols for visualizing the spinal cord gray matter. Magn Reson Med 2022; 88:849-859. [DOI: 10.1002/mrm.29249] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Revised: 03/03/2022] [Accepted: 03/07/2022] [Indexed: 12/28/2022]
Affiliation(s)
- Julien Cohen‐Adad
- NeuroPoly Lab Institute of Biomedical Engineering, Polytechnique Montreal Montreal Canada
- Functional Neuroimaging Unit, CRIUGM University of Montreal Montreal Canada
- Mila ‐ Quebec AI Institute Montreal Canada
| | - Eva Alonso‐Ortiz
- NeuroPoly Lab Institute of Biomedical Engineering, Polytechnique Montreal Montreal Canada
| | - Stephanie Alley
- NeuroPoly Lab Institute of Biomedical Engineering, Polytechnique Montreal Montreal Canada
| | | | | | - Signe Johanna Vannesjo
- Wellcome Center for Integrative Neuroimaging, FMRIB University of Oxford, John Radcliffe Hospital Oxford UK
- Department of Physics Norwegian University of Science and Technology Trondheim Norway
| | - Haleh Karbasforoushan
- Interdepartmental Neuroscience Program Northwestern University School of Medicine Chicago IL USA
- Department of Psychiatry and Behavioral Sciences, School of Medicine Stanford University Stanford CA USA
| | - Maryam Seif
- Spinal Cord Injury Center, Balgrist University Hospital University of Zurich Zurich Switzerland
- Department of Neurophysics Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany
| | - Alan C. Seifert
- Biomedical Engineering and Imaging Institute, Department of Radiology, Graduate School of Biomedical Sciences Icahn School of Medicine at Mount Sinai New York NY USA
| | - Junqian Xu
- Biomedical Engineering and Imaging Institute, Department of Radiology, Graduate School of Biomedical Sciences Icahn School of Medicine at Mount Sinai New York NY USA
| | - Joo‐Won Kim
- Biomedical Engineering and Imaging Institute, Department of Radiology, Graduate School of Biomedical Sciences Icahn School of Medicine at Mount Sinai New York NY USA
| | - René Labounek
- Departments of Neurology and Biomedical Engineering University Hospital Olomouc Olomouc Czech Republic
- Division of Clinical Behavioral Neuroscience, Department of Pediatrics Masonic Institute for the Developing Brain, University of Minnesota Minneapolis MN USA
| | - Lubomír Vojtíšek
- Central European Institute of Technology Masaryk University Brno Czech Republic
| | - Marek Dostál
- Department of Radiology and Nuclear Medicine University Hospital Brno Brno Czech Republic
| | - Jan Valošek
- Departments of Neurology and Biomedical Engineering University Hospital Olomouc Olomouc Czech Republic
| | - Rebecca S. Samson
- Queen Square MS Centre, UCL Institute of Neurology, Faculty of Brain Sciences University College London London UK
| | - Francesco Grussu
- Queen Square MS Centre, UCL Institute of Neurology, Faculty of Brain Sciences University College London London UK
- Radiomics Group, Vall d'Hebron Institute of Oncology Vall d'Hebron Barcelona Hospital Campus Barcelona Spain
| | - Marco Battiston
- Queen Square MS Centre, UCL Institute of Neurology, Faculty of Brain Sciences University College London London UK
| | - Claudia A. M. Gandini Wheeler‐Kingshott
- Queen Square MS Centre, UCL Institute of Neurology, Faculty of Brain Sciences University College London London UK
- Department of Brain and Behavioral Sciences University of Pavia Pavia Italy
- Brain MRI 3T Research Center C. Mondino National Neurological Institute Pavia Italy
| | - Marios C. Yiannakas
- Queen Square MS Centre, UCL Institute of Neurology, Faculty of Brain Sciences University College London London UK
| | | | | | - Brian Johnson
- MR Clinical Development, Philips North America Gainesville FL USA
| | - Ferran Prados
- Queen Square MS Centre, UCL Institute of Neurology, Faculty of Brain Sciences University College London London UK
- e‐Health Center, Universitat Oberta de Catalunya Barcelona Spain
- Center for Medical Imaging Computing, Medical Physics and Biomedical Engineering, University College London London UK
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Simultaneous assessment of regional distributions of atrophy across the neuraxis in MS patients. Neuroimage Clin 2022; 34:102985. [PMID: 35316667 PMCID: PMC8938332 DOI: 10.1016/j.nicl.2022.102985] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 03/05/2022] [Accepted: 03/07/2022] [Indexed: 11/22/2022]
Abstract
BACKGROUND The ability to assess brain and cord atrophy simultaneously would improve the efficiency of MRI to track disease evolution. OBJECTIVE To test a promising tool to simultaneously map the regional distribution of atrophy in multiple sclerosis (MS) patients across the brain and cord. METHODS Voxel-based morphometry combined with a statistical parametric mapping probabilistic brain-spinal cord (SPM-BSC) template was applied to standard T1-weighted magnetic resonance imaging (MRI) scans covering the brain and cervical cord from 37 MS patients and 20 healthy controls (HC). We also measured the cord area at C2-C3 with a semi-automatic segmentation method using (i) the same T1-weighted acquisitions used for the new voxel-based analysis and (ii) dedicated spinal cord phase sensitive inversion recovery (PSIR) acquisitions. Cervical cord findings derived from the three approaches were compared to each other and the goodness to fit to clinical scores was assessed by regression analyses. RESULTS The SPM-BSC approach revealed a severity-dependent pattern of atrophy across the cervical cord and thalamus in MS patients when compared to HCs. The magnitude of cord atrophy was confirmed by the semi-automatic extraction approach at C2-C3 using both standard brain T1-weighted and advanced cord dedicated acquisitions. Associations between atrophy of cord and thalamus with disability and cognition were demonstrated. CONCLUSION Atrophy in the brain and cervical cord of MS patients can be identified simultaneously and rapidly at the voxel-level. The SPM-BSC approach yields similar results as available standard processing tools with the added advantage of performing the analysis simultaneously and faster.
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Wendebourg MJ, Weigel M, Richter L, Gocheva V, Hafner P, Orsini AL, Crepulja V, Schmidt S, Huck A, Oechtering J, Blatow M, Haas T, Granziera C, Kappos L, Cattin P, Bieri O, Fischer D, Schlaeger R. Spinal Cord Gray Matter Atrophy is associated with functional decline in Post-Polio Syndrome. Eur J Neurol 2022; 29:1435-1445. [PMID: 35102676 PMCID: PMC9310958 DOI: 10.1111/ene.15261] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 12/30/2021] [Accepted: 01/20/2022] [Indexed: 11/29/2022]
Abstract
Objective To determine if patients with post‐polio syndrome (PPS) show spinal cord gray matter (SCGM) atrophy and to assess associations between SCGM atrophy, muscle strength and patient‐reported functional decline. Methods Twenty patients diagnosed with PPS (March of Dimes criteria) and 20 age‐ and sex‐matched healthy controls (HC) underwent 3T axial 2D‐rAMIRA magnetic resonance imaging at the intervertebral disc levels C2/C3–C6/C7, T9/T10 and the lumbar enlargement level (Tmax) (0.5 × 0.5 mm2 in‐plane resolution). SCGM areas were segmented manually by two independent raters. Muscle strength, self‐reported fatigue, depression and pain measures were assessed. Results Post‐polio syndrome patients showed significantly and preferentially reduced SCGM areas at C2/C3 (p = 0.048), C3/C4 (p = 0.001), C4/C5 (p < 0.001), C5/C6 (p = 0.004) and Tmax (p = 0.041) compared to HC. SCGM areas were significantly associated with muscle strength in corresponding myotomes even after adjustment for fatigue, pain and depression. SCGM areaTmax together with age and sex explained 68% of ankle dorsiflexion strength variance. No associations were found with age at or time since infection. Patients reporting PPS‐related decline in arm function showed significant cervical SCGM atrophy compared to stable patients adjusted for initial disease severity. Conclusions Patients with PPS show significant SCGM atrophy that correlates with muscle strength and is associated with PPS‐related functional decline. Our findings suggest a secondary neurodegenerative process underlying SCGM atrophy in PPS that is not explained by aging or residua of the initial infection alone. Confirmation by longitudinal studies is needed. The described imaging methodology is promising for developing novel imaging surrogates for SCGM diseases.
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Affiliation(s)
- Maria Janina Wendebourg
- Neurology Clinic and Policlinic, Department of Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland.,ThINK Basel, Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | - Matthias Weigel
- Neurology Clinic and Policlinic, Department of Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland.,ThINK Basel, Department of Biomedical Engineering, University of Basel, Basel, Switzerland.,Division of Radiological Physics, Department of Radiology, University Hospital Basel, Basel, Switzerland.,Department of Biomedical Engineering, University of Basel, Basel, Switzerland.,MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Laura Richter
- Neurology Clinic and Policlinic, Department of Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Vanya Gocheva
- Division of Paediatric Neurology, University of Basel Children's Hospital, Basel, Switzerland
| | - Patricia Hafner
- Division of Paediatric Neurology, University of Basel Children's Hospital, Basel, Switzerland
| | - Anna-Lena Orsini
- Division of Paediatric Neurology, University of Basel Children's Hospital, Basel, Switzerland
| | - Valentina Crepulja
- Neurology Clinic and Policlinic, Department of Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland.,ThINK Basel, Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | - Simone Schmidt
- Division of Paediatric Neurology, University of Basel Children's Hospital, Basel, Switzerland
| | - Antal Huck
- Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | - Johanna Oechtering
- Neurology Clinic and Policlinic, Department of Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Maria Blatow
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Tanja Haas
- Division of Radiological Physics, Department of Radiology, University Hospital Basel, Basel, Switzerland.,Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | - Cristina Granziera
- Neurology Clinic and Policlinic, Department of Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland.,ThINK Basel, Department of Biomedical Engineering, University of Basel, Basel, Switzerland.,MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Ludwig Kappos
- Neurology Clinic and Policlinic, Department of Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland.,ThINK Basel, Department of Biomedical Engineering, University of Basel, Basel, Switzerland.,MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Philippe Cattin
- Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | - Oliver Bieri
- Division of Radiological Physics, Department of Radiology, University Hospital Basel, Basel, Switzerland.,Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | - Dirk Fischer
- Division of Paediatric Neurology, University of Basel Children's Hospital, Basel, Switzerland
| | - Regina Schlaeger
- Neurology Clinic and Policlinic, Department of Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland.,ThINK Basel, Department of Biomedical Engineering, University of Basel, Basel, Switzerland.,MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
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10
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Cohen-Adad J, Alonso-Ortiz E, Abramovic M, Arneitz C, Atcheson N, Barlow L, Barry RL, Barth M, Battiston M, Büchel C, Budde M, Callot V, Combes AJE, De Leener B, Descoteaux M, de Sousa PL, Dostál M, Doyon J, Dvorak A, Eippert F, Epperson KR, Epperson KS, Freund P, Finsterbusch J, Foias A, Fratini M, Fukunaga I, Wheeler-Kingshott CAMG, Germani G, Gilbert G, Giove F, Gros C, Grussu F, Hagiwara A, Henry PG, Horák T, Hori M, Joers J, Kamiya K, Karbasforoushan H, Keřkovský M, Khatibi A, Kim JW, Kinany N, Kitzler H, Kolind S, Kong Y, Kudlička P, Kuntke P, Kurniawan ND, Kusmia S, Labounek R, Laganà MM, Laule C, Law CS, Lenglet C, Leutritz T, Liu Y, Llufriu S, Mackey S, Martinez-Heras E, Mattera L, Nestrasil I, O'Grady KP, Papinutto N, Papp D, Pareto D, Parrish TB, Pichiecchio A, Prados F, Rovira À, Ruitenberg MJ, Samson RS, Savini G, Seif M, Seifert AC, Smith AK, Smith SA, Smith ZA, Solana E, Suzuki Y, Tackley G, Tinnermann A, Valošek J, Van De Ville D, Yiannakas MC, Weber KA, Weiskopf N, Wise RG, Wyss PO, Xu J. Generic acquisition protocol for quantitative MRI of the spinal cord. Nat Protoc 2021; 16:4611-4632. [PMID: 34400839 PMCID: PMC8811488 DOI: 10.1038/s41596-021-00588-0] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Accepted: 06/10/2021] [Indexed: 02/08/2023]
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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Affiliation(s)
- Julien Cohen-Adad
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, Quebec, Canada.
- Functional Neuroimaging Unit, CRIUGM, University of Montreal, Montreal, Quebec, Canada.
- Mila-Quebec AI Institute, Montreal, Quebec, Canada.
| | - Eva Alonso-Ortiz
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, Quebec, Canada
| | - Mihael Abramovic
- Department of Radiology, Swiss Paraplegic Centre, Nottwil, Switzerland
| | - Carina Arneitz
- Department of Radiology, Swiss Paraplegic Centre, Nottwil, Switzerland
| | - Nicole Atcheson
- Centre for Advanced Imaging, The University of Queensland, Brisbane, Queensland, Australia
| | - Laura Barlow
- Department of Radiology, University of British Columbia, Vancouver, British Columbia, Canada
| | - Robert L Barry
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
- Harvard-Massachusetts Institute of Technology Health Sciences & Technology, Cambridge, MA, USA
| | - Markus Barth
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Queensland, Australia
| | - Marco Battiston
- NMR Research Unit, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Christian Büchel
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Matthew Budde
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Virginie Callot
- Aix-Marseille Univ, CNRS, CRMBM, Marseille, France
- APHM, Hopital Universitaire Timone, CEMEREM, Marseille, France
| | - Anna J E Combes
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Benjamin De Leener
- Department of Computer and Software Engineering, Polytechnique Montreal, Montreal, Quebec, Canada
- CHU Sainte-Justine Research Centre, Montreal, Quebec, Canada
| | - Maxime Descoteaux
- Centre de Recherche CHUS, CIMS, Sherbrooke, Quebec, Canada
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science department, Université de Sherbrooke, Sherbrooke, Quebec, Canada
| | | | - Marek Dostál
- UHB - University Hospital Brno and Masaryk University, Department of Radiology and Nuclear Medicine, Brno, Czech Republic
| | - Julien Doyon
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Adam Dvorak
- Department of Physics and Astronomy, University of British Columbia, Vancouver, British Columbia, Canada
| | - Falk Eippert
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Karla R Epperson
- Richard M. Lucas Center, Stanford University School of Medicine, Stanford, CA, USA
| | - Kevin S Epperson
- Richard M. Lucas Center, Stanford University School of Medicine, Stanford, CA, USA
| | - Patrick Freund
- Spinal Cord Injury Center Balgrist, University of Zurich, Zurich, Switzerland
| | - Jürgen Finsterbusch
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Alexandru Foias
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, Quebec, Canada
| | - Michela Fratini
- Institute of Nanotechnology, CNR, Rome, Italy
- IRCCS Santa Lucia Foundation, Rome, Italy
| | - Issei Fukunaga
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Claudia A M Gandini Wheeler-Kingshott
- NMR Research Unit, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
- Brain MRI 3T Research Centre, IRCCS Mondino Foundation, Pavia, Italy
| | - Giancarlo Germani
- Brain MRI 3T Research Centre, IRCCS Mondino Foundation, Pavia, Italy
| | | | - Federico Giove
- IRCCS Santa Lucia Foundation, Rome, Italy
- CREF - Museo storico della fisica e Centro studi e ricerche Enrico Fermi, Rome, Italy
| | - Charley Gros
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, Quebec, Canada
- Centre for Advanced Imaging, The University of Queensland, Brisbane, Queensland, Australia
| | - Francesco Grussu
- NMR Research Unit, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Akifumi Hagiwara
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Pierre-Gilles Henry
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA
| | - Tomáš Horák
- Multimodal and functional imaging laboratory, Central European Institute of Technology (CEITEC), Brno, Czech Republic
| | - Masaaki Hori
- Department of Radiology, Toho University Omori Medical Center, Tokyo, Japan
| | - James Joers
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA
| | - Kouhei Kamiya
- Department of Radiology, the University of Tokyo, Tokyo, Japan
| | - Haleh Karbasforoushan
- Interdepartmental Neuroscience Program, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford University, Stanford, CA, USA
| | - Miloš Keřkovský
- UHB - University Hospital Brno and Masaryk University, Department of Radiology and Nuclear Medicine, Brno, Czech Republic
| | - Ali Khatibi
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
- Centre of Precision Rehabilitation for Spinal Pain (CPR Spine), School of Sport, Exercise and Rehabilitation Sciences, College of Life and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, UK
| | - Joo-Won Kim
- BioMedical Engineering and Imaging Institute (BMEII), Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Nawal Kinany
- Institute of Bioengineering/Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Hagen Kitzler
- Institute of Diagnostic and Interventional Neuroradiology, Carl Gustav Carus University Hospital, Technische Universität Dresden, Dresden, Germany
| | - Shannon Kolind
- Department of Radiology, University of British Columbia, Vancouver, British Columbia, Canada
- Department of Physics and Astronomy, University of British Columbia, Vancouver, British Columbia, Canada
- Department of Medicine (Neurology), University of British Columbia, Vancouver, British Columbia, Canada
| | - Yazhuo Kong
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
- Wellcome Centre For Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Petr Kudlička
- Multimodal and functional imaging laboratory, Central European Institute of Technology (CEITEC), Brno, Czech Republic
| | - Paul Kuntke
- Institute of Diagnostic and Interventional Neuroradiology, Carl Gustav Carus University Hospital, Technische Universität Dresden, Dresden, Germany
| | - Nyoman D Kurniawan
- Centre for Advanced Imaging, The University of Queensland, Brisbane, Queensland, Australia
| | - Slawomir Kusmia
- CUBRIC, Cardiff University, Wales, UK
- Centre for Medical Image Computing (CMIC), Medical Physics and Biomedical Engineering Department, University College London, London, UK
- Epilepsy Society MRI Unit, Chalfont St Peter, UK
| | - René Labounek
- Division of Clinical Behavioral Neuroscience, Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
- Departments of Neurology and Biomedical Engineering, University Hospital Olomouc, Olomouc, Czech Republic
| | | | - Cornelia Laule
- Departments of Radiology, Pathology & Laboratory Medicine, Physics & Astronomy; International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, British Columbia, Canada
| | - Christine S Law
- Division of Pain Medicine, Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Christophe Lenglet
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA
| | - Tobias Leutritz
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Yaou Liu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Tiantan Image Research Center, China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Sara Llufriu
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain
| | - Sean Mackey
- Division of Pain Medicine, Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Eloy Martinez-Heras
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain
| | - Loan Mattera
- Fondation Campus Biotech Genève, Geneva, Switzerland
| | - Igor Nestrasil
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA
- Division of Clinical Behavioral Neuroscience, Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Kristin P O'Grady
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Radiology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Nico Papinutto
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Daniel Papp
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, Quebec, Canada
- Wellcome Centre For Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Deborah Pareto
- Neuroradiology Section, Vall d'Hebron University Hospital, Barcelona, Spain
| | - Todd B Parrish
- Interdepartmental Neuroscience Program, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Anna Pichiecchio
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
- Brain MRI 3T Research Centre, IRCCS Mondino Foundation, Pavia, Italy
| | - Ferran Prados
- NMR Research Unit, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Centre for Medical Image Computing (CMIC), Medical Physics and Biomedical Engineering Department, University College London, London, UK
- E-health Centre, Universitat Oberta de Catalunya, Barcelona, Spain
| | - Àlex Rovira
- Neuroradiology Section, Vall d'Hebron University Hospital, Barcelona, Spain
| | - Marc J Ruitenberg
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia
| | - Rebecca S Samson
- NMR Research Unit, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Giovanni Savini
- Brain MRI 3T Research Centre, IRCCS Mondino Foundation, Pavia, Italy
| | - Maryam Seif
- Spinal Cord Injury Center Balgrist, University of Zurich, Zurich, Switzerland
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Alan C Seifert
- BioMedical Engineering and Imaging Institute (BMEII), Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alex K Smith
- Wellcome Centre For Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Seth A Smith
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Radiology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Zachary A Smith
- University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Elisabeth Solana
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain
| | - Yuichi Suzuki
- Department of Radiology, the University of Tokyo, Tokyo, Japan
| | | | - Alexandra Tinnermann
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Jan Valošek
- Department of Neurology, Faculty of Medicine and Dentistry, Palacký University and University Hospital Olomouc, Olomouc, Czech Republic
| | - Dimitri Van De Ville
- Institute of Bioengineering/Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Marios C Yiannakas
- NMR Research Unit, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Kenneth A Weber
- Division of Pain Medicine, Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Nikolaus Weiskopf
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig University, Leipzig, Germany
| | - Richard G Wise
- CUBRIC, Cardiff University, Wales, UK
- Institute for Advanced Biomedical Technologies, Department of Neuroscience, Imaging and Clinical Sciences, "G. D'Annunzio University" of Chieti-Pescara, Chieti, Italy
| | - Patrik O Wyss
- Department of Radiology, Swiss Paraplegic Centre, Nottwil, Switzerland
| | - Junqian Xu
- BioMedical Engineering and Imaging Institute (BMEII), Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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11
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Lukas C, Bellenberg B, Prados F, Valsasina P, Parmar K, Brouwer I, Pareto D, Rovira À, Sastre-Garriga J, Gandini Wheeler-Kingshott CAM, Kappos L, Rocca MA, Filippi M, Yiannakas M, Barkhof F, Vrenken H. Quantification of Cervical Cord Cross-Sectional Area: Which Acquisition, Vertebra Level, and Analysis Software? A Multicenter Repeatability Study on a Traveling Healthy Volunteer. Front Neurol 2021; 12:693333. [PMID: 34421797 PMCID: PMC8371197 DOI: 10.3389/fneur.2021.693333] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2021] [Accepted: 06/14/2021] [Indexed: 11/15/2022] Open
Abstract
Background: Considerable spinal cord (SC) atrophy occurs in multiple sclerosis (MS). While MRI-based techniques for SC cross-sectional area (CSA) quantification have improved over time, there is no common agreement on whether to measure at single vertebral levels or across larger regions and whether upper SC CSA can be reliably measured from brain images. Aim: To compare in a multicenter setting three CSA measurement methods in terms of repeatability at different anatomical levels. To analyze the agreement between measurements performed on the cervical cord and on brain MRI. Method: One healthy volunteer was scanned three times on the same day in six sites (three scanner vendors) using a 3T MRI protocol including sagittal 3D T1-weighted imaging of the brain (covering the upper cervical cord) and of the SC. Images were analyzed using two semiautomated methods [NeuroQLab (NQL) and the Active Surface Model (ASM)] and the fully automated Spinal Cord Toolbox (SCT) on different vertebral levels (C1-C2; C2/3) on SC and brain images and the entire cervical cord (C1-C7) on SC images only. Results: CSA estimates were significantly smaller using SCT compared to NQL and ASM (p < 0.001), regardless of the cord level. Inter-scanner repeatability was best in C1-C7: coefficients of variation for NQL, ASM, and SCT: 0.4, 0.6, and 1.0%, respectively. CSAs estimated in brain MRI were slightly lower than in SC MRI (all p ≤ 0.006 at the C1-C2 level). Despite protocol harmonization between the centers with regard to image resolution and use of high-contrast 3D T1-weighted sequences, the variability of CSA was partly scanner dependent probably due to differences in scanner geometry, coil design, and details of the MRI parameter settings. Conclusion: For CSA quantification, dedicated isotropic SC MRI should be acquired, which yielded best repeatability in the entire cervical cord. In the upper part of the cervical cord, use of brain MRI scans entailed only a minor loss of CSA repeatability compared to SC MRI. Due to systematic differences between scanners and the CSA quantification software, both should be kept constant within a study. The MRI dataset of this study is available publicly to test new analysis approaches.
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Affiliation(s)
- Carsten Lukas
- Institute of Neuroradiology, St. Josef Hospital, Ruhr-University Bochum, Bochum, Germany
| | - Barbara Bellenberg
- Institute of Neuroradiology, St. Josef Hospital, Ruhr-University Bochum, Bochum, Germany
| | - Ferran Prados
- Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing (CMIC), University College London, London, United Kingdom
- Queen Square Multiple Sclerosis Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
- e-Health Centre, Universitat Oberta de Catalunya, Barcelona, Spain
| | - Paola Valsasina
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Katrin Parmar
- Neurological Clinic and Policlinic, Department of Medicine, University Hospital Basel, Basel, Switzerland
| | - Iman Brouwer
- Department of Radiology and Nuclear Medicine, Multiple Sclerosis Center Amsterdam, Amsterdam Neuroscience Amsterdam University Medical Centers (UMC), Vrije Universiteit Medical Center (VUmc), Amsterdam, Netherlands
| | - Deborah Pareto
- Section of Neuroradiology, Department of Radiology, Hospital Universitari Vall d'Hebron, Barcelona, Spain
| | - Àlex Rovira
- Section of Neuroradiology, Department of Radiology, Hospital Universitari Vall d'Hebron, Barcelona, Spain
| | - Jaume Sastre-Garriga
- Department of Neurology–Neuroimmunology, Multiple Sclerosis Center of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Barcelona, Spain
| | - Claudia A. M. Gandini Wheeler-Kingshott
- Queen Square Multiple Sclerosis Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
- Department of Brain & Behavioral Sciences, University of Pavia, Pavia, Italy
- Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Ludwig Kappos
- Neurological Clinic and Policlinic, Department of Medicine, University Hospital Basel, Basel, Switzerland
- Department of Biomedical Engineering, University of Basel, Allschwig, Switzerland
| | - Maria A. Rocca
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
- Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Marios Yiannakas
- Queen Square Multiple Sclerosis Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Frederik Barkhof
- Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing (CMIC), University College London, London, United Kingdom
- Queen Square Multiple Sclerosis Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
- Department of Radiology and Nuclear Medicine, Multiple Sclerosis Center Amsterdam, Amsterdam Neuroscience Amsterdam University Medical Centers (UMC), Vrije Universiteit Medical Center (VUmc), Amsterdam, Netherlands
| | - Hugo Vrenken
- Department of Radiology and Nuclear Medicine, Multiple Sclerosis Center Amsterdam, Amsterdam Neuroscience Amsterdam University Medical Centers (UMC), Vrije Universiteit Medical Center (VUmc), Amsterdam, Netherlands
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12
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Wolf K, Reisert M, Beltrán SF, Klingler JH, Hubbe U, Krafft AJ, Egger K, Hohenhaus M. Focal cervical spinal stenosis causes mechanical strain on the entire cervical spinal cord tissue - A prospective controlled, matched-pair analysis based on phase-contrast MRI. NEUROIMAGE-CLINICAL 2021; 30:102580. [PMID: 33578322 PMCID: PMC7875814 DOI: 10.1016/j.nicl.2021.102580] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Revised: 11/30/2020] [Accepted: 01/21/2021] [Indexed: 12/27/2022]
Abstract
BACKGROUND Focally increased spinal cord motion at the level of cervical spinal stenosis has been revealed by phase-contrast MRI (PC-MRI). OBJECTIVE To investigate spinal cord motion among patients suffering of degenerative cervical myelopathy (DCM) across the entire cervical spine applying automated segmentation and standardized PC-MRI post-processing protocols. METHODS Prospective, matched-pair controlled trial on 29 patients with stenosis at C5/C6. MRI-protocol covering all cervical segments: 3D T2-SPACE, prospectively ECG-triggered sagittal PC-MRI. Segmentation by trained 3D hierarchical deep convolutional neural network and data processing were conducted via in-house software pipeline. Parameters per segment: maximum velocity, peak-to-peak (PTP)-amplitude, total displacement, PTP-amplitudeHB (PTP-amplitude per duration of heartbeat), and, for characterization of intraindividual alterations, the PTP-amplitude index between the cervical segments C3/C4-C7/T1 and C2/C3. RESULTS Spinal cord motion was increased at C4/C5, C5/C6 and C6/C7 among patients (all parameters, p < 0.001-0.025). The PTP-amplitude index revealed an increase from C3/C4 to C4/C5 (p = 0.002), C4/C5 to C5/C6 (p = 0.037) and a decrease from C5/C6 to C6/C7 and C6/C7 to C7/T1 (p < 0.001, each). This implied an up-building stretch on spinal cord tissue cranial and a mechanical compression caudal of the stenotic level. Furthermore, significant far range effects across the entire cervical spinal cord were observed (e.g. PTP-amplitude C2/C3 vs. C6/C7, p = 0.026) in contrast to controls (p = 1.00). CONCLUSION This study revealed the nature and extends of mechanical stress on the entire cervical spinal cord tissue due to focal stenosis. These pathophysiological alterations of spinal cord motion can be expected to be clinically relevant.
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Affiliation(s)
- Katharina Wolf
- Department of Neurology and Neurophysiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany.
| | - Marco Reisert
- Department of Radiology, Medical Physics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | - Saúl Felipe Beltrán
- Department of Neurology and Neurophysiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | - Jan-Helge Klingler
- Department of Neurosurgery, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | - Ulrich Hubbe
- Department of Neurosurgery, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | - Axel J Krafft
- Department of Radiology, Medical Physics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | - Karl Egger
- Department of Neuroradiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany; Department of Radiology, Tauernklinikum Zell am See/Mittersill, Salzburg, Austria
| | - Marc Hohenhaus
- Department of Neurosurgery, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
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13
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Servelhere KR, Casseb RF, de Lima FD, Rezende TJR, Ramalho LP, França MC. Spinal Cord Gray and White Matter Damage in Different Hereditary Spastic Paraplegia Subtypes. AJNR Am J Neuroradiol 2021; 42:610-615. [PMID: 33478946 DOI: 10.3174/ajnr.a7017] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Accepted: 10/04/2020] [Indexed: 12/12/2022]
Abstract
BACKGROUND AND PURPOSE Spinal cord damage is a hallmark of hereditary spastic paraplegias, but it is still not clear whether specific subtypes of the disease have distinctive patterns of spinal cord gray (GM) and white (WM) matter involvement. We compared cervical cross-sectional GM and WM areas in patients with distinct hereditary spastic paraplegia subtypes. We also assessed whether these metrics correlated with clinical parameters. MATERIALS AND METHODS We analyzed 37 patients (17 men; mean age, 47.3 [SD, 16.5] years) and 21 healthy controls (7 men; mean age, 42.3 [SD, 13.2] years). There were 7 patients with spastic paraplegia type 3A (SPG3A), 12 with SPG4, 10 with SPG7, and 8 with SPG11. Image acquisition was performed on a 3T MR imaging scanner, and T2*-weighted 2D images were assessed by the Spinal Cord Toolbox. Statistical analyses were performed in SPSS using nonparametric tests and false discovery rate-corrected P values < .05. RESULTS The mean disease duration for the hereditary spastic paraplegia group was 22.4 [SD, 13.8] years and the mean Spastic Paraplegia Rating Scale score was 22.8 [SD, 11.0]. We failed to identify spinal cord atrophy in SPG3A and SPG7. In contrast, we found abnormalities in patients with SPG4 and SPG11. Both subtypes had spinal cord GM and WM atrophy. SPG4 showed a strong inverse correlation between GM area and disease duration (ρ = -0.903, P < .001). CONCLUSIONS Cervical spinal cord atrophy is found in some but not all hereditary spastic paraplegia subtypes. Spinal cord damage in SPG4 and 11 involves both GM and WM.
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Affiliation(s)
- K R Servelhere
- From the School of Medical Sciences (K.R.S., F.D.d.L. T.J.R.R., L.P.R., M.C.F.), University of Campinas, Campinas, Brazil
| | - R F Casseb
- Seaman Family MR Research Center (R.F.C.), University of Calgary, Calgary, Alberta, Canada
| | - F D de Lima
- From the School of Medical Sciences (K.R.S., F.D.d.L. T.J.R.R., L.P.R., M.C.F.), University of Campinas, Campinas, Brazil
| | - T J R Rezende
- From the School of Medical Sciences (K.R.S., F.D.d.L. T.J.R.R., L.P.R., M.C.F.), University of Campinas, Campinas, Brazil
| | - L P Ramalho
- From the School of Medical Sciences (K.R.S., F.D.d.L. T.J.R.R., L.P.R., M.C.F.), University of Campinas, Campinas, Brazil
| | - M C França
- From the School of Medical Sciences (K.R.S., F.D.d.L. T.J.R.R., L.P.R., M.C.F.), University of Campinas, Campinas, Brazil
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14
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Papinutto N, Cordano C, Asteggiano C, Caverzasi E, Mandelli ML, Lauricella M, Yabut N, Neylan M, Kirkish G, Gorno-Tempini ML, Henry RG. MRI Measurement of Upper Cervical Spinal Cord Cross-Sectional Area in Children. J Neuroimaging 2020; 30:598-602. [PMID: 32639671 DOI: 10.1111/jon.12758] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 06/24/2020] [Accepted: 06/26/2020] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND AND PURPOSE Neurological and neurodegenerative diseases can affect the spinal cord (SC) of pediatric patients. Magnetic resonance imaging (MRI) allows for in vivo quantification of SC atrophy via cross-sectional area (CSA). The study of CSA values in the general population is important to disentangle disease-related changes from intersubject variability. This study aimed at providing normative values for cervical CSA in children, extending our previous work performed with adults. METHODS Seventy-eight children (age 7-17 years) were selected from a Developmental Dyslexia study. All subjects underwent a 3T brain MRI session and any incidental findings were reported on the scans. A sagittal 1 mm3 3-dimensional T1 -weighted brain acquisition extended to the upper cervical cord was used to measure CSA at C2-C3, as well as spinal canal area and skull volume (V-scale). These three metrics were linearly fitted as a function of age to extract trends and percentage annual changes. Sex differences of CSA were assessed using least squares regression analyses, adjusting for age. We tested normalization strategies proven to be effective in reducing the intersubject variability of adults' CSA. RESULTS CSA changed as a function of age at a faster rate when compared with skull volume (CSA: 1.82% increase, V-scale: .60% reduction). Sex had a statistically significant effect on CSA. Normalization methods based on canal area and skull volume reduced the CSA intersubject variability up to 16.84%. CONCLUSIONS We present CSA normative values in a large cohort of children, reporting on sources of intersubject variability and how to reduce them applying normalization methods previously developed.
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Affiliation(s)
- Nico Papinutto
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, CA
| | - Christian Cordano
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, CA
| | - Carlo Asteggiano
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, CA.,Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Eduardo Caverzasi
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, CA
| | - Maria Luisa Mandelli
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, CA
| | - Michael Lauricella
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, CA
| | - Nicole Yabut
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, CA
| | - Matthew Neylan
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, CA
| | - Gina Kirkish
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, CA
| | - Maria Luisa Gorno-Tempini
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, CA
| | - Roland G Henry
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, CA
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15
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Yokota Y, Takeda C, Kidoh M, Oda S, Aoki R, Ito K, Morita K, Haraoka K, Yamashita Y, Iizuka H, Kato S, Tsujita K, Ikeda O, Yamashita Y, Utsunomiya D. Effects of Deep Learning Reconstruction Technique in High-Resolution Non-contrast Magnetic Resonance Coronary Angiography at a 3-Tesla Machine. Can Assoc Radiol J 2020; 72:120-127. [DOI: 10.1177/0846537119900469] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
Purpose: To evaluate the effects of deep learning reconstruction (DLR) in qualitative and quantitative image quality of non-contrast magnetic resonance coronary angiography (MRCA). Methods: Ten healthy volunteers underwent conventional MRCA (C-MRCA) and high-resolution (HR) MRCA on a 3T magnetic resonance imaging with a voxel size of 1.8 × 1.1 × 1.7 mm3 and 1.8 × 0.6 × 1.0 mm3, respectively, for C-MRCA and HR-MRCA. High-resolution magnetic resonance coronary angiography was also reconstructed with the DLR technique (DLR-HR-MRCA). We compared the contrast-to-noise ratio (CNR) and visual evaluation scores for vessel sharpness and traceability of proximal and distal coronary vessels on a 4-point scale among 3 image series. Results: The vascular CNR value on the C-MRCA and the DLR-HR-MRCA was significantly higher than that on the HR-MRCA in the proximal and distal coronary arteries (13.9 ± 6.4, 11.3 ± 4.4, and 7.8 ± 2.6 for C-MRCA, DLR-HR-MRCA, and HR-MRCA, P < .05, respectively). Mean visual evaluation scores for the vessel sharpness and traceability of proximal and distal coronary vessels were significantly higher on the HR-DLR-MRCA than the C-MRCA ( P < .05, respectively). Conclusion: Deep learning reconstruction significantly improved the CNR of coronary arteries on HR-MRCA, resulting in both higher visual image quality and better vessel traceability compared with C-MRCA.
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Affiliation(s)
- Yasuhiro Yokota
- Diagnostic Radiology, Faculty of Life Sciences, Kumamoto University, Kumamoto-shi, Japan
| | - Chika Takeda
- Diagnostic Radiology, Yokohama City University Graduate School of Medicine, Yokohama-shi, Japan
| | - Masafumi Kidoh
- Diagnostic Radiology, Faculty of Life Sciences, Kumamoto University, Kumamoto-shi, Japan
| | - Seitaro Oda
- Diagnostic Radiology, Faculty of Life Sciences, Kumamoto University, Kumamoto-shi, Japan
| | - Ryo Aoki
- Diagnostic Radiology, Yokohama City University Graduate School of Medicine, Yokohama-shi, Japan
| | - Kenichi Ito
- Diagnostic Radiology, Yokohama City University Graduate School of Medicine, Yokohama-shi, Japan
| | - Kosuke Morita
- Central Radiology, Kumamoto University Hospital, Kumamoto-shi, Japan
| | - Kentaro Haraoka
- MRI Systems Division, Canon Medical Systems Corporation, Kawasaki-shi, Japan
| | - Yuichi Yamashita
- MRI Systems Division, Canon Medical Systems Corporation, Kawasaki-shi, Japan
| | - Hitoshi Iizuka
- Diagnostic Radiology, Yokohama City University Graduate School of Medicine, Yokohama-shi, Japan
| | - Shingo Kato
- Diagnostic Radiology, Yokohama City University Graduate School of Medicine, Yokohama-shi, Japan
- Cardiology, Kanagawa Cardiovascular and Respiratory Center, Yokohama-shi, Japan
| | - Kenichi Tsujita
- Cardiovascular Medicine, Faculty of Life Sciences, Kumamoto University, Kumamoto-shi, Japan
| | - Osamu Ikeda
- Diagnostic Radiology, Faculty of Life Sciences, Kumamoto University, Kumamoto-shi, Japan
| | - Yasuyuki Yamashita
- Diagnostic Radiology, Faculty of Life Sciences, Kumamoto University, Kumamoto-shi, Japan
| | - Daisuke Utsunomiya
- Diagnostic Radiology, Yokohama City University Graduate School of Medicine, Yokohama-shi, Japan
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16
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Papinutto N, Asteggiano C, Bischof A, Gundel TJ, Caverzasi E, Stern WA, Bastianello S, Hauser SL, Henry RG. Intersubject Variability and Normalization Strategies for Spinal Cord Total Cross-Sectional and Gray Matter Areas. J Neuroimaging 2019; 30:110-118. [PMID: 31571307 DOI: 10.1111/jon.12666] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Revised: 09/02/2019] [Accepted: 09/16/2019] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND AND PURPOSE The quantification of spinal cord (SC) atrophy by MRI has assumed an important role in assessment of neuroinflammatory/neurodegenerative diseases and traumatic SC injury. Recent technical advances make possible the quantification of gray matter (GM) and white matter tissues in clinical settings. However, the goal of a reliable diagnostic, prognostic or predictive marker is still elusive, in part due to large intersubject variability of SC areas. Here, we investigated the sources of this variability and explored effective strategies to reduce it. METHODS One hundred twenty-nine healthy subjects (mean age: 41.0 ± 15.9) underwent MRI on a Siemens 3T Skyra scanner. Two-dimensional PSIR at the C2-C3 vertebral level and a sagittal 1 mm3 3D T1-weighted brain acquisition extended to the upper cervical cord were acquired. Total cross-sectional area and GM area were measured at C2-C3, as well as measures of the vertebra, spinal canal and the skull. Correlations between the different metrics were explored using Pearson product-moment coefficients. The most promising metrics were used to normalize cord areas using multiple regression analyses. RESULTS The most effective normalization metrics were the V-scale (from SienaX) and the product of the C2-C3 spinal canal diameters. Normalization methods based on these metrics reduced the intersubject variability of cord areas of up to 17.74%. The measured cord areas had a statistically significant sex difference, while the effect of age was moderate. CONCLUSIONS The present work explored in a large cohort of healthy subjects the source of intersubject variability of SC areas and proposes effective normalization methods for its reduction.
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Affiliation(s)
- Nico Papinutto
- Department of Neurology, University of California, San Francisco, CA
| | - Carlo Asteggiano
- Department of Neurology, University of California, San Francisco, CA.,Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Antje Bischof
- Department of Neurology, University of California, San Francisco, CA
| | - Tristan J Gundel
- Department of Neurology, University of California, San Francisco, CA
| | - Eduardo Caverzasi
- Department of Neurology, University of California, San Francisco, CA.,Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - William A Stern
- Department of Neurology, University of California, San Francisco, CA
| | - Stefano Bastianello
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Stephen L Hauser
- Department of Neurology, University of California, San Francisco, CA
| | - Roland G Henry
- Department of Neurology, University of California, San Francisco, CA
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17
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Chien C, Scheel M, Schmitz-Hübsch T, Borisow N, Ruprecht K, Bellmann-Strobl J, Paul F, Brandt AU. Spinal cord lesions and atrophy in NMOSD with AQP4-IgG and MOG-IgG associated autoimmunity. Mult Scler 2018; 25:1926-1936. [PMID: 30475082 DOI: 10.1177/1352458518815596] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
BACKGROUND Spinal cord (SC) affection is a hallmark symptom of neuromyelitis optica spectrum disorders (NMOSD). Patients with aquaporin-4 (AQP4-IgG+) or myelin oligodendrocyte glycoprotein (MOG-IgG+) antibody seropositivity show this overlapping clinical phenotype. OBJECTIVE Quantitative comparison of SC lesions and atrophy in AQP4-IgG+ and MOG-IgG+ NMOSD. METHODS AQP4-IgG+ (n = 38), MOG-IgG+ (n = 15) NMOSD patients and healthy controls (HC, n = 24) were analysed for SC lesion (prevalence, length, location), atrophy as mean upper cervical cord area (MUCCA), Expanded Disability Status Scale (EDSS), timed 25-foot walk speed (T25FWS) and 9-hole peg test (9HPT) measures. RESULTS In total, 92% (35/38) of AQP4-IgG+ and 53% (8/15) of MOG-IgG+ patients had myelitis attacks (χ2 = 6.47, p = 0.011). 65.8%/26.7% of AQP4-/MOG-IgG+ patients had chronic SC lesions (χ2 = 5.16, p = 0.023), with similar proportions in cervical, upper thoracic and lower thoracic cord, and no length differences. MUCCA was decreased in AQP4-IgG+ (t = -2.27, p = 0.028), but not MOG-IgG+ patients (t = 0.58, p = 0.57) compared to HC. MUCCA associated with myelitis attacks (rho = -0.33, p = 0.016), EDSS (rho = -0.31, p = 0.030), pyramidal functional score (rho = -0.42, p = 0.003), T25FWS (r = 0.43, p = 0.010) and 9HPT Z-score (r = 0.32, p = 0.037), regardless of antibody status. CONCLUSION AQP4-IgG+ patients had more myelitis attacks, SC lesions and SC atrophy was more pronounced than in MOG-IgG+ patients. MUCCA is associated with clinical myelitis attacks and disability in all NMOSD patients.
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Affiliation(s)
- Claudia Chien
- NeuroCure Clinical Research Center, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Michael Scheel
- NeuroCure Clinical Research Center, Charité - Universitätsmedizin Berlin, Berlin, Germany/Department of Neuroradiology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Tanja Schmitz-Hübsch
- NeuroCure Clinical Research Center, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Nadja Borisow
- NeuroCure Clinical Research Center, Charité - Universitätsmedizin Berlin, Berlin, Germany/Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Klemens Ruprecht
- Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Judith Bellmann-Strobl
- NeuroCure Clinical Research Center, Charité - Universitätsmedizin Berlin, Berlin, Germany/Experimental and Clinical Research Center, Max Delbrück Center for Molecular Medicine and Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Friedemann Paul
- NeuroCure Clinical Research Center, Charité - Universitätsmedizin Berlin, Berlin, Germany/ Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany/ Experimental and Clinical Research Center, Max Delbrück Center for Molecular Medicine and Charité -Universitätsmedizin Berlin, Berlin, Germany
| | - Alexander U Brandt
- NeuroCure Clinical Research Center, Charité - Universitätsmedizin Berlin, Berlin, Germany/Department of Neurology, University of California, Irvine, CA, USA
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