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Herthum H, Hetzer S. Tensor denoising of quantitative multi-parameter mapping. Magn Reson Med 2024; 92:145-157. [PMID: 38368616 DOI: 10.1002/mrm.30050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 01/12/2024] [Accepted: 01/25/2024] [Indexed: 02/20/2024]
Abstract
PURPOSE Quantitative multi-parameter mapping (MPM) provides maps of physical quantities representing physiologically meaningful tissue characteristics, which allows to investigate microstructure-function relationships reflecting normal or pathologic processes in the brain. However, the achievable spatial resolution and stability of MPM for basic research or clinical applications is severely constrained by SNR limits of the MR acquisition process, resulting in relatively long acquisition times. To increase SNR, we denoise MPM acquisitions using principal component analysis along tensors exploiting the Marchenko-Pastur law (tMPPCA). METHODS tMPPCA denoising was applied to three sets of MPM raw data before the quantification of maps of proton density, magnetization transfer saturation, R1, and R2*. The regional SNR gain for high-resolution MPM was investigated as well as reproducibility gains for clinically optimized protocols with moderate and high acceleration factors at different image resolutions. RESULTS Substantial noise reduction in raw data was achieved, resulting in reduced noise for quantitative mapping up to sixfold without introducing bias of mean values (below 1%). Scan-rescan fluctuations were reduced up to threefold. Denoising allowed to decrease the voxel volume fourfold at the same scan time or reduce the scan time twofold at same voxel volume without loss of sensitivity. CONCLUSIONS tMPPCA denoising can (a) improve of fine spatial and temporal patterns, (b) considerably reduce scan time for clinical applications, or (c) increase resolution to potentially push cutting-edge MPM protocols from the upper to the lower limit of the mesoscopic scale.
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Affiliation(s)
- Helge Herthum
- Berlin Center for Advanced Neuroimaging, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- Bernstein Center for Computational Neuroscience, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Stefan Hetzer
- Berlin Center for Advanced Neuroimaging, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- Bernstein Center for Computational Neuroscience, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
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2
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Schilling KG, Combes AJE, Ramadass K, Rheault F, Sweeney G, Prock L, Sriram S, Cohen-Adad J, Gore JC, Landman BA, Smith SA, O'Grady KP. Influence of preprocessing, distortion correction and cardiac triggering on the quality of diffusion MR images of spinal cord. Magn Reson Imaging 2024; 108:11-21. [PMID: 38309376 DOI: 10.1016/j.mri.2024.01.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 01/04/2024] [Accepted: 01/14/2024] [Indexed: 02/05/2024]
Abstract
Diffusion MRI of the spinal cord (SC) is susceptible to geometric distortion caused by field inhomogeneities, and prone to misalignment across time series and signal dropout caused by biological motion. Several modifications of image acquisition and image processing techniques have been introduced to overcome these artifacts, but their specific benefits are largely unproven and warrant further investigations. We aim to evaluate two specific aspects of image acquisition and processing that address image quality in diffusion studies of the spinal cord: susceptibility corrections to reduce geometric distortions, and cardiac triggering to minimize motion artifacts. First, we evaluate 4 distortion preprocessing strategies on 7 datasets of the cervical and lumbar SC and find that while distortion correction techniques increase geometric similarity to structural images, they are largely driven by the high-contrast cerebrospinal fluid, and do not consistently improve the geometry within the cord nor improve white-to-gray matter contrast. We recommend at a minimum to perform bulk-motion correction in preprocessing and posit that improvements/adaptations are needed for spinal cord distortion preprocessing algorithms, which are currently optimized and designed for brain imaging. Second, we design experiments to evaluate the impact of removing cardiac triggering. We show that when triggering is foregone, images are qualitatively similar to triggered sequences, do not have increased prevalence of artifacts, and result in similar diffusion tensor indices with similar reproducibility to triggered acquisitions. When triggering is removed, much shorter acquisitions are possible, which are also qualitatively and quantitatively similar to triggered sequences. We suggest that removing cardiac triggering for cervical SC diffusion can be a reasonable option to save time with minimal sacrifice to image quality.
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Affiliation(s)
- Kurt G Schilling
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - Anna J E Combes
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Karthik Ramadass
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA; Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Francois Rheault
- Medical Imaging and Neuroinformatic (MINi) Lab, Department of Computer Science, University of Sherbrooke, Canada
| | - Grace Sweeney
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Logan Prock
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Subramaniam Sriram
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - 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, Montreal, QC, Canada
| | - John C Gore
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Bennett A Landman
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA; Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Seth A Smith
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Kristin P O'Grady
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
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3
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Renton AI, Dao TT, Johnstone T, Civier O, Sullivan RP, White DJ, Lyons P, Slade BM, Abbott DF, Amos TJ, Bollmann S, Botting A, Campbell MEJ, Chang J, Close TG, Dörig M, Eckstein K, Egan GF, Evas S, Flandin G, Garner KG, Garrido MI, Ghosh SS, Grignard M, Halchenko YO, Hannan AJ, Heinsfeld AS, Huber L, Hughes ME, Kaczmarzyk JR, Kasper L, Kuhlmann L, Lou K, Mantilla-Ramos YJ, Mattingley JB, Meier ML, Morris J, Narayanan A, Pestilli F, Puce A, Ribeiro FL, Rogasch NC, Rorden C, Schira MM, Shaw TB, Sowman PF, Spitz G, Stewart AW, Ye X, Zhu JD, Narayanan A, Bollmann S. Neurodesk: an accessible, flexible and portable data analysis environment for reproducible neuroimaging. Nat Methods 2024; 21:804-808. [PMID: 38191935 DOI: 10.1038/s41592-023-02145-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 11/27/2023] [Indexed: 01/10/2024]
Abstract
Neuroimaging research requires purpose-built analysis software, which is challenging to install and may produce different results across computing environments. The community-oriented, open-source Neurodesk platform ( https://www.neurodesk.org/ ) harnesses a comprehensive and growing suite of neuroimaging software containers. Neurodesk includes a browser-accessible virtual desktop, command-line interface and computational notebook compatibility, allowing for accessible, flexible, portable and fully reproducible neuroimaging analysis on personal workstations, high-performance computers and the cloud.
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Affiliation(s)
- Angela I Renton
- The University of Queensland, Queensland Brain Institute, St Lucia, Brisbane, Queensland, Australia.
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia.
| | - Thuy T Dao
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia
| | - Tom Johnstone
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn, Victoria, Australia
| | - Oren Civier
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn, Victoria, Australia
| | - Ryan P Sullivan
- The University of Sydney, School of Biomedical Engineering, Sydney, New South Wales, Australia
| | - David J White
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn, Victoria, Australia
| | - Paris Lyons
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn, Victoria, Australia
| | - Benjamin M Slade
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn, Victoria, Australia
| | - David F Abbott
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Toluwani J Amos
- School of Life Science and Technology, University of Electronic Science and Technology, Chengdu, China
| | - Saskia Bollmann
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia
| | - Andy Botting
- Australian Research Data Commons (ARDC), Sydney, New South Wales, Australia
| | - Megan E J Campbell
- School of Psychological Sciences, University of Newcastle, Newcastle, New South Wales, Australia
- Hunter Medical Research Institute Imaging Centre, Newcastle, New South Wales, Australia
| | - Jeryn Chang
- The University of Queensland, School of Biomedical Sciences, St Lucia, Brisbane, Queensland, Australia
| | - Thomas G Close
- The University of Sydney, School of Biomedical Engineering, Sydney, New South Wales, Australia
| | - Monika Dörig
- Integrative Spinal Research, Department of Chiropractic Medicine, Balgrist University Hospital, University of Zurich, Zurich, Switzerland
| | - Korbinian Eckstein
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia
| | - Gary F Egan
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
- Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia
| | - Stefanie Evas
- School of Psychology, University of Adelaide, Adelaide, South Australia, Australia
- Human Health, Health & Biosecurity, CSIRO, Adelaide, South Australia, Australia
| | - Guillaume Flandin
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Kelly G Garner
- School of Psychology, University of New South Wales, Sydney, New South Wales, Australia
- The University of Queensland, School of Psychology, St Lucia, Brisbane, Queensland, Australia
| | - Marta I Garrido
- Melbourne School of Psychological Sciences, he University of Melbourne, Melbourne, Victoria, Australia
- Graeme Clark Institute for Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia
| | - Satrajit S Ghosh
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Otolaryngology - Head and Neck Surgery, Harvard Medical School, Boston, MA, USA
| | - Martin Grignard
- GIGA CRC In-Vivo Imaging, University of Liège, Liège, Belgium
| | - Yaroslav O Halchenko
- Center for Open Neuroscience, Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
| | - Anthony J Hannan
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Anibal S Heinsfeld
- Department of Psychology, Center for Perceptual Systems, Institute for Neuroscience, Center For Learning and Memory, The University of Texas at Austin, Austin, TX, USA
| | - Laurentius Huber
- National Institute of Mental Health (NIMH), National Institutes Health, Bethesda, MD, USA
| | - Matthew E Hughes
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn, Victoria, Australia
| | - Jakub R Kaczmarzyk
- Department of Biomedical Informatics, Stony Brook University, New York, NY, USA
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, New York, NY, USA
| | - Lars Kasper
- BRAIN-TO Lab, Krembil Brain Institute, University Health Network, Toronto, Ontario, Canada
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Levin Kuhlmann
- Department of Data Science and AI, Faculty of Information Technology, Monash University, Melbourne, Victoria, Australia
| | - Kexin Lou
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia
- Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Yorguin-Jose Mantilla-Ramos
- Grupo Neuropsicología y Conducta (GRUNECO), Facultad de Medicina, Universidad de Antioquia, Medellín, Colombia
| | - Jason B Mattingley
- The University of Queensland, Queensland Brain Institute, St Lucia, Brisbane, Queensland, Australia
- The University of Queensland, School of Psychology, St Lucia, Brisbane, Queensland, Australia
| | - Michael L Meier
- Integrative Spinal Research, Department of Chiropractic Medicine, Balgrist University Hospital, University of Zurich, Zurich, Switzerland
| | - Jo Morris
- Australian Research Data Commons (ARDC), Sydney, New South Wales, Australia
| | - Akshaiy Narayanan
- School of Computer Science, The University of Auckland, Auckland, New Zealand
| | - Franco Pestilli
- Department of Psychology, Center for Perceptual Systems, Institute for Neuroscience, Center For Learning and Memory, The University of Texas at Austin, Austin, TX, USA
| | - Aina Puce
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Fernanda L Ribeiro
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia
| | - Nigel C Rogasch
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
- Discipline of Psychiatry, Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia
- Hopwood Centre for Neurobiology, Lifelong Health Theme, South Australian Health and Medical Research Institute (SAHMRI), Adelaide, South Australia, Australia
| | - Chris Rorden
- McCausland Center for Brain Imaging, Department of Psychology, University of South Carolina, Columbia, SC, USA
| | - Mark M Schira
- School of Psychology, University of Wollongong, Wollongong, New South Wales, Australia
| | - Thomas B Shaw
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia
- The University of Queensland, Centre for Advanced Imaging, St Lucia, Brisbane, Queensland, Australia
- Department of Neurology, Royal Brisbane and Women's Hospital, Brisbane, Queensland, Australia
| | - Paul F Sowman
- Macquarie University, School of Psychological Sciences, Sydney, New South Wales, Australia
| | - Gershon Spitz
- Department of Neuroscience, Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia
- Monash-Epworth Rehabilitation Research Centre, Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
| | - Ashley W Stewart
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Queensland, Australia
| | - Xincheng Ye
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia
| | - Judy D Zhu
- Macquarie University, School of Psychological Sciences, Sydney, New South Wales, Australia
| | - Aswin Narayanan
- The University of Queensland, Centre for Advanced Imaging, St Lucia, Brisbane, Queensland, Australia
| | - Steffen Bollmann
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia.
- The University of Queensland, Centre for Advanced Imaging, St Lucia, Brisbane, Queensland, Australia.
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Queensland, Australia.
- Queensland Digital Health Centre, The University of Queensland, Brisbane, Queensland, Australia.
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Tur C, Battiston M, Yiannakas MC, Collorone S, Calvi A, Prados F, Kanber B, Grussu F, Ricciardi A, Pajak P, Martinelli D, Schneider T, Ciccarelli O, Samson RS, Wheeler-Kingshott CAG. What contributes to disability in progressive MS? A brain and cervical cord-matched quantitative MRI study. Mult Scler 2024; 30:516-534. [PMID: 38372019 DOI: 10.1177/13524585241229969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
BACKGROUND We assessed the ability of a brain-and-cord-matched quantitative magnetic resonance imaging (qMRI) protocol to differentiate patients with progressive multiple sclerosis (PMS) from controls, in terms of normal-appearing (NA) tissue abnormalities, and explain disability. METHODS A total of 27 patients and 16 controls were assessed on the Expanded Disability Status Scale (EDSS), 25-foot timed walk (TWT), 9-hole peg (9HPT) and symbol digit modalities (SDMT) tests. All underwent 3T brain and (C2-C3) cord structural imaging and qMRI (relaxometry, quantitative magnetisation transfer, multi-shell diffusion-weighted imaging), using a fast brain-and-cord-matched protocol with brain-and-cord-unified imaging readouts. Lesion and NA-tissue volumes and qMRI metrics reflecting demyelination and axonal loss were obtained. Random forest analyses identified the most relevant volumetric/qMRI measures to clinical outcomes. Confounder-adjusted linear regression estimated the actual MRI-clinical associations. RESULTS Several qMRI/volumetric differences between patients and controls were observed (p < 0.01). Higher NA-deep grey matter quantitative-T1 (EDSS: beta = 7.96, p = 0.006; 9HPT: beta = -0.09, p = 0.004), higher NA-white matter orientation dispersion index (TWT: beta = -3.21, p = 0.005; SDMT: beta = -847.10, p < 0.001), lower whole-cord bound pool fraction (9HPT: beta = 0.79, p = 0.001) and higher NA-cortical grey matter quantitative-T1 (SDMT = -94.31, p < 0.001) emerged as particularly relevant predictors of greater disability. CONCLUSION Fast brain-and-cord-matched qMRI protocols are feasible and identify demyelination - combined with other mechanisms - as key for disability accumulation in PMS.
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Affiliation(s)
- Carmen Tur
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, UCL (University College London) Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Multiple Sclerosis Centre of Catalonia (Cemcat). Vall d'Hebron Institute of Research. Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Marco Battiston
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, UCL (University College London) Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Marios C Yiannakas
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, UCL (University College London) Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Sara Collorone
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, UCL (University College London) Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Alberto Calvi
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, UCL (University College London) Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Fundació de Recerca Clínic Barcelona-Institut d'Investigacions Biomèdiques August Pi i Sunyer, Hospital Clinic, Barcelona, Spain
| | - Ferran Prados
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, UCL (University College London) Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK
- eHealth Center, Universitat Oberta de Catalunya, Barcelona, Spain
| | - Baris Kanber
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, UCL (University College London) Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Francesco Grussu
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, UCL (University College London) 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
| | - Antonio Ricciardi
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, UCL (University College London) Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Patrizia Pajak
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, UCL (University College London) Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Daniele Martinelli
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
| | | | - Olga Ciccarelli
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, UCL (University College London) Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- NIHR UCLH Biomedical Research Centre, London, UK
| | - Rebecca S Samson
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, UCL (University College London) Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Claudia Am Gandini Wheeler-Kingshott
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, UCL (University College London) 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 Connectivity Research Center, IRCCS Mondino Foundation, Pavia, Italy
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Hellström M, Löfstedt T, Garpebring A. Denoising and uncertainty estimation in parameter mapping with approximate Bayesian deep image priors. Magn Reson Med 2023; 90:2557-2571. [PMID: 37582257 DOI: 10.1002/mrm.29823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2022] [Revised: 06/26/2023] [Accepted: 07/18/2023] [Indexed: 08/17/2023]
Abstract
PURPOSE To mitigate the problem of noisy parameter maps with high uncertainties by casting parameter mapping as a denoising task based on Deep Image Priors. METHODS We extend the concept of denoising with Deep Image Prior (DIP) into parameter mapping by treating the output of an image-generating network as a parametrization of tissue parameter maps. The method implicitly denoises the parameter mapping process by filtering low-level image features with an untrained convolutional neural network (CNN). Our implementation includes uncertainty estimation from Bernoulli approximate variational inference, implemented with MC dropout, which provides model uncertainty in each voxel of the denoised parameter maps. The method is modular, so the specifics of different applications (e.g., T1 mapping) separate into application-specific signal equation blocks. We evaluate the method on variable flip angle T1 mapping, multi-echo T2 mapping, and apparent diffusion coefficient mapping. RESULTS We found that deep image prior adapts successfully to several applications in parameter mapping. In all evaluations, the method produces noise-reduced parameter maps with decreased uncertainty compared to conventional methods. The downsides of the proposed method are the long computational time and the introduction of some bias from the denoising prior. CONCLUSION DIP successfully denoise the parameter mapping process and applies to several applications with limited hyperparameter tuning. Further, it is easy to implement since DIP methods do not use network training data. Although time-consuming, uncertainty information from MC dropout makes the method more robust and provides useful information when properly calibrated.
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Affiliation(s)
- Max Hellström
- Department of Radiation Sciences, Umeå University, Umeå, Sweden
| | - Tommy Löfstedt
- Department of Radiation Sciences, Umeå University, Umeå, Sweden
- Department of Computing Science, Umeå University, Umeå, Sweden
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Moeller S, Buko EO, Parvaze SP, Dowdle L, Ugurbil K, Johnson CP, Akcakaya M. Locally low-rank denoising in transform domains. bioRxiv 2023:2023.11.21.568193. [PMID: 38076916 PMCID: PMC10705240 DOI: 10.1101/2023.11.21.568193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
Purpose To develop an extension to locally low rank (LLR) denoising techniques based on transform domain processing that reduces the number of images required in the MR image series for high-quality denoising. Theory and Methods LLR methods with random matrix theory-based thresholds are successfully used in the denoising of MR image series in a number of applications. The performance of these methods depend on how well the LLR assumption is satisfied, which deteriorates with few numbers of images, as is commonly encountered in quantitative MRI applications. We propose a transform-domain approach for denoising of MR image series to represent the underlying signal with higher fidelity when using a locally low rank approximation. The efficacy of the method is demonstrated for fully-sampled k-space, undersampled k-space, DICOM images, and complex-valued SENSE-1 images in quantitative MRI applications with as few as 4 images. Results For both MSK and brain applications, the transform domain denoising preserves local subtle variability, whereas the quantitative maps based on image domain LLR methods tend to be locally more homogeneous. Conclusion A transform domain extension to LLR denoising produces high quality images and is compatible with both raw k-space data and vendor reconstructed data. This allows for improved imaging and more accurate quantitative analyses and parameters obtained therefrom.
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Affiliation(s)
- Steen Moeller
- University of Minnesota, Department of Radiology, Center for Magnetic Resonance Research, 2021 6 Street SE
| | - Erick O Buko
- University of Minnesota, Department of Radiology, Center for Magnetic Resonance Research, 2021 6 Street SE
| | - Suhail P Parvaze
- University of Minnesota, Department of Radiology, Center for Magnetic Resonance Research, 2021 6 Street SE
| | - Logan Dowdle
- University of Minnesota, Department of Radiology, Center for Magnetic Resonance Research, 2021 6 Street SE
| | - Kamil Ugurbil
- University of Minnesota, Department of Radiology, Center for Magnetic Resonance Research, 2021 6 Street SE
| | - Casey P Johnson
- University of Minnesota, Department of Radiology, Center for Magnetic Resonance Research, 2021 6 Street SE
| | - Mehmet Akcakaya
- University of Minnesota, Department of Radiology, Center for Magnetic Resonance Research, 2021 6 Street SE
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Soustelle L, Troalen T, Hertanu A, Ranjeva JP, Guye M, Varma G, Alsop DC, Duhamel G, Girard OM. Quantitative magnetization transfer MRI unbiased by on-resonance saturation and dipolar order contributions. Magn Reson Med 2023. [PMID: 37154400 DOI: 10.1002/mrm.29678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 03/22/2023] [Accepted: 04/01/2023] [Indexed: 05/10/2023]
Abstract
PURPOSE To demonstrate the bias in quantitative MT (qMT) measures introduced by the presence of dipolar order and on-resonance saturation (ONRS) effects using magnetization transfer (MT) spoiled gradient-recalled (SPGR) acquisitions, and propose changes to the acquisition and analysis strategies to remove these biases. METHODS The proposed framework consists of SPGR sequences prepared with simultaneous dual-offset frequency-saturation pulses to cancel out dipolar order and associated relaxation (T1D ) effects in Z-spectrum acquisitions, and a matched quantitative MT (qMT) mathematical model that includes ONRS effects of readout pulses. Variable flip angle and MT data were fitted jointly to simultaneously estimate qMT parameters (macromolecular proton fraction [MPF], T2,f , T2,b , R, and free pool T1 ). This framework is compared with standard qMT and investigated in terms of reproducibility, and then further developed to follow a joint single-point qMT methodology for combined estimation of MPF and T1 . RESULTS Bland-Altman analyses demonstrated a systematic underestimation of MPF (-2.5% and -1.3%, on average, in white and gray matter, respectively) and overestimation of T1 (47.1 ms and 38.6 ms, on average, in white and gray matter, respectively) if both ONRS and dipolar order effects are ignored. Reproducibility of the proposed framework is excellent (ΔMPF = -0.03% and ΔT1 = -19.0 ms). The single-point methodology yielded consistent MPF and T1 values with respective maximum relative average bias of -0.15% and -3.5 ms found in white matter. CONCLUSION The influence of acquisition strategy and matched mathematical model with regard to ONRS and dipolar order effects in qMT-SPGR frameworks has been investigated. The proposed framework holds promise for improved accuracy with reproducibility.
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Affiliation(s)
- Lucas Soustelle
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France
- APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
| | | | - Andreea Hertanu
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France
- APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
| | - Jean-Philippe Ranjeva
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France
- APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
| | - Maxime Guye
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France
- APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
| | - Gopal Varma
- Division of MR Research, Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - David C Alsop
- Division of MR Research, Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Guillaume Duhamel
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France
- APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
| | - Olivier M Girard
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France
- APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
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8
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Kaptan M, Horn U, Vannesjo SJ, Mildner T, Weiskopf N, Finsterbusch J, Brooks JCW, Eippert F. Reliability of resting-state functional connectivity in the human spinal cord: assessing the impact of distinct noise sources. Neuroimage 2023; 275:120152. [PMID: 37142169 DOI: 10.1016/j.neuroimage.2023.120152] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2023] [Revised: 04/20/2023] [Accepted: 05/01/2023] [Indexed: 05/06/2023] Open
Abstract
The investigation of spontaneous fluctuations of the blood-oxygen-level-dependent (BOLD) signal has recently been extended from the brain to the spinal cord, where it has stimulated interest from a clinical perspective. A number of resting-state functional magnetic resonance imaging (fMRI) studies have demonstrated robust functional connectivity between the time series of BOLD fluctuations in bilateral dorsal horns and between those in bilateral ventral horns, in line with the functional neuroanatomy of the spinal cord. A necessary step prior to extension to clinical studies is assessing the reliability of such resting-state signals, which we aimed to do here in a group of 45 healthy young adults at the clinically prevalent field strength of 3T. When investigating connectivity in the entire cervical spinal cord, we observed fair to good reliability for dorsal-dorsal and ventral-ventral connectivity, whereas reliability was poor for within- and between-hemicord dorsal-ventral connectivity. Considering how prone spinal cord fMRI is to noise, we extensively investigated the impact of distinct noise sources and made two crucial observations: removal of physiological noise led to a reduction in functional connectivity strength and reliability - due to the removal of stable and participant-specific noise patterns - whereas removal of thermal noise considerably increased the detectability of functional connectivity without a clear influence on reliability. Finally, we also assessed connectivity within spinal cord segments and observed that while the pattern of connectivity was similar to that of whole cervical cord, reliability at the level of single segments was consistently poor. Taken together, our results demonstrate the presence of reliable resting-state functional connectivity in the human spinal cord even after thoroughly accounting for physiological and thermal noise, but at the same time urge caution if focal changes in connectivity (e.g. due to segmental lesions) are to be studied, especially in a longitudinal manner.
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Affiliation(s)
- Merve Kaptan
- Max Planck Research Group Pain Perception, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
| | - Ulrike Horn
- Max Planck Research Group Pain Perception, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - S Johanna Vannesjo
- Department of Physics, Norwegian University of Science and Technology, Trondheim, Norway
| | - Toralf Mildner
- Methods & Development Group Nuclear Magnetic Resonance, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - 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, University of Leipzig, Leipzig, Germany
| | - Jürgen Finsterbusch
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Jonathan C W Brooks
- School of Psychology, University of East Anglia Wellcome Wolfson Brain Imaging Centre (UWWBIC), Norwich, United Kingdom
| | - Falk Eippert
- Max Planck Research Group Pain Perception, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
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9
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Boonsuth R, Battiston M, Grussu F, Samlidou CM, Calvi A, Samson RS, Gandini Wheeler-Kingshott CAM, Yiannakas MC. Feasibility of in vivo multi-parametric quantitative magnetic resonance imaging of the healthy sciatic nerve with a unified signal readout protocol. Sci Rep 2023; 13:6565. [PMID: 37085693 PMCID: PMC10121559 DOI: 10.1038/s41598-023-33618-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Accepted: 04/15/2023] [Indexed: 04/23/2023] Open
Abstract
Magnetic resonance neurography (MRN) has been used successfully over the years to investigate the peripheral nervous system (PNS) because it allows early detection and precise localisation of neural tissue damage. However, studies demonstrating the feasibility of combining MRN with multi-parametric quantitative magnetic resonance imaging (qMRI) methods, which provide more specific information related to nerve tissue composition and microstructural organisation, can be invaluable. The translation of emerging qMRI methods previously validated in the central nervous system to the PNS offers real potential to characterise in patients in vivo the underlying pathophysiological mechanisms involved in a plethora of conditions of the PNS. The aim of this study was to assess the feasibility of combining MRN with qMRI to measure diffusion, magnetisation transfer and relaxation properties of the healthy sciatic nerve in vivo using a unified signal readout protocol. The reproducibility of the multi-parametric qMRI protocol as well as normative qMRI measures in the healthy sciatic nerve are reported. The findings presented herein pave the way to the practical implementation of joint MRN-qMRI in future studies of pathological conditions affecting the PNS.
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Affiliation(s)
- Ratthaporn Boonsuth
- NMR Research Unit, Department of Neuroinflammation, Faculty of Brain Sciences, Queen Square MS Centre, UCL Queen Square Institute of Neurology, University College London, London, UK.
- Department of Radiologic Technology, Faculty of Associated Medical Sciences, Chiang Mai University, Chiang Mai, Thailand.
| | - Marco Battiston
- NMR Research Unit, Department of Neuroinflammation, Faculty of Brain Sciences, Queen Square MS Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Francesco Grussu
- NMR Research Unit, Department of Neuroinflammation, Faculty of Brain Sciences, Queen Square MS Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Christina Maria Samlidou
- NMR Research Unit, Department of Neuroinflammation, Faculty of Brain Sciences, Queen Square MS Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Alberto Calvi
- NMR Research Unit, Department of Neuroinflammation, Faculty of Brain Sciences, Queen Square MS Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
- Laboratory of Advanced Imaging in Neuroimmunological Diseases, Center of Neuroimmunology, Hospital Clinic Barcelona, Fundació Clinic Per a La Recerca Biomedica, Barcelona, Spain
| | - Rebecca S Samson
- NMR Research Unit, Department of Neuroinflammation, Faculty of Brain Sciences, Queen Square MS Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Claudia A M Gandini Wheeler-Kingshott
- NMR Research Unit, Department of Neuroinflammation, Faculty of Brain Sciences, Queen Square MS Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
- Brain Connectivity Research Centre, IRCCS Mondino Foundation, Pavia, Italy
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
| | - Marios C Yiannakas
- NMR Research Unit, Department of Neuroinflammation, Faculty of Brain Sciences, Queen Square MS Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
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10
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Fernandes FF, Olesen JL, Jespersen SN, Shemesh N. MP-PCA denoising of fMRI time-series data can lead to artificial activation "spreading". Neuroimage 2023; 273:120118. [PMID: 37062372 DOI: 10.1016/j.neuroimage.2023.120118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Revised: 04/10/2023] [Accepted: 04/13/2023] [Indexed: 04/18/2023] Open
Abstract
MP-PCA denoising has become the method of choice for denoising MRI data since it provides an objective threshold to separate the signal components from unwanted thermal noise components. In rodents, thermal noise in the coils is an important source of noise that can reduce the accuracy of activation mapping in fMRI. Further confounding this problem, vendor data often contains zero-filling and other post-processing steps that may violate MP-PCA assumptions. Here, we develop an approach to denoise vendor data and assess activation "spreading" caused by MP-PCA denoising in rodent task-based fMRI data. Data was obtained from N = 3 mice using conventional multislice and ultrafast acquisitions (1 s and 50 ms temporal resolution, respectively), during visual stimulation. MP-PCA denoising produced SNR gains of 64% and 39% and Fourier Spectral Amplitude (FSA) increases in BOLD maps of 9% and 7% for multislice and ultrafast data, respectively, when using a small [2 2] denoising window. Larger windows provided higher SNR and FSA gains with increased spatial extent of activation that may or may not represent real activation. Simulations showed that MP-PCA denoising can incur activation "spreading" with increased false positive rate and smoother functional maps due to local "bleeding" of principal components, and that the optimal denoising window for improved specificity of functional mapping, based on Dice score calculations, depends on the data's tSNR and functional CNR. This "spreading" effect applies also to another recently proposed low-rank denoising method (NORDIC), although to a lesser degree. Our results bode well for enhancing spatial and/or temporal resolution in future fMRI work, while taking into account the sensitivity/specificity trade-offs of low-rank denoising methods.
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Affiliation(s)
| | - Jonas L Olesen
- Center of Functionally Integrative Neuroscience (CFIN) and MINDLab, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Department of Physics and Astronomy, Aarhus University, Aarhus, Denmark
| | - Sune N Jespersen
- Center of Functionally Integrative Neuroscience (CFIN) and MINDLab, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Department of Physics and Astronomy, Aarhus University, Aarhus, Denmark
| | - Noam Shemesh
- Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal.
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11
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Ricciardi A, Grussu F, Kanber B, Prados F, Yiannakas MC, Solanky BS, Riemer F, Golay X, Brownlee W, Ciccarelli O, Alexander DC, Gandini Wheeler-Kingshott CAM. Patterns of inflammation, microstructural alterations, and sodium accumulation define multiple sclerosis subtypes after 15 years from onset. Front Neuroinform 2023; 17:1060511. [PMID: 37035717 PMCID: PMC10076673 DOI: 10.3389/fninf.2023.1060511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 02/28/2023] [Indexed: 04/11/2023] Open
Abstract
Introduction Conventional MRI is routinely used for the characterization of pathological changes in multiple sclerosis (MS), but due to its lack of specificity is unable to provide accurate prognoses, explain disease heterogeneity and reconcile the gap between observed clinical symptoms and radiological evidence. Quantitative MRI provides measures of physiological abnormalities, otherwise invisible to conventional MRI, that correlate with MS severity. Analyzing quantitative MRI measures through machine learning techniques has been shown to improve the understanding of the underlying disease by better delineating its alteration patterns. Methods In this retrospective study, a cohort of healthy controls (HC) and MS patients with different subtypes, followed up 15 years from clinically isolated syndrome (CIS), was analyzed to produce a multi-modal set of quantitative MRI features encompassing relaxometry, microstructure, sodium ion concentration, and tissue volumetry. Random forest classifiers were used to train a model able to discriminate between HC, CIS, relapsing remitting (RR) and secondary progressive (SP) MS patients based on these features and, for each classification task, to identify the relative contribution of each MRI-derived tissue property to the classification task itself. Results and discussion Average classification accuracy scores of 99 and 95% were obtained when discriminating HC and CIS vs. SP, respectively; 82 and 83% for HC and CIS vs. RR; 76% for RR vs. SP, and 79% for HC vs. CIS. Different patterns of alterations were observed for each classification task, offering key insights in the understanding of MS phenotypes pathophysiology: atrophy and relaxometry emerged particularly in the classification of HC and CIS vs. MS, relaxometry within lesions in RR vs. SP, sodium ion concentration in HC vs. CIS, and microstructural alterations were involved across all tasks.
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Affiliation(s)
- Antonio Ricciardi
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
| | - Francesco Grussu
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
- Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Baris Kanber
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Ferran Prados
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
- eHealth Center, Universitat Oberta de Catalunya, Barcelona, Spain
| | - Marios C. Yiannakas
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
| | - Bhavana S. Solanky
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
| | - Frank Riemer
- Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Xavier Golay
- Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
| | - Wallace Brownlee
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
| | - Olga Ciccarelli
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
- NIHR UCLH Biomedical Research Centre, London, United Kingdom
| | - Daniel C. Alexander
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Claudia A. M. Gandini Wheeler-Kingshott
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
- Brain Connectivity Research Center, IRCCS Mondino Foundation, Pavia, Italy
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12
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Renton AI, Dao TT, Johnstone T, Civier O, Sullivan RP, White DJ, Lyons P, Slade BM, Abbott DF, Amos TJ, Bollmann S, Botting A, Campbell MEJ, Chang J, Close TG, Eckstein K, Egan GF, Evas S, Flandin G, Garner KG, Garrido MI, Ghosh SS, Grignard M, Hannan AJ, Huber R, Kaczmarzyk JR, Kasper L, Kuhlmann L, Lou K, Mantilla-Ramos YJ, Mattingley JB, Morris J, Narayanan A, Pestilli F, Puce A, Ribeiro FL, Rogasch NC, Rorden C, Schira M, Shaw TB, Sowman PF, Spitz G, Stewart A, Ye X, Zhu JD, Hughes ME, Narayanan A, Bollmann S. Neurodesk: An accessible, flexible, and portable data analysis environment for reproducible neuroimaging. Res Sq 2023:rs.3.rs-2649734. [PMID: 36993557 PMCID: PMC10055538 DOI: 10.21203/rs.3.rs-2649734/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Neuroimaging data analysis often requires purpose-built software, which can be challenging to install and may produce different results across computing environments. Beyond being a roadblock to neuroscientists, these issues of accessibility and portability can hamper the reproducibility of neuroimaging data analysis pipelines. Here, we introduce the Neurodesk platform, which harnesses software containers to support a comprehensive and growing suite of neuroimaging software (https://www.neurodesk.org/). Neurodesk includes a browser-accessible virtual desktop environment and a command line interface, mediating access to containerized neuroimaging software libraries on various computing platforms, including personal and high-performance computers, cloud computing and Jupyter Notebooks. This community-oriented, open-source platform enables a paradigm shift for neuroimaging data analysis, allowing for accessible, flexible, fully reproducible, and portable data analysis pipelines.
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Affiliation(s)
- Angela I. Renton
- The University of Queensland, Queensland Brain Institute, St Lucia 4072, Australia
- The University of Queensland, School of Information Technology and Electrical Engineering, St Lucia 4072, Australia
| | - Thuy T. Dao
- The University of Queensland, School of Information Technology and Electrical Engineering, St Lucia 4072, Australia
| | - Tom Johnstone
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn 3122, Australia
| | - Oren Civier
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn 3122, Australia
| | - Ryan P. Sullivan
- The University of Sydney, School of Biomedical Engineering, Sydney, Australia
| | - David J. White
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn 3122, Australia
| | - Paris Lyons
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn 3122, Australia
| | - Benjamin M. Slade
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn 3122, Australia
| | - David F. Abbott
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Victoria, Australia
| | - Toluwani J. Amos
- School of Life Science and Technology, University of Electronic Science and Technology, China
| | - Saskia Bollmann
- The University of Queensland, School of Information Technology and Electrical Engineering, St Lucia 4072, Australia
| | - Andy Botting
- Australian Research Data Commons (ARDC), Australia
| | - Megan E. J. Campbell
- School of Psychological Sciences, University of Newcastle, Australia
- Hunter Medical Research Institute Imaging Centre, Newcastle, Australia
| | - Jeryn Chang
- The University of Queensland, School of Biomedical Sciences, St Lucia 4072, Australia
| | - Thomas G. Close
- The University of Sydney, School of Biomedical Engineering, Sydney, Australia
| | - Korbinian Eckstein
- The University of Queensland, School of Information Technology and Electrical Engineering, St Lucia 4072, Australia
| | - Gary F. Egan
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Victoria, Australia
- Monash Biomedical Imaging, Monash University, Victoria, Australia
| | - Stefanie Evas
- School of Psychology, University of Adelaide, Adelaide, 5000, Australia
- Human Health, Health & Biosecurity, CSIRO, Adelaide, 5000, Australia
| | - Guillaume Flandin
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Kelly G. Garner
- The University of Queensland, Queensland Brain Institute, St Lucia 4072, Australia
- The University of Queensland, School of Psychology, St Lucia 4072, Australia
| | - Marta I. Garrido
- Melbourne School of Psychological Sciences, The University of Melbourne
- Graeme Clark Institute for Biomedical Engineering, The University of Melbourne
| | - Satrajit S. Ghosh
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Otolaryngology - Head and Neck Surgery, Harvard Medical School, Boston, MA, USA
| | - Martin Grignard
- GIGA CRC In-Vivo Imaging, University of Liege, Liege, Belgium
| | - Anthony J. Hannan
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Victoria, Australia
| | - Renzo Huber
- Functional Magnetic Resonance Imaging Core Facility (FMRIF), National Institute of Mental Health (NIMH), USA
| | - Jakub R. Kaczmarzyk
- Medical Scientist Training Program, Stony Brook University, Stony Brook, NY, United States of America
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, United States of America
| | - Lars Kasper
- Techna Institute, University Health Network, Toronto, Canada
| | - Levin Kuhlmann
- Department of Data Science and AI, Faculty of Information Technology, Monash University, Clayton VIC 3800, Australia
| | - Kexin Lou
- The University of Queensland, School of Information Technology and Electrical Engineering, St Lucia 4072, Australia
| | | | - Jason B. Mattingley
- The University of Queensland, Queensland Brain Institute, St Lucia 4072, Australia
- The University of Queensland, School of Psychology, St Lucia 4072, Australia
| | - Jo Morris
- Australian Research Data Commons (ARDC), Australia
| | | | - Franco Pestilli
- Department of Psychology, Center for Perceptual Systems, Center for Theoretical and Computational Neuroscience, Center on Aging and Population Sciences, Center for Learning and Memory, The University of Texas at Austin, 108 E Dean Keeton St, Austin, TX 78712, USA
| | - Aina Puce
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Fernanda L. Ribeiro
- The University of Queensland, School of Information Technology and Electrical Engineering, St Lucia 4072, Australia
| | - Nigel C. Rogasch
- Discipline of Psychiatry, Adelaide Medical School, University of Adelaide, Australia
- Hopwood Centre for Neurobiology, Lifelong Health Theme, South Australian Health and Medical Research Institute (SAHMRI), Adelaide, SA, Australia
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Victoria, Australia
| | - Chris Rorden
- McCausland Center for Brain Imaging, Department of Psychology, University of South Carolina, Columbia SC, 29208, USA
| | - Mark Schira
- School of Psychology, University of Wollongong, Wollongong, 2522, Australia
| | - Thomas B. Shaw
- The University of Queensland, School of Information Technology and Electrical Engineering, St Lucia 4072, Australia
- The University of Queensland, Centre for Advanced Imaging, St Lucia 4072, Australia
- Department of Neurology, Royal Brisbane and Women's Hospital, Brisbane, Australia
| | - Paul F. Sowman
- Macquarie University, School of Psychological Sciences, North Ryde 2112, Australia
| | - Gershon Spitz
- Department of Neuroscience, Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Australia
- Monash-Epworth Rehabilitation Research Centre, Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, 3168, Australia
| | - Ashley Stewart
- The University of Queensland, School of Information Technology and Electrical Engineering, St Lucia 4072, Australia
| | - Xincheng Ye
- The University of Queensland, School of Information Technology and Electrical Engineering, St Lucia 4072, Australia
| | - Judy D. Zhu
- Macquarie University, School of Psychological Sciences, North Ryde 2112, Australia
| | - Matthew E. Hughes
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn 3122, Australia
| | - Aswin Narayanan
- The University of Queensland, Centre for Advanced Imaging, St Lucia 4072, Australia
| | - Steffen Bollmann
- The University of Queensland, School of Information Technology and Electrical Engineering, St Lucia 4072, Australia
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Ferizi U, Müller-Oehring EM, Peterson ET, Pohl KM. The distortions of the free water model for diffusion MRI data when assuming single compartment relaxometry and proton density. Phys Med Biol 2023; 68:10.1088/1361-6560/acb30b. [PMID: 36638532 PMCID: PMC10100575 DOI: 10.1088/1361-6560/acb30b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Accepted: 01/13/2023] [Indexed: 01/15/2023]
Abstract
Objective.To document the bias of thesimplifiedfree water model of diffusion MRI (dMRI) signal vis-à-vis aspecificmodel which, in addition to diffusion, incorporates compartment-specific proton density (PD), T1 recovery during repetition time (TR), and T2 decay during echo time (TE).Approach.Both models assume that volume fractionfof the total signal in any voxel arises from the free water compartment (fw) such as cerebrospinal fluid or edema, and the remainder (1-f) from hindered water (hw) which is constrained by cellular structures such as white matter (WM). Thespecificandsimplifiedmodels are compared on a synthetic dataset, using a range of PD, T1 and T2 values. We then fit the models to anin vivohealthy brain dMRI dataset. For bothsyntheticandin vivodata we use experimentally feasible TR, TE, signal-to-noise ratio (SNR) and physiologically plausible diffusion profiles.Main results.From the simulations we see that the difference between the estimatedsimplified fandspecific fis largest for mid-range ground-truthf, and it increases as SNR increases. The estimation of volume fractionfis sensitive to the choice of model,simplifiedorspecific, but the estimated diffusion parameters are robust to small perturbations in the simulation.Specific fis more accurate and precise thansimplified f. In the white matter (WM) regions of thein vivoimages,specific fis lower thansimplified f.Significance.In dMRI models for free water, accounting for compartment specific PD, T1 and T2, in addition to diffusion, improves the estimation of model parameters. This extra model specification attenuates the estimation bias of compartmental volume fraction without affecting the estimation of other diffusion parameters.
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Affiliation(s)
- Uran Ferizi
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States of America
| | - Eva M Müller-Oehring
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States of America
| | - Eric T Peterson
- Center for Health Sciences, SRI International, Menlo Park, CA, United States of America
| | - Kilian M Pohl
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States of America
- Center for Health Sciences, SRI International, Menlo Park, CA, United States of America
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Schilling KG, Fadnavis S, Batson J, Visagie M, Combes AJE, By S, McKnight CD, Bagnato F, Garyfallidis E, Landman BA, Smith SA, O'Grady KP. Denoising of diffusion MRI in the cervical spinal cord - effects of denoising strategy and acquisition on intra-cord contrast, signal modeling, and feature conspicuity. Neuroimage 2023; 266:119826. [PMID: 36543265 PMCID: PMC9843739 DOI: 10.1016/j.neuroimage.2022.119826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 11/02/2022] [Accepted: 12/17/2022] [Indexed: 12/23/2022] Open
Abstract
Quantitative diffusion MRI (dMRI) is a promising technique for evaluating the spinal cord in health and disease. However, low signal-to-noise ratio (SNR) can impede interpretation and quantification of these images. The purpose of this study is to evaluate several dMRI denoising approaches on their ability to improve the quality, reliability, and accuracy of quantitative diffusion MRI of the spinal cord. We evaluate three denoising approaches (Non-Local Means, Marchenko-Pastur PCA, and a newly proposed Patch2Self algorithm) and conduct five experiments to validate the denoising performance on clinical-quality and commonly-acquired dMRI acquisitions: 1) a phantom experiment to assess denoising error and bias; 2) a multi-vendor, multi-acquisition open experiment for both qualitative and quantitative evaluation of noise residuals; 3) a bootstrapping experiment to estimate uncertainty of parametric maps; 4) an assessment of spinal cord lesion conspicuity in a multiple sclerosis group; and 5) an evaluation of denoising for advanced parametric multi-compartment modeling. We find that all methods improve signal-to-noise ratio and conspicuity of MS lesions in individual diffusion weighted images (DWIs), but MPPCA and Patch2Self excel at improving the quality and intra-cord contrast of diffusion weighted images - removing signal fluctuations due to thermal noise while improving precision of estimation of diffusion parameters even with very few DWIs (i.e., 16-32) typical of clinical acquisitions. These denoising approaches hold promise for facilitating reliable diffusion observations and measurements in the spinal cord to investigate biological and pathological processes.
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Affiliation(s)
- Kurt G Schilling
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States; Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States.
| | - Shreyas Fadnavis
- Department of Intelligent Systems Engineering, Indiana University Bloomington, Bloomington, IN, United States
| | - Joshua Batson
- The Public Health Company, California, United States
| | - Mereze Visagie
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Anna J E Combes
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States; Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Samantha By
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States
| | - Colin D McKnight
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Francesca Bagnato
- Neuroimaging Unit, Neuroimmunology Division, Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, United States; Department of Neurology, VA Hospital, TN Valley Healthcare System, Nashville, TN, United States
| | - Eleftherios Garyfallidis
- Department of Intelligent Systems Engineering, Indiana University Bloomington, Bloomington, IN, United States
| | - Bennett A Landman
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States; Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, United States
| | - Seth A Smith
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States; Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States
| | - Kristin P O'Grady
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States; Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States.
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15
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Stern N, Radunsky D, Blumenfeld‐Katzir T, Chechik Y, Solomon C, Ben‐Eliezer N. Mapping of magnetic resonance imaging's transverse relaxation time at low signal-to-noise ratio using Bloch simulations and principal component analysis image denoising. NMR Biomed 2022; 35:e4807. [PMID: 35899528 PMCID: PMC9787782 DOI: 10.1002/nbm.4807] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 07/24/2022] [Accepted: 07/25/2022] [Indexed: 06/15/2023]
Abstract
High-resolution mapping of magnetic resonance imaging (MRI)'s transverse relaxation time (T2 ) can benefit many clinical applications by offering improved anatomic details, enhancing the ability to probe tissues' microarchitecture, and facilitating the identification of early pathology. Increasing spatial resolutions, however, decreases data's signal-to-noise ratio (SNR), particularly at clinical scan times. This impairs imaging quality, and the accuracy of subsequent radiological interpretation. Recently, principal component analysis (PCA) was employed for denoising diffusion-weighted MR images and was shown to be effective for improving parameter estimation in multiexponential relaxometry. This study combines the Marchenko-Pastur PCA (MP-PCA) signal model with the echo modulation curve (EMC) algorithm for denoising multiecho spin-echo (MESE) MRI data and improving the precision of EMC-generated single T2 relaxation maps. The denoising technique was validated on simulations, phantom scans, and in vivo brain and knee data. MESE scans were performed on a 3-T Siemens scanner. The acquired images were denoised using the MP-PCA algorithm and were then provided as input for the EMC T2 -fitting algorithm. Quantitative analysis of the denoising quality included comparing the standard deviation and coefficient of variation of T2 values, along with gold standard SNR estimation of the phantom scans. The presented denoising technique shows an increase in T2 maps' precision and SNR, while successfully preserving the morphological features of the tissue. Employing MP-PCA denoising as a preprocessing step decreases the noise-related variability of T2 maps produced by the EMC algorithm and thus increases their precision. The proposed method can be useful for a wide range of clinical applications by facilitating earlier detection of pathologies and improving the accuracy of patients' follow-up.
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Affiliation(s)
- Neta Stern
- Department of Biomedical EngineeringTel Aviv UniversityIsrael
| | - Dvir Radunsky
- Department of Biomedical EngineeringTel Aviv UniversityIsrael
| | | | - Yigal Chechik
- Department of OrthopedicsShamir Medical CenterBe'er Ya'akovIsrael
- Sackler Faculty of MedicineTel Aviv UniversityTel AvivIsrael
| | - Chen Solomon
- Department of Biomedical EngineeringTel Aviv UniversityIsrael
| | - Noam Ben‐Eliezer
- Department of Biomedical EngineeringTel Aviv UniversityIsrael
- Sagol School of NeuroscienceTel Aviv UniversityIsrael
- Center for Advanced Imaging Innovation and Research (CAIR)New York University School of MedicineNew YorkNew YorkUSA
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16
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Combes AJ, 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] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [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,Corresponding author at: 1161 21st Ave S, MCN AA1105, Nashville, TN 37232, USA.
| | - 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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Tax CMW, Bastiani M, Veraart J, Garyfallidis E, Okan Irfanoglu M. What's New and What's Next in Diffusion MRI Preprocessing. Neuroimage 2021;:118830. [PMID: 34965454 DOI: 10.1016/j.neuroimage.2021.118830] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 10/26/2021] [Accepted: 12/15/2021] [Indexed: 02/07/2023] Open
Abstract
Diffusion MRI (dMRI) provides invaluable information for the study of tissue microstructure and brain connectivity, but suffers from a range of imaging artifacts that greatly challenge the analysis of results and their interpretability if not appropriately accounted for. This review will cover dMRI artifacts and preprocessing steps, some of which have not typically been considered in existing pipelines or reviews, or have only gained attention in recent years: brain/skull extraction, B-matrix incompatibilities w.r.t the imaging data, signal drift, Gibbs ringing, noise distribution bias, denoising, between- and within-volumes motion, eddy currents, outliers, susceptibility distortions, EPI Nyquist ghosts, gradient deviations, B1 bias fields, and spatial normalization. The focus will be on “what’s new” since the notable advances prior to and brought by the Human Connectome Project (HCP), as presented in the predecessing issue on “Mapping the Connectome” in 2013. In addition to the development of novel strategies for dMRI preprocessing, exciting progress has been made in the availability of open source tools and reproducible pipelines, databases and simulation tools for the evaluation of preprocessing steps, and automated quality control frameworks, amongst others. Finally, this review will consider practical considerations and our view on “what’s next” in dMRI preprocessing.
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18
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Slator PJ, Palombo M, Miller KL, Westin C, Laun F, Kim D, Haldar JP, Benjamini D, Lemberskiy G, de Almeida Martins JP, Hutter J. Combined diffusion-relaxometry microstructure imaging: Current status and future prospects. Magn Reson Med 2021; 86:2987-3011. [PMID: 34411331 PMCID: PMC8568657 DOI: 10.1002/mrm.28963] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 06/25/2021] [Accepted: 07/20/2021] [Indexed: 12/15/2022]
Abstract
Microstructure imaging seeks to noninvasively measure and map microscopic tissue features by pairing mathematical modeling with tailored MRI protocols. This article reviews an emerging paradigm that has the potential to provide a more detailed assessment of tissue microstructure-combined diffusion-relaxometry imaging. Combined diffusion-relaxometry acquisitions vary multiple MR contrast encodings-such as b-value, gradient direction, inversion time, and echo time-in a multidimensional acquisition space. When paired with suitable analysis techniques, this enables quantification of correlations and coupling between multiple MR parameters-such as diffusivity, T 1 , T 2 , and T 2 ∗ . This opens the possibility of disentangling multiple tissue compartments (within voxels) that are indistinguishable with single-contrast scans, enabling a new generation of microstructural maps with improved biological sensitivity and specificity.
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Affiliation(s)
- Paddy J. Slator
- Centre for Medical Image ComputingDepartment of Computer ScienceUniversity College LondonLondonUK
| | - Marco Palombo
- Centre for Medical Image ComputingDepartment of Computer ScienceUniversity College LondonLondonUK
| | - Karla L. Miller
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUK
| | - Carl‐Fredrik Westin
- Department of RadiologyBrigham and Women’s HospitalHarvard Medical SchoolBostonMAUSA
| | - Frederik Laun
- Institute of RadiologyUniversity Hospital ErlangenFriedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
| | - Daeun Kim
- Ming Hsieh Department of Electrical and Computer EngineeringUniversity of Southern CaliforniaLos AngelesCAUSA
- Signal and Image Processing InstituteUniversity of Southern CaliforniaLos AngelesCAUSA
| | - Justin P. Haldar
- Ming Hsieh Department of Electrical and Computer EngineeringUniversity of Southern CaliforniaLos AngelesCAUSA
- Signal and Image Processing InstituteUniversity of Southern CaliforniaLos AngelesCAUSA
| | - Dan Benjamini
- The Eunice Kennedy Shriver National Institute of Child Health and Human DevelopmentBethesdaMDUSA
- The Center for Neuroscience and Regenerative MedicineUniformed Service University of the Health SciencesBethesdaMDUSA
| | | | - Joao P. de Almeida Martins
- Division of Physical Chemistry, Department of ChemistryLund UniversityLundSweden
- Department of Radiology and Nuclear MedicineSt. Olav’s University HospitalTrondheimNorway
| | - Jana Hutter
- Centre for Biomedical EngineeringSchool of Biomedical Engineering and ImagingKing’s College LondonLondonUK
- Centre for the Developing BrainSchool of Biomedical Engineering and ImagingKing’s College LondonLondonUK
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19
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Soustelle L, Troalen T, Hertanu A, Mchinda S, Ranjeva JP, Guye M, Varma G, Alsop DC, Duhamel G, Girard OM. A strategy to reduce the sensitivity of inhomogeneous magnetization transfer (ihMT) imaging to radiofrequency transmit field variations at 3 T. Magn Reson Med 2021; 87:1346-1359. [PMID: 34779020 DOI: 10.1002/mrm.29055] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 09/17/2021] [Accepted: 10/05/2021] [Indexed: 02/05/2023]
Abstract
PURPOSE To minimize the sensitivity of inhomogeneous magnetization transfer gradient-echo (ihMT-GRE) imaging to radiofrequency (RF) transmit field ( B 1 + ) inhomogeneities at 3 T. METHODS The ihMT-GRE sequence was optimized by varying the concentration of the RF saturation energy over time, obtained by increasing the saturation pulse power while extending the sequence repetition time (TR). Different protocols were tested using numerical simulations and human in vivo experiments in the brain white matter (WM) of healthy subjects at 3 T. The sensitivity of the ihMT ratio (ihMTR) to B 1 + variations was investigated by comparing measurements obtained at nominal transmitter adjustments and following a 20% global B 1 + drop. The resulting relative variations (δihMTR ) were evaluated voxelwise as a function of the local B 1 + distribution. The reproducibility of the protocol providing minimal B 1 + bias was assessed in a test-retest experiment. RESULTS In line with simulations, ihMT-GRE experiments conducted at high concentration of the RF energy over time demonstrated strong reduction of the B 1 + inhomogeneity effects in the human WM. Under the optimal conditions of 350-ms TR and 3-µT root mean square (RMS) saturation power, 73% of all WM voxels presented δihMTR below 10%. Reproducibility analysis yielded a close-to-zero systematic bias (ΔihMTR = -0.081%) and a high correlation (ρ² = 0.977) between test and retest experiments. CONCLUSION Concentrating RF saturation energy in ihMT-GRE sequences mitigates the sensitivity of the ihMTR to B 1 + variations and allows for clinical-ready ihMT imaging at 3 T. This feature is of particular interest for high and ultra-high field applications.
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Affiliation(s)
- Lucas Soustelle
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France.,APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
| | | | - Andreea Hertanu
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France.,APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
| | - Samira Mchinda
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France.,APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
| | - Jean-Philippe Ranjeva
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France.,APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
| | - Maxime Guye
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France.,APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
| | - Gopal Varma
- Division of MR Research, Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - David C Alsop
- Division of MR Research, Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Guillaume Duhamel
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France.,APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
| | - Olivier M Girard
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France.,APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
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20
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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 2nd, Weiskopf N, Wise RG, Wyss PO, Xu J. Generic acquisition protocol for quantitative MRI of the spinal cord. Nat Protoc 2021; 16:4611-32. [PMID: 34400839 DOI: 10.1038/s41596-021-00588-0] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [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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21
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Irimia A, Van Horn JD. Mapping the rest of the human connectome: Atlasing the spinal cord and peripheral nervous system. Neuroimage 2021; 225:117478. [PMID: 33160086 PMCID: PMC8485987 DOI: 10.1016/j.neuroimage.2020.117478] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 09/15/2020] [Accepted: 10/13/2020] [Indexed: 12/13/2022] Open
Abstract
The emergence of diffusion, structural, and functional neuroimaging methods has enabled major multi-site efforts to map the human connectome, which has heretofore been defined as containing all neural connections in the central nervous system (CNS). However, these efforts are not structured to examine the richness and complexity of the peripheral nervous system (PNS), which arguably forms the (neglected) rest of the connectome. Despite increasing interest in an atlas of the spinal cord (SC) and PNS which is simultaneously stereotactic, interactive, electronically dissectible, scalable, population-based and deformable, little attention has thus far been devoted to this task of critical importance. Nevertheless, the atlasing of these complete neural structures is essential for neurosurgical planning, neurological localization, and for mapping those components of the human connectome located outside of the CNS. Here we recommend a modification to the definition of the human connectome to include the SC and PNS, and argue for the creation of an inclusive atlas to complement current efforts to map the brain's human connectome, to enhance clinical education, and to assist progress in neuroscience research. In addition to providing a critical overview of existing neuroimaging techniques, image processing methodologies and algorithmic advances which can be combined for the creation of a full connectome atlas, we outline a blueprint for ultimately mapping the entire human nervous system and, thereby, for filling a critical gap in our scientific knowledge of neural connectivity.
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Affiliation(s)
- Andrei Irimia
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, 3715 McClintock Avenue, Los Angeles CA 90089, United States; Corwin D. Denney Research Center, Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, 1042 Downey Way, Los Angeles, CA 90089, United States.
| | - John Darrell Van Horn
- Department of Psychology, University of Virginia, 485 McCormick Road, Gilmer Hall, Room 102, Charlottesville, Virginia 22903, United States; School of Data Science, University of Virginia, Dell 1, Charlottesville, Virginia 22903, United States.
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22
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Ades-Aron B, Lemberskiy G, Veraart J, Golfinos J, Fieremans E, Novikov DS, Shepherd T. Improved Task-based Functional MRI Language Mapping in Patients with Brain Tumors through Marchenko-Pastur Principal Component Analysis Denoising. Radiology 2020; 298:365-373. [PMID: 33289611 DOI: 10.1148/radiol.2020200822] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Background Functional MRI improves preoperative planning in patients with brain tumors, but task-correlated signal intensity changes are only 2%-3% above baseline. This makes accurate functional mapping challenging. Marchenko-Pastur principal component analysis (MP-PCA) provides a novel strategy to separate functional MRI signal from noise without requiring user input or prior data representation. Purpose To determine whether MP-PCA denoising improves activation magnitude for task-based functional MRI language mapping in patients with brain tumors. Materials and Methods In this Health Insurance Portability and Accountability Act-compliant study, MP-PCA performance was first evaluated by using simulated functional MRI data with a known ground truth. Right-handed, left-language-dominant patients with brain tumors who successfully performed verb generation, sentence completion, and finger tapping functional MRI tasks were retrospectively identified between January 2017 and August 2018. On the group level, for each task, histograms of z scores for original and MP-PCA denoised data were extracted from relevant regions and contralateral homologs were seeded by a neuroradiologist blinded to functional MRI findings. Z scores were compared with paired two-sided t tests, and distributions were compared with effect size measurements and the Kolmogorov-Smirnov test. The number of voxels with a z score greater than 3 was used to measure task sensitivity relative to task duration. Results Twenty-three patients (mean age ± standard deviation, 43 years ± 18; 13 women) were evaluated. MP-PCA denoising led to a higher median z score of task-based functional MRI voxel activation in left hemisphere cortical regions for verb generation (from 3.8 ± 1.0 to 4.5 ± 1.4; P < .001), sentence completion (from 3.7 ± 1.0 to 4.3 ± 1.4; P < .001), and finger tapping (from 6.9 ± 2.4 to 7.9 ± 2.9; P < .001). Median z scores did not improve in contralateral homolog regions for verb generation (from -2.7 ± 0.54 to -2.5 ± 0.40; P = .90), sentence completion (from -2.3 ± 0.21 to -2.4 ± 0.37; P = .39), or finger tapping (from -2.3 ± 1.20 to -2.7 ± 1.40; P = .07). Individual functional MRI task durations could be truncated by at least 40% after MP-PCA without degradation of clinically relevant correlations between functional cortex and functional MRI tasks. Conclusion Denoising with Marchenko-Pastur principal component analysis led to higher task correlations in relevant cortical regions during functional MRI language mapping in patients with brain tumors. © RSNA, 2020 Online supplemental material is available for this article.
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Affiliation(s)
- Benjamin Ades-Aron
- From the Center for Biomedical Imaging, Department of Radiology (B.A.A., G.L., J.V., E.F., D.S.N., T.S.) and Department of Neurosurgery (J.G.), New York University School of Medicine, 2nd Floor, 660 First Ave, New York, NY 10016; and Department of Electrical and Computer Engineering, New York University Tandon School of Engineering, New York, NY (B.A.A.)
| | - Gregory Lemberskiy
- From the Center for Biomedical Imaging, Department of Radiology (B.A.A., G.L., J.V., E.F., D.S.N., T.S.) and Department of Neurosurgery (J.G.), New York University School of Medicine, 2nd Floor, 660 First Ave, New York, NY 10016; and Department of Electrical and Computer Engineering, New York University Tandon School of Engineering, New York, NY (B.A.A.)
| | - Jelle Veraart
- From the Center for Biomedical Imaging, Department of Radiology (B.A.A., G.L., J.V., E.F., D.S.N., T.S.) and Department of Neurosurgery (J.G.), New York University School of Medicine, 2nd Floor, 660 First Ave, New York, NY 10016; and Department of Electrical and Computer Engineering, New York University Tandon School of Engineering, New York, NY (B.A.A.)
| | - John Golfinos
- From the Center for Biomedical Imaging, Department of Radiology (B.A.A., G.L., J.V., E.F., D.S.N., T.S.) and Department of Neurosurgery (J.G.), New York University School of Medicine, 2nd Floor, 660 First Ave, New York, NY 10016; and Department of Electrical and Computer Engineering, New York University Tandon School of Engineering, New York, NY (B.A.A.)
| | - Els Fieremans
- From the Center for Biomedical Imaging, Department of Radiology (B.A.A., G.L., J.V., E.F., D.S.N., T.S.) and Department of Neurosurgery (J.G.), New York University School of Medicine, 2nd Floor, 660 First Ave, New York, NY 10016; and Department of Electrical and Computer Engineering, New York University Tandon School of Engineering, New York, NY (B.A.A.)
| | - Dmitry S Novikov
- From the Center for Biomedical Imaging, Department of Radiology (B.A.A., G.L., J.V., E.F., D.S.N., T.S.) and Department of Neurosurgery (J.G.), New York University School of Medicine, 2nd Floor, 660 First Ave, New York, NY 10016; and Department of Electrical and Computer Engineering, New York University Tandon School of Engineering, New York, NY (B.A.A.)
| | - Timothy Shepherd
- From the Center for Biomedical Imaging, Department of Radiology (B.A.A., G.L., J.V., E.F., D.S.N., T.S.) and Department of Neurosurgery (J.G.), New York University School of Medicine, 2nd Floor, 660 First Ave, New York, NY 10016; and Department of Electrical and Computer Engineering, New York University Tandon School of Engineering, New York, NY (B.A.A.)
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Chen Y, Haacke EM, Bernitsas E. Imaging of the Spinal Cord in Multiple Sclerosis: Past, Present, Future. Brain Sci 2020; 10:E857. [PMID: 33202821 DOI: 10.3390/brainsci10110857] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2020] [Revised: 10/30/2020] [Accepted: 11/11/2020] [Indexed: 11/17/2022] Open
Abstract
Spinal cord imaging in multiple sclerosis (MS) plays a significant role in diagnosing and tracking disease progression. The spinal cord is one of four key areas of the central nervous system where documenting the dissemination in space in the McDonald criteria for diagnosing MS. Spinal cord lesion load and the severity of cord atrophy are believed to be more relevant to disability than white matter lesions in the brain in different phenotypes of MS. Axonal loss contributes to spinal cord atrophy in MS and its degree correlates with disease severity and prognosis. Therefore, measures of axonal loss are often reliable biomarkers for monitoring disease progression. With recent technical advances, more and more qualitative and quantitative MRI techniques have been investigated in an attempt to provide objective and reliable diagnostic and monitoring biomarkers in MS. In this article, we discuss the role of spinal cord imaging in the diagnosis and prognosis of MS and, additionally, we review various techniques that may improve our understanding of the disease.
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