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Cagol A, Tsagkas C, Granziera C. Advanced Brain Imaging in Central Nervous System Demyelinating Diseases. Neuroimaging Clin N Am 2024; 34:335-357. [PMID: 38942520 DOI: 10.1016/j.nic.2024.03.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/30/2024]
Abstract
In recent decades, advances in neuroimaging have profoundly transformed our comprehension of central nervous system demyelinating diseases. Remarkable technological progress has enabled the integration of cutting-edge acquisition and postprocessing techniques, proving instrumental in characterizing subtle focal changes, diffuse microstructural alterations, and macroscopic pathologic processes. This review delves into state-of-the-art modalities applied to multiple sclerosis, neuromyelitis optica spectrum disorders, and myelin oligodendrocyte glycoprotein antibody-associated disease. Furthermore, it explores how this dynamic landscape holds significant promise for the development of effective and personalized clinical management strategies, encompassing support for differential diagnosis, prognosis, monitoring treatment response, and patient stratification.
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Affiliation(s)
- Alessandro Cagol
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Hegenheimermattweg 167b, 4123 Allschwil, Switzerland; Department of Neurology, University Hospital Basel, Petersgraben 4, 4031 Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Spitalstrasse 2, 4031 Basel, Switzerland; Department of Health Sciences, University of Genova, Via A. Pastore, 1 16132 Genova, Italy. https://twitter.com/CagolAlessandr0
| | - Charidimos Tsagkas
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Hegenheimermattweg 167b, 4123 Allschwil, Switzerland; Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health (NIH), 10 Center Drive, Bethesda, MD 20892, USA
| | - Cristina Granziera
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Hegenheimermattweg 167b, 4123 Allschwil, Switzerland; Department of Neurology, University Hospital Basel, Petersgraben 4, 4031 Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Spitalstrasse 2, 4031 Basel, Switzerland.
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Young G, Nguyen VS, Howlett-Prieto Q, Abuaf AF, Carroll TJ, Kawaji K, Javed A. T1 mapping from routine 3D T1-weighted inversion recovery sequences in clinical practice: comparison against reference inversion recovery fast field echo T1 scans and feasibility in multiple sclerosis. Neuroradiology 2024:10.1007/s00234-024-03400-4. [PMID: 38880824 DOI: 10.1007/s00234-024-03400-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2024] [Accepted: 06/06/2024] [Indexed: 06/18/2024]
Abstract
BACKGROUND AND PURPOSE Quantitative T1 mapping can be an essential tool for assessing tissue injury in multiple sclerosis (MS). We introduce T1-REQUIRE, a method that converts a single high-resolution anatomical 3D T1-weighted Turbo Field Echo (3DT1TFE) scan into a parametric T1 map that could be used for quantitative assessment of tissue damage. We present the accuracy and feasibility of this method in MS. METHODS 14 subjects with relapsing-remitting MS and 10 healthy subjects were examined. T1 maps were generated from 3DT1TFE images using T1-REQUIRE, which estimates T1 values using MR signal equations and internal tissue reference T1 values. Estimated T1 of lesions, white, and gray matter regions were compared with reference Inversion-Recovery Fast Field Echo T1 values and analyzed via correlation and Bland-Altman (BA) statistics. RESULTS 159 T1-weighted (T1W) hypointense MS lesions and 288 gray matter regions were examined. T1 values for MS lesions showed a Pearson's correlation of r = 0.81 (p < 0.000), R2 = 0.65, and Bias = 4.18%. BA statistics showed a mean difference of -53.95 ms and limits of agreement (LOA) of -344.20 and 236.30 ms. Non-lesional normal-appearing white matter had a correlation coefficient of r = 0.82 (p < 0.000), R2 = 0.67, Bias = 8.78%, mean difference of 73.87 ms, and LOA of -55.67 and 203.41 ms. CONCLUSIONS We demonstrate the feasibility of retroactively derived high-resolution T1 maps from routinely acquired anatomical images, which could be used to quantify tissue pathology in MS. The results of this study will set the stage for testing this method in larger clinical studies for examining MS disease activity and progression.
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Affiliation(s)
- Griffin Young
- Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Vivian S Nguyen
- Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Quentin Howlett-Prieto
- Department of Neurology and Rehabilitation, University of Illinois at Chicago, Chicago, IL, USA
| | | | - Timothy J Carroll
- Department of Radiology, The University of Chicago, Chicago, IL, USA
| | - Keigo Kawaji
- Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Adil Javed
- Department of Neurology, The University of Chicago, Chicago, IL, 5841 South Maryland Avenue, MC2030, 60637, USA.
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Bilgic B, Costagli M, Chan KS, Duyn J, Langkammer C, Lee J, Li X, Liu C, Marques JP, Milovic C, Robinson SD, Schweser F, Shmueli K, Spincemaille P, Straub S, van Zijl P, Wang Y. Recommended implementation of quantitative susceptibility mapping for clinical research in the brain: A consensus of the ISMRM electro-magnetic tissue properties study group. Magn Reson Med 2024; 91:1834-1862. [PMID: 38247051 PMCID: PMC10950544 DOI: 10.1002/mrm.30006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 10/31/2023] [Accepted: 12/14/2023] [Indexed: 01/23/2024]
Abstract
This article provides recommendations for implementing QSM for clinical brain research. It is a consensus of the International Society of Magnetic Resonance in Medicine, Electro-Magnetic Tissue Properties Study Group. While QSM technical development continues to advance rapidly, the current QSM methods have been demonstrated to be repeatable and reproducible for generating quantitative tissue magnetic susceptibility maps in the brain. However, the many QSM approaches available have generated a need in the neuroimaging community for guidelines on implementation. This article outlines considerations and implementation recommendations for QSM data acquisition, processing, analysis, and publication. We recommend that data be acquired using a monopolar 3D multi-echo gradient echo (GRE) sequence and that phase images be saved and exported in Digital Imaging and Communications in Medicine (DICOM) format and unwrapped using an exact unwrapping approach. Multi-echo images should be combined before background field removal, and a brain mask created using a brain extraction tool with the incorporation of phase-quality-based masking. Background fields within the brain mask should be removed using a technique based on SHARP or PDF, and the optimization approach to dipole inversion should be employed with a sparsity-based regularization. Susceptibility values should be measured relative to a specified reference, including the common reference region of the whole brain as a region of interest in the analysis. The minimum acquisition and processing details required when reporting QSM results are also provided. These recommendations should facilitate clinical QSM research and promote harmonized data acquisition, analysis, and reporting.
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Affiliation(s)
- Berkin Bilgic
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA
| | - Mauro Costagli
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Sciences (DINOGMI), University of Genoa, Genoa, Italy
- Laboratory of Medical Physics and Magnetic Resonance, IRCCS Stella Maris, Pisa, Italy
| | - Kwok-Shing Chan
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Jeff Duyn
- Advanced MRI Section, NINDS, National Institutes of Health, Bethesda, Maryland, USA
| | | | - Jongho Lee
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea
| | - Xu Li
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Chunlei Liu
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California, USA
- Helen Wills Neuroscience Institute, University of California, Berkeley, California, USA
| | - José P Marques
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Carlos Milovic
- School of Electrical Engineering (EIE), Pontificia Universidad Catolica de Valparaiso, Valparaiso, Chile
| | - Simon Daniel Robinson
- High Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
- Centre of Advanced Imaging, University of Queensland, Brisbane, Australia
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences at the University at Buffalo, Buffalo, New York, USA
- Center for Biomedical Imaging, Clinical and Translational Science Institute at the University at Buffalo, Buffalo, New York, USA
| | - Karin Shmueli
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Pascal Spincemaille
- MRI Research Institute, Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Sina Straub
- Department of Radiology, Mayo Clinic, Jacksonville, Florida, USA
| | - Peter van Zijl
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Yi Wang
- MRI Research Institute, Departments of Radiology and Biomedical Engineering, Cornell University, New York, New York, USA
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Malmberg MA, Odéen H, Hofstetter LW, Hadley JR, Parker DL. Validation of single reference variable flip angle (SR-VFA) dynamic T 1 mapping with T 2 * correction using a novel rotating phantom. Magn Reson Med 2024; 91:1419-1433. [PMID: 38115639 PMCID: PMC10872756 DOI: 10.1002/mrm.29944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 10/12/2023] [Accepted: 11/09/2023] [Indexed: 12/21/2023]
Abstract
PURPOSE To validate single reference variable flip angle (SR-VFA) dynamic T1 mapping with and without T2 * correction against inversion recovery (IR) T1 measurements. METHODS A custom cylindrical phantom with three concentric compartments was filled with variably doped agar to produce a smooth spatial gradient of the T1 relaxation rate as a function of angle across each compartment. IR T1 , VFA T1 , and B1 + measurements were made on the phantom before rotation, and multi-echo stack-of-radial dynamic images were acquired during rotation via an MRI-compatible motor. B1 + -corrected SR-VFA and SR-VFA-T2 * T1 maps were computed from the sliding window reconstructed images and compared against rotationally registered IR and VFA T1 maps to determine the percentage error. RESULTS Both VFA and SR-VFA-T2 * T1 maps fell within 10% of IR T1 measurements for a low rotational speed, with a mean accuracy of 2.3% ± 2.6% and 2.8% ± 2.6%, respectively. Increasing rotational speed was found to decrease the accuracy due to increasing temporal smoothing over ranges where the T1 change had a nonconstant slope. SR-VFA T1 mapping was found to have similar accuracy as the SR-VFA-T2 * and VFA methods at low TEs (˜<2 ms), whereas accuracy degraded strongly with later TEs. T2 * correction of the SR-VFA T1 maps was found to consistently improve accuracy and precision, especially at later TEs. CONCLUSION SR-VFA-T2 * dynamic T1 mapping was found to be accurate against reference IR T1 measurements within 10% in an agar phantom. Further validation is needed in mixed fat-water phantoms and in vivo.
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Affiliation(s)
- Michael A. Malmberg
- Department of Bioengineering, University of Utah, Salt Lake City, UT, USA
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT, USA
| | - Henrik Odéen
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT, USA
| | | | - J. Rock Hadley
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT, USA
| | - Dennis L. Parker
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT, USA
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Ravano V, Piredda GF, Krasensky J, Andelova M, Uher T, Srpova B, Havrdova EK, Vodehnalova K, Horakova D, Nytrova P, Disselhorst JA, Hilbert T, Maréchal B, Thiran JP, Kober T, Richiardi J, Vaneckova M. Tract-wise microstructural analysis informs on current and future disability in early multiple sclerosis. J Neurol 2024; 271:631-641. [PMID: 37819462 PMCID: PMC10827809 DOI: 10.1007/s00415-023-12023-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 09/21/2023] [Accepted: 09/24/2023] [Indexed: 10/13/2023]
Abstract
OBJECTIVES Microstructural characterization of patients with multiple sclerosis (MS) has been shown to correlate better with disability compared to conventional radiological biomarkers. Quantitative MRI provides effective means to characterize microstructural brain tissue changes both in lesions and normal-appearing brain tissue. However, the impact of the location of microstructural alterations in terms of neuronal pathways has not been thoroughly explored so far. Here, we study the extent and the location of tissue changes probed using quantitative MRI along white matter (WM) tracts extracted from a connectivity atlas. METHODS We quantified voxel-wise T1 tissue alterations compared to normative values in a cohort of 99 MS patients. For each WM tract, we extracted metrics reflecting tissue alterations both in lesions and normal-appearing WM and correlated these with cross-sectional disability and disability evolution after 2 years. RESULTS In early MS patients, T1 alterations in normal-appearing WM correlated better with disability evolution compared to cross-sectional disability. Further, the presence of lesions in supratentorial tracts was more strongly associated with cross-sectional disability, while microstructural alterations in infratentorial pathways yielded higher correlations with disability evolution. In progressive patients, all major WM pathways contributed similarly to explaining disability, and correlations with disability evolution were generally poor. CONCLUSIONS We showed that microstructural changes evaluated in specific WM pathways contribute to explaining future disability in early MS, hence highlighting the potential of tract-wise analyses in monitoring disease progression. Further, the proposed technique allows to estimate WM tract-specific microstructural characteristics in clinically compatible acquisition times, without the need for advanced diffusion imaging.
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Affiliation(s)
- Veronica Ravano
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland.
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
| | - Gian Franco Piredda
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Jan Krasensky
- Department of Radiology, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - Michaela Andelova
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - Tomas Uher
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - Barbora Srpova
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - Eva Kubala Havrdova
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - Karolina Vodehnalova
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - Dana Horakova
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - Petra Nytrova
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - Jonathan A Disselhorst
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Tom Hilbert
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Bénédicte Maréchal
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Jean-Philippe Thiran
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Tobias Kober
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Jonas Richiardi
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Manuela Vaneckova
- Department of Radiology, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
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Nabizadeh F, Zafari R, Mohamadi M, Maleki T, Fallahi MS, Rafiei N. MRI features and disability in multiple sclerosis: A systematic review and meta-analysis. J Neuroradiol 2024; 51:24-37. [PMID: 38172026 DOI: 10.1016/j.neurad.2023.11.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Revised: 11/28/2023] [Accepted: 11/28/2023] [Indexed: 01/05/2024]
Abstract
BACKGROUND In this systematic review and meta-analysis, we aimed to investigate the correlation between disability in patients with Multiple sclerosis (MS) measured by the Expanded Disability Status Scale (EDSS) and brain Magnetic Resonance Imaging (MRI) features to provide reliable results on which characteristics in the MRI can predict disability and prognosis of the disease. METHODS A systematic literature search was performed using three databases including PubMed, Scopus, and Web of Science. The selected peer-reviewed studies must report a correlation between EDSS scores and MRI features. The correlation coefficients of included studies were converted to the Fisher's z scale, and the results were pooled. RESULTS Overall, 105 studies A total of 16,613 patients with MS entered our study. We found no significant correlation between total brain volume and EDSS assessment (95 % CI: -0.37 to 0.08; z-score: -0.15). We examined the potential correlation between the volume of T1 and T2 lesions and the level of disability. A positive significant correlation was found (95 % CI: 0.19 to 0.43; z-score: 0.31), (95 % CI: 0.17 to 0.33; z-score: 0.25). We observed a significant correlation between white matter volume and EDSS score in patients with MS (95 % CI: -0.37 to -0.03; z-score: -0.21). Moreover, there was a significant negative correlation between gray matter volume and disability (95 % CI: -0.025 to -0.07; z-score: -0.16). CONCLUSION In conclusion, this systematic review and meta-analysis revealed that disability in patients with MS is linked to extensive changes in different brain regions, encompassing gray and white matter, as well as T1 and T2 weighted MRI lesions.
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Affiliation(s)
- Fardin Nabizadeh
- School of Medicine, Iran University of Medical Sciences, Tehran, Iran.
| | - Rasa Zafari
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Mobin Mohamadi
- School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Tahereh Maleki
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Nazanin Rafiei
- School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
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Eajazi A, Weinschenk C, Chhabra A. Imaging Biomarkers of Peripheral Nerves: Focus on Magnetic Resonance Neurography and Ultrasonography. Semin Musculoskelet Radiol 2024; 28:92-102. [PMID: 38330973 DOI: 10.1055/s-0043-1776427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2024]
Abstract
Peripheral neuropathy is a prevalent and debilitating condition affecting millions of individuals globally. Magnetic resonance neurography (MRN) and ultrasonography (US) are noninvasive methods offering comprehensive visualization of peripheral nerves, using anatomical and functional imaging biomarkers to ensure accurate evaluation. For optimized MRN, superior and high-resolution two-dimensional and three-dimensional imaging protocols are essential. The anatomical MRN and US imaging markers include quantitative measures of nerve and fascicular size and signal, and qualitative markers of course and morphology. Among them, quantitative markers of T2-signal intensity ratio are sensitive to nerve edema-like signal changes, and the T1-mapping technique reveals nerve and muscle tissue fatty and fibrous compositional alterations.The functional markers are derived from physiologic properties of nerves, such as diffusion characteristics or blood flow. They include apparent diffusion coefficient from diffusion-weighted imaging and fractional anisotropy and tractography from diffusion tensor imaging to delve into peripheral nerve microstructure and integrity. Peripheral nerve perfusion using dynamic contrast-enhanced magnetic resonance imaging estimates perfusion parameters, offering insights into nerve health and neuropathies involving edema, inflammation, demyelination, and microvascular alterations in conditions like type 2 diabetes, linking nerve conduction pathophysiology to vascular permeability alterations.Imaging biomarkers thus play a pivotal role in the diagnosis, prognosis, and monitoring of nerve pathologies, thereby ensuring comprehensive assessment and elevating patient care. These biomarkers provide valuable insights into nerve structure, function, and pathophysiology, contributing to the accurate diagnosis and management planning for peripheral neuropathy.
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Affiliation(s)
- Alireza Eajazi
- Department of Radiology, UT Southwestern Medical Center, Dallas, Texas
| | - Cindy Weinschenk
- Department of Radiology, UT Southwestern Medical Center, Dallas, Texas
| | - Avneesh Chhabra
- Department of Radiology, UT Southwestern Medical Center, Dallas, Texas
- Department of Radiology & Orthopedic Surgery, UT Southwestern Medical Center, Dallas, Texas
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Zampini MA, Sijbers J, Verhoye M, Garipov R. A preparation pulse for fast steady state approach in Actual Flip angle Imaging. Med Phys 2024; 51:306-318. [PMID: 37480220 DOI: 10.1002/mp.16624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 05/26/2023] [Accepted: 06/20/2023] [Indexed: 07/23/2023] Open
Abstract
BACKGROUND Actual Flip angle Imaging (AFI) is a sequence used for B1 mapping, also embedded in the Variable flip angle with AFI for simultaneous estimation of T1 , B1 and equilibrium magnetization. PURPOSE To investigate the design of a preparation module for AFI to allow a fast approach to steady state (SS) without requiring the use of dummy acquisitions. METHODS The features of a preparation module with a B1 insensitive adiabatic pulse, spoiler gradients, and a recovery timeT r e c $T_{rec}$ were studied with simulations and validated via experiments and acquired with different k-space traveling strategies. The robustness of the flip angle of the preparation pulse on the acquired signal is studied. RESULTS When a 90° adiabatic pulse is used, the forthcomingT r e c $T_{rec}$ can be expressed as a function of repetition times and AFI flip angle only asTR 1 ( n + cos α ) / ( 1 - cos 2 α ) $\mathrm{TR_1}(n+\cos \alpha )/(1-\cos ^2\alpha )$ , where n represents the ratio between the two repetition times of AFI. The robustness of the method is demonstrated by showing that using the values further away from 90° still allows for a faster approach to SS than the use of dummy pulses. CONCLUSIONS The preparation module is particularly advantageous for low flip angles, as well as for AFI sequences that sample the center of the k-space early in the sequence, such as centric ordering acquisitions, and for ultrafast EPI-based AFI methods, thus allowing to reduce scanner overhead time.
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Affiliation(s)
- Marco Andrea Zampini
- MR Solutions Ltd., Ashbourne House, Guildford, Surrey, UK
- Bio-Imaging Lab, Department of Biomedical Sciences, University of Antwerp, Belgium
| | - Jan Sijbers
- imec-Vision Lab, Department of Physics, University of Antwerp, Belgium
- μNEURO Research Centre of Excellence, University of Antwerp, Belgium
| | - Marleen Verhoye
- Bio-Imaging Lab, Department of Biomedical Sciences, University of Antwerp, Belgium
- μNEURO Research Centre of Excellence, University of Antwerp, Belgium
| | - Ruslan Garipov
- MR Solutions Ltd., Ashbourne House, Guildford, Surrey, UK
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Zhang T, Zhao Y, Jin W, Li Y, Guo R, Ke Z, Luo J, Li Y, Liang ZP. B 1 mapping using pre-learned subspaces for quantitative brain imaging. Magn Reson Med 2023; 90:2089-2101. [PMID: 37345702 DOI: 10.1002/mrm.29764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 05/24/2023] [Accepted: 05/30/2023] [Indexed: 06/23/2023]
Abstract
PURPOSE To develop a machine learning-based method for estimation of both transmitter and receiver B1 fields desired for correction of the B1 inhomogeneity effects in quantitative brain imaging. THEORY AND METHODS A subspace model-based machine learning method was proposed for estimation of B1t and B1r fields. Probabilistic subspace models were used to capture scan-dependent variations in the B1 fields; the subspace basis and coefficient distributions were learned from pre-scanned training data. Estimation of the B1 fields for new experimental data was achieved by solving a linear optimization problem with prior distribution constraints. We evaluated the performance of the proposed method for B1 inhomogeneity correction in quantitative brain imaging scenarios, including T1 and proton density (PD) mapping from variable-flip-angle spoiled gradient-echo (SPGR) data as well as neurometabolic mapping from MRSI data, using phantom, healthy subject and brain tumor patient data. RESULTS In both phantom and healthy subject data, the proposed method produced high-quality B1 maps. B1 correction on SPGR data using the estimated B1 maps produced significantly improved T1 and PD maps. In brain tumor patients, the proposed method produced more accurate and robust B1 estimation and correction results than conventional methods. The B1 maps were also applied to MRSI data from tumor patients and produced improved neurometabolite maps, with better separation between pathological and normal tissues. CONCLUSION This work presents a novel method to estimate B1 variations using probabilistic subspace models and machine learning. The proposed method may make correction of B1 inhomogeneity effects more robust in practical applications.
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Affiliation(s)
- Tianxiao Zhang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yibo Zhao
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Wen Jin
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Yudu Li
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Rong Guo
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Siemens Medical Solutions USA, Inc., Urbana, Illinois, USA
| | - Ziwen Ke
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Jie Luo
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yao Li
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Zhi-Pei Liang
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
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Maiworm M, Hamid C, Wagner M, Nöth U, Deichmann R, Seiler A, Gracien RM. Multiparametric quantitative MRI reveals progressive cortical damage over time in clinically stable relapsing-remitting MS. J Neurol Neurosurg Psychiatry 2023; 94:786-791. [PMID: 37169544 DOI: 10.1136/jnnp-2022-330894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 04/17/2023] [Indexed: 05/13/2023]
Abstract
BACKGROUND In relapsing-remitting multiple sclerosis (RRMS), cortical grey matter pathology relevantly contributes to long-term disability. Still, diffuse cortical inflammation cannot be detected with conventional MRI. OBJECTIVE We aimed to assess microstructural damage of cortical grey matter over time and the relation to clinical disability as well as relapse activity in patients with RRMS using multiparametric quantitative (q)MRI techniques. METHODS On 40 patients with RRMS and 33 age-matched and sex-matched healthy controls, quantitative T1, T2, T2* and proton density (PD) mapping was performed at baseline and follow-up after 2 years. Cortical qMRI parameter values were extracted with the FreeSurfer software using a surface-based approach. QMRI parameters, cortical thickness and white matter lesion (WML) load, as well as Expanded Disability Status Scale (EDSS) and relapse rate, were compared between time points. RESULTS Over 2 years, significant increases of T1 (p≤0.001), PD (p≤0.001) and T2 (p=0.005) values were found in the patient, but not in the control group. At decreased relapse rate over time (p=0.001), cortical thickness, WML volume and EDSS remained unchanged. CONCLUSION Despite clinical stability, cortical T1, T2 and PD values increased over time, indicating progressive demyelination and increasing water content. These parameters represent promising surrogate parameters of diffuse cortical inflammation in RRMS.
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Affiliation(s)
- Michelle Maiworm
- Department of Neurology, University Hospital Frankfurt, Frankfurt am Main, Germany
- Brain Imaging Center, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Celona Hamid
- Department of Neurology, University Hospital Frankfurt, Frankfurt am Main, Germany
- Brain Imaging Center, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Marlies Wagner
- Brain Imaging Center, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Ulrike Nöth
- Brain Imaging Center, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Ralf Deichmann
- Brain Imaging Center, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Alexander Seiler
- Department of Neurology, University Hospital Frankfurt, Frankfurt am Main, Germany
- Brain Imaging Center, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - René-Maxime Gracien
- Department of Neurology, University Hospital Frankfurt, Frankfurt am Main, Germany
- Brain Imaging Center, Goethe University Frankfurt, Frankfurt am Main, Germany
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11
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Wearn A, Raket LL, Collins DL, Spreng RN. Longitudinal changes in hippocampal texture from healthy aging to Alzheimer's disease. Brain Commun 2023; 5:fcad195. [PMID: 37465755 PMCID: PMC10351670 DOI: 10.1093/braincomms/fcad195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Revised: 05/09/2023] [Accepted: 07/04/2023] [Indexed: 07/20/2023] Open
Abstract
Early detection of Alzheimer's disease is essential to develop preventive treatment strategies. Detectible change in brain volume emerges relatively late in the pathogenic progression of disease, but microstructural changes caused by early neuropathology may cause subtle changes in the MR signal, quantifiable using texture analysis. Texture analysis quantifies spatial patterns in an image, such as smoothness, randomness and heterogeneity. We investigated whether the MRI texture of the hippocampus, an early site of Alzheimer's disease pathology, is sensitive to changes in brain microstructure before the onset of cognitive impairment. We also explored the longitudinal trajectories of hippocampal texture across the Alzheimer's continuum in relation to hippocampal volume and other biomarkers. Finally, we assessed the ability of texture to predict future cognitive decline, over and above hippocampal volume. Data were acquired from the Alzheimer's Disease Neuroimaging Initiative. Texture was calculated for bilateral hippocampi on 3T T1-weighted MRI scans. Two hundred and ninety-three texture features were reduced to five principal components that described 88% of total variance within cognitively unimpaired participants. We assessed cross-sectional differences in these texture components and hippocampal volume between four diagnostic groups: cognitively unimpaired amyloid-β- (n = 406); cognitively unimpaired amyloid-β+ (n = 213); mild cognitive impairment amyloid-β+ (n = 347); and Alzheimer's disease dementia amyloid-β+ (n = 202). To assess longitudinal texture change across the Alzheimer's continuum, we used a multivariate mixed-effects spline model to calculate a 'disease time' for all timepoints based on amyloid PET and cognitive scores. This was used as a scale on which to compare the trajectories of biomarkers, including volume and texture of the hippocampus. The trajectories were modelled in a subset of the data: cognitively unimpaired amyloid-β- (n = 345); cognitively unimpaired amyloid-β+ (n = 173); mild cognitive impairment amyloid-β+ (n = 301); and Alzheimer's disease dementia amyloid-β+ (n = 161). We identified a difference in texture component 4 at the earliest stage of Alzheimer's disease, between cognitively unimpaired amyloid-β- and cognitively unimpaired amyloid-β+ older adults (Cohen's d = 0.23, Padj = 0.014). Differences in additional texture components and hippocampal volume emerged later in the disease continuum alongside the onset of cognitive impairment (d = 0.30-1.22, Padj < 0.002). Longitudinal modelling of the texture trajectories revealed that, while most elements of texture developed over the course of the disease, noise reduced sensitivity for tracking individual textural change over time. Critically, however, texture provided additional information than was provided by volume alone to more accurately predict future cognitive change (d = 0.32-0.63, Padj < 0.0001). Our results support the use of texture as a measure of brain health, sensitive to Alzheimer's disease pathology, at a time when therapeutic intervention may be most effective.
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Affiliation(s)
- Alfie Wearn
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, QC, Canada H3A 2B4
| | - Lars Lau Raket
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund SE-221 00, Sweden
- Novo Nordisk A/S, Søborg 2860, Denmark
| | - D Louis Collins
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, QC, Canada H3A 2B4
- McConnell Brain Imaging Centre, McGill University, Montreal, QC, Canada H3A 2B4
| | - R Nathan Spreng
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, QC, Canada H3A 2B4
- McConnell Brain Imaging Centre, McGill University, Montreal, QC, Canada H3A 2B4
- Departments of Psychology and Psychiatry, McGill University, Montreal, QC, Canada H3A 2B4
- Douglas Mental Health University Institute, Verdun, QC, Canada H4H 1R3
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12
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Oost W, Huitema AJ, Kats K, Giepmans BNG, Kooistra SM, Eggen BJL, Baron W. Pathological ultrastructural alterations of myelinated axons in normal appearing white matter in progressive multiple sclerosis. Acta Neuropathol Commun 2023; 11:100. [PMID: 37340488 DOI: 10.1186/s40478-023-01598-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 06/02/2023] [Indexed: 06/22/2023] Open
Abstract
Multiple sclerosis (MS) pathophysiology includes inflammation, demyelination and neurodegeneration, but the exact mechanisms of disease initiation and progression are unknown. A major feature of lesions is lack of myelin, which increases axonal energy demand and requires adaptation in number and size of mitochondria. Outside lesions, subtle and diffuse alterations are observed in normal appearing white matter (NAWM) and normal appearing grey matter (NAGM), including increased oxidative stress, reduced axon density and changes in myelin composition and morphology. On an ultrastructural level, only limited data is available on alterations in myelinated axons. We generated large scale 2D scanning transmission electron microscopy images ('nanotomy') of non-demyelinated brain tissue of control and progressive MS donors, accessible via an open-access online repository. We observed a reduced density of myelinated axons in NAWM, without a decrease in cross-sectional axon area. Small myelinated axons were less frequently and large myelinated axons were more frequently present in NAWM, while the g-ratio was similar. The correlation between axonal mitochondrial radius and g-ratio was lost in NAWM, but not in NAGM. Myelinated axons in control GM and NAGM had a similar g-ratio and radius distribution. We hypothesize that axonal loss in NAWM is likely compensated by swelling of the remaining myelinated axons and subsequent adjustment of myelin thickness to maintain their g-ratio. Failure of axonal mitochondria to adjust their size and fine-tuning of myelin thickness may render NAWM axons and their myelin more susceptible to injury.
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Affiliation(s)
- Wendy Oost
- Department of Biomedical Sciences of Cells and Systems, Section Molecular Neurobiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- MS Center Noord Nederland, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Allard J Huitema
- Department of Biomedical Sciences of Cells and Systems, Section Molecular Neurobiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- MS Center Noord Nederland, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Kim Kats
- Department of Biomedical Sciences of Cells and Systems, Section Molecular Neurobiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Ben N G Giepmans
- Department of Biomedical Sciences of Cells and Systems, Section Molecular Neurobiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Susanne M Kooistra
- Department of Biomedical Sciences of Cells and Systems, Section Molecular Neurobiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- MS Center Noord Nederland, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Bart J L Eggen
- Department of Biomedical Sciences of Cells and Systems, Section Molecular Neurobiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- MS Center Noord Nederland, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Wia Baron
- Department of Biomedical Sciences of Cells and Systems, Section Molecular Neurobiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
- MS Center Noord Nederland, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
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13
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Straub S, El-Sanosy E, Emmerich J, Sandig FL, Ladd ME, Schlemmer HP. Quantitative magnetic resonance imaging biomarkers for cortical pathology in multiple sclerosis at 7 T. NMR IN BIOMEDICINE 2023; 36:e4847. [PMID: 36259249 DOI: 10.1002/nbm.4847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 10/08/2022] [Accepted: 10/12/2022] [Indexed: 06/16/2023]
Abstract
Substantial cortical gray matter tissue damage, which correlates with clinical disease severity, has been revealed in multiple sclerosis (MS) using advanced magnetic resonance imaging (MRI) methods at 3 T and the use of ultra-high field, as well as in histopathology studies. While clinical assessment mainly focuses on lesions using T 1 - and T 2 -weighted MRI, quantitative MRI (qMRI) methods are capable of uncovering subtle microstructural changes. The aim of this ultra-high field study is to extract possible future MR biomarkers for the quantitative evaluation of regional cortical pathology. Because of their sensitivity to iron, myelin, and in part specifically to cortical demyelination, T 1 , T 2 , R 2 * , and susceptibility mapping were performed including two novel susceptibility markers; in addition, cortical thickness as well as the volumes of 34 cortical regions were computed. Data were acquired in 20 patients and 16 age- and sex-matched healthy controls. In 18 cortical regions, large to very large effect sizes (Cohen's d ≥ 1) and statistically significant differences in qMRI values between patients and controls were revealed compared with only four regions when using more standard MR measures, namely, volume and cortical thickness. Moreover, a decrease in all susceptibility contrasts ( χ , χ + , χ - ) and R 2 * values indicates that the role of cortical demyelination might outweigh inflammatory processes in the form of iron accumulation in cortical MS pathology, and might also indicate iron loss. A significant association between susceptibility contrasts as well as R 2 * of the caudal middle frontal gyrus and disease duration was found (adjusted R2 : 0.602, p = 0.0011). Quantitative MRI parameters might be more sensitive towards regional cortical pathology compared with the use of conventional markers only and therefore may play a role in early detection of tissue damage in MS in the future.
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Affiliation(s)
- Sina Straub
- Division of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Faculty of Medicine, Heidelberg University, Heidelberg, Germany
| | - Edris El-Sanosy
- Division Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Julian Emmerich
- Division of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Frederik L Sandig
- Division Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Mark E Ladd
- Division of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Faculty of Medicine, Heidelberg University, Heidelberg, Germany
- Faculty of Physics and Astronomy, Heidelberg University, Heidelberg, Germany
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14
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Chen X, Schädelin S, Lu PJ, Ocampo-Pineda M, Weigel M, Barakovic M, Ruberte E, Cagol A, Marechal B, Kober T, Kuhle J, Kappos L, Melie-Garcia L, Granziera C. Personalized maps of T1 relaxometry abnormalities provide correlates of disability in multiple sclerosis patients. Neuroimage Clin 2023; 37:103349. [PMID: 36801600 PMCID: PMC9958406 DOI: 10.1016/j.nicl.2023.103349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Revised: 02/08/2023] [Accepted: 02/10/2023] [Indexed: 02/16/2023]
Abstract
OBJECTIVES AND AIMS Quantitative MRI (qMRI) has greatly improved the sensitivity and specificity of microstructural brain pathology in multiple sclerosis (MS) when compared to conventional MRI (cMRI). More than cMRI, qMRI also provides means to assess pathology within the normal-appearing and lesion tissue. In this work, we further developed a method providing personalized quantitative T1 (qT1) abnormality maps in individual MS patients by modeling the age dependence of qT1 alterations. In addition, we assessed the relationship between qT1 abnormality maps and patients' disability, in order to evaluate the potential value of this measurement in clinical practice. METHODS We included 119 MS patients (64 relapsing-remitting MS (RRMS), 34 secondary progressive MS (SPMS), 21 primary progressive MS (PPMS)), and 98 Healthy Controls (HC). All individuals underwent 3T MRI examinations, including Magnetization Prepared 2 Rapid Acquisition Gradient Echoes (MP2RAGE) for qT1 maps and High-Resolution 3D Fluid Attenuated Inversion Recovery (FLAIR) imaging. To calculate personalized qT1 abnormality maps, we compared qT1 in each brain voxel in MS patients to the average qT1 obtained in the same tissue (grey/white matter) and region of interest (ROI) in healthy controls, hereby providing individual voxel-based Z-score maps. The age dependence of qT1 in HC was modeled using linear polynomial regression. We computed the average qT1 Z-scores in white matter lesions (WMLs), normal-appearing white matter (NAWM), cortical grey matter lesions (GMcLs) and normal-appearing cortical grey matter (NAcGM). Lastly, a multiple linear regression (MLR) model with the backward selection including age, sex, disease duration, phenotype, lesion number, lesion volume and average Z-score (NAWM/NAcGM/WMLs/GMcLs) was used to assess the relationship between qT1 measures and clinical disability (evaluated with EDSS). RESULTS The average qT1 Z-score was higher in WMLs than in NAWM. (WMLs: 1.366 ± 0.409, NAWM: -0.133 ± 0.288, [mean ± SD], p < 0.001). The average Z-score in NAWM in RRMS patients was significantly lower than in PPMS patients (p = 0.010). The MLR model showed a strong association between average qT1 Z-scores in white matter lesions (WMLs) and EDSS (R2 = 0.549, β = 0.178, 97.5 % CI = 0.030 to 0.326, p = 0.019). Specifically, we measured a 26.9 % increase in EDSS per unit of qT1 Z-score in WMLs in RRMS patients (R2 = 0.099, β = 0.269, 97.5 % CI = 0.078 to 0.461, p = 0.007). CONCLUSIONS We showed that personalized qT1 abnormality maps in MS patients provide measures related to clinical disability, supporting the use of those maps in clinical practice.
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Affiliation(s)
- Xinjie Chen
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Sabine Schädelin
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Po-Jui Lu
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Mario Ocampo-Pineda
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Matthias Weigel
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland; Division of Radiological Physics, Department of Radiology, University Hospital Basel, Basel, Switzerland
| | - Muhamed Barakovic
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Esther Ruberte
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Alessandro Cagol
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Benedicte Marechal
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland
| | - Tobias Kober
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland
| | - Jens Kuhle
- Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Ludwig Kappos
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Lester Melie-Garcia
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Cristina Granziera
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland.
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15
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Ferraro PM, Gualco L, Costagli M, Schiavi S, Ponzano M, Signori A, Massa F, Pardini M, Castellan L, Levrero F, Zacà D, Piredda GF, Hilbert T, Kober T, Roccatagliata L. Compressed sensing (CS) MP2RAGE versus standard MPRAGE: A comparison of derived brain volume measurements. Phys Med 2022; 103:166-174. [DOI: 10.1016/j.ejmp.2022.10.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 09/16/2022] [Accepted: 10/23/2022] [Indexed: 11/11/2022] Open
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16
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Jia F, Liao Y, Li X, Ye Z, Li P, Zhou X, Li Q, Wang S, Ning G, Qu H. Preliminary Study on Quantitative Assessment of the Fetal Brain Using MOLLI T1 Mapping Sequence. J Magn Reson Imaging 2022; 56:1505-1512. [PMID: 35394092 DOI: 10.1002/jmri.28195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 03/28/2022] [Accepted: 03/29/2022] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Prenatal quantitative evaluation of myelin is important. However, few techniques are suitable for the quantitative evaluation of fetal myelination. PURPOSE To optimize a modified Look-Locker inversion recovery (MOLLI) T1 mapping sequence for fetal brain development study. STUDY TYPE Prospective observational preliminary cohort study. POPULATION A total of 71 women with normal fetuses divided into mid-pregnancy (gestational age 24-28 weeks, N = 25) and late pregnancy (gestational age > 28 weeks, N = 46) groups. FIELD STRENGTH/SEQUENCE A 3 T/MOLLI sequence. ASSESSMENT T1 values were measured in pedunculus cerebri, basal ganglia, thalamus, posterior limb of the internal capsule, temporal white matter, occipital white matter, frontal white matter, and parietal white matter by two radiologists (11 and 16 years of experience, respectively). STATISTICAL TESTS The Kruskal-Wallis test was used for reginal comparison. For each region of interest (ROI), differences in T1 values between the mid and late pregnancy groups were assessed by the Mann Whitney U test. Pearson correlation coefficients (r) were used to evaluate the correlations between T1 values and gestational age for each ROI. Intraobserver and interobserver agreement was determined by the intraclass correlation coefficient (ICC). A P value <0.05 was considered statistically significant. RESULTS Interobserver and intraobserver agreements of T1 were good for all ROIs (all ICCs > 0.700). There were significant differences in T1 values between lobal white matter and deep regions, respectively. Significant T1 values differences were found between middle and late pregnancy groups in pedunculus cerebri, basal ganglion, thalamus, posterior limb of the internal capsule, temporal, and occipital white matter. The T1 values showed significantly negative correlations with gestational weeks in pedunculus cerebri (r = -0.80), basal ganglion (r = -0.60), thalamus (r = -0.68), and posterior limb of the internal capsule (r = -0.77). DATA CONCLUSION The T1 values of fetal brain may be assessed using the MOLLI sequence and may reflect the myelination. EVIDENCE LEVEL 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Fenglin Jia
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China.,Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Yi Liao
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China.,Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Xuesheng Li
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China.,Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Zhijun Ye
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China.,Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Pei Li
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China.,Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Xiaoyue Zhou
- MR Collaborations, Siemens Healthineers, Shanghai, People's Republic of China
| | - Qing Li
- MR Collaborations, Siemens Healthineers, Shanghai, People's Republic of China
| | - Shaoyu Wang
- MR Scientific Marketing, Siemens Healthineers, Shanghai, People's Republic of China
| | - Gang Ning
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China.,Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Haibo Qu
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China.,Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
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Rahmanzadeh R, Weigel M, Lu PJ, Melie-Garcia L, Nguyen TD, Cagol A, La Rosa F, Barakovic M, Lutti A, Wang Y, Bach Cuadra M, Radue EW, Gaetano L, Kappos L, Kuhle J, Magon S, Granziera C. A comparative assessment of myelin-sensitive measures in multiple sclerosis patients and healthy subjects. Neuroimage Clin 2022; 36:103177. [PMID: 36067611 PMCID: PMC9468574 DOI: 10.1016/j.nicl.2022.103177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 08/22/2022] [Accepted: 08/27/2022] [Indexed: 12/14/2022]
Abstract
INTRODUCTION Multiple Sclerosis (MS) is a common neurological disease primarily characterized by myelin damage in lesions and in normal - appearing white and gray matter (NAWM, NAGM). Several quantitative MRI (qMRI) methods are sensitive to myelin characteristics by measuring specific tissue biophysical properties. However, there are currently few studies assessing the relative reproducibility and sensitivity of qMRI measures to MS pathology in vivo in patients. METHODS We performed two studies. The first study assessed of the sensitivity of qMRI measures to MS pathology: in this work, we recruited 150 MS and 100 healthy subjects, who underwent brain MRI at 3 T including quantitative T1 mapping (qT1), quantitative susceptibility mapping (QSM), magnetization transfer saturation imaging (MTsat) and myelin water imaging for myelin water fraction (MWF). The sensitivity of qMRIs to MS focal pathology (MS lesions vs peri-plaque white/gray matter (PPWM/PPGM)) was studied lesion-wise; the sensitivity to diffuse normal appearing (NA) pathology was measured using voxel-wise threshold-free cluster enhancement (TFCE) in NAWM and vertex-wise inflated cortex analysis in NAGM. Furthermore, the sensitivity of qMRI to the identification of lesion tissue was investigated using a voxel-wise logistic regression analysis to distinguish MS lesion and PP voxels. The second study assessed the reproducibility of myelin-sensitive qMRI measures in a single scanner. To evaluate the intra-session and inter-session reproducibility of qMRI measures, we have investigated 10 healthy subjects, who underwent two brain 3 T MRIs within the same day (without repositioning), and one after 1-week interval. Five region of interest (ROIs) in white and deep grey matter areas were segmented, and inter- and intra- session reproducibility was studied using the intra-class correlation coefficient (ICC). Further, we also investigated the voxel-wise reproducibility of qMRI measures in NAWM and NAGM. RESULTS qT1 and QSM showed the highest sensitivity to distinguish MS focal WM and cortical pathology from peri-plaque WM (P < 0.0001), although QSM also showed the highest variance when applied to lesions. MWF and MTsat exhibited the highest sensitivity to NAWM pathology (P < 0.01). On the other hand, qT1 appeared to be the most sensitive measure to NAGM pathology (P < 0.01). All myelin-sensitive qMRI measures exhibited high inter/intra sessional ICCs in various WM and deep GM ROIs, in NAWM and in NAGM (ICC 0.82 ± 0.12). CONCLUSION This work shows that the applied qT1, MWF, MTsat and QSM are highly reproducible and exhibit differential sensitivity to focal and diffuse WM and GM pathology in MS patients.
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Affiliation(s)
- Reza Rahmanzadeh
- Translational Imaging in Neurology Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland,Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Matthias Weigel
- Translational Imaging in Neurology Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland,Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland,Division of Radiological Physics, Department of Radiology, University Hospital Basel, Basel, Switzerland
| | - Po-Jui Lu
- Translational Imaging in Neurology Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland,Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Lester Melie-Garcia
- Translational Imaging in Neurology Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland,Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Thanh D. Nguyen
- Department of Radiology, Weill Cornell Medical College, New York, NY, USA
| | - Alessandro Cagol
- Translational Imaging in Neurology Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland,Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Francesco La Rosa
- Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland,CIBM Center for Biomedical Imaging, Lausanne, Switzerland,Radiology Department, Lausanne University and University Hospital, Lausanne, Switzerland
| | - Muhamed Barakovic
- Translational Imaging in Neurology Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland,Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Antoine Lutti
- Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Yi Wang
- Department of Radiology, Weill Cornell Medical College, New York, NY, USA
| | - Meritxell Bach Cuadra
- Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland,CIBM Center for Biomedical Imaging, Lausanne, Switzerland,Radiology Department, Lausanne University and University Hospital, Lausanne, Switzerland
| | - Ernst-Wilhelm Radue
- Translational Imaging in Neurology Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | | | - Ludwig Kappos
- Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Jens Kuhle
- Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Stefano Magon
- Pharmaceutical Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Cristina Granziera
- Translational Imaging in Neurology Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland,Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland,Corresponding author.
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18
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T1 and ADC histogram parameters may be an in vivo biomarker for predicting the grade, subtype, and proliferative activity of meningioma. Eur Radiol 2022; 33:258-269. [PMID: 35953734 DOI: 10.1007/s00330-022-09026-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 06/05/2022] [Accepted: 07/09/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVE To investigate the value of histogram analysis of T1 mapping and diffusion-weighted imaging (DWI) in predicting the grade, subtype, and proliferative activity of meningioma. METHODS This prospective study comprised 69 meningioma patients who underwent preoperative MRI including T1 mapping and DWI. The histogram metrics, including mean, median, maximum, minimum, 10th percentiles (C10), 90th percentiles (C90), kurtosis, skewness, and variance, of T1 and apparent diffusion coefficient (ADC) values were extracted from the whole tumour and peritumoural oedema using FeAture Explorer. The Mann-Whitney U test was used for comparison between low- and high-grade tumours. Receiver operating characteristic (ROC) curve and logistic regression analyses were performed to identify the differential diagnostic performance. The Kruskal-Wallis test was used to further classify meningioma subtypes. Spearman's rank correlation coefficients were calculated to analyse the correlations between histogram parameters and Ki-67 expression. RESULTS High-grade meningiomas showed significantly higher mean, maximum, C90, and variance of T1 (p = 0.001-0.009), lower minimum, and C10 of ADC (p = 0.013-0.028), compared to low-grade meningiomas. For all histogram parameters, the highest individual distinctive power was T1 C90 with an AUC of 0.805. The best diagnostic accuracy was obtained by combining the T1 C90 and ADC C10 with an AUC of 0.864. The histogram parameters differentiated 4/6 pairs of subtype pairs. Significant correlations were identified between Ki-67 and histogram parameters of T1 (C90, mean) and ADC (C10, kurtosis, variance). CONCLUSION T1 and ADC histogram parameters may represent an in vivo biomarker for predicting the grade, subtype, and proliferative activity of meningioma. KEY POINTS • The histogram parameter based on T1 mapping and DWI is useful to preoperatively evaluate the grade, subtype, and proliferative activity of meningioma. • The combination of T1 C90 and ADC C10 showed the best performance for differentiating low- and high-grade meningiomas.
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19
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Trotier AJ, Dilharreguy B, Anandra S, Corbin N, Lefrançois W, Ozenne V, Miraux S, Ribot EJ. The Compressed Sensing MP2RAGE as a Surrogate to the MPRAGE for Neuroimaging at 3 T. Invest Radiol 2022; 57:366-378. [PMID: 35030106 PMCID: PMC9390231 DOI: 10.1097/rli.0000000000000849] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 11/08/2021] [Indexed: 11/26/2022]
Abstract
OBJECTIVES The magnetization-prepared 2 rapid acquisition gradient echo (MP2RAGE) sequence provides quantitative T1 maps in addition to high-contrast morphological images. Advanced acceleration techniques such as compressed sensing (CS) allow its acquisition time to be compatible with clinical applications. To consider its routine use in future neuroimaging protocols, the repeatability of the segmented brain structures was evaluated and compared with the standard morphological sequence (magnetization-prepared rapid gradient echo [MPRAGE]). The repeatability of the T1 measurements was also assessed. MATERIALS AND METHODS Thirteen healthy volunteers were scanned either 3 or 4 times at several days of interval, on a 3 T clinical scanner, with the 2 sequences (CS-MP2RAGE and MPRAGE), set with the same spatial resolution (0.8-mm isotropic) and scan duration (6 minutes 21 seconds). The reconstruction time of the CS-MP2RAGE outputs (including the 2 echo images, the MP2RAGE image, and the T1 map) was 3 minutes 33 seconds, using an open-source in-house algorithm implemented in the Gadgetron framework.Both precision and variability of volume measurements obtained from CAT12 and VolBrain were assessed. The T1 accuracy and repeatability were measured on phantoms and on humans and were compared with literature.Volumes obtained from the CS-MP2RAGE and the MPRAGE images were compared using Student t tests (P < 0.05 was considered significant). RESULTS The CS-MP2RAGE acquisition provided morphological images of the same quality and higher contrasts than the standard MPRAGE images. Similar intravolunteer variabilities were obtained with the CS-MP2RAGE and the MPRAGE segmentations. In addition, high-resolution T1 maps were obtained from the CS-MP2RAGE. T1 times of white and gray matters and several deep gray nuclei are consistent with the literature and show very low variability (<1%). CONCLUSIONS The CS-MP2RAGE can be used in future protocols to rapidly obtain morphological images and quantitative T1 maps in 3-dimensions while maintaining high repeatability in volumetry and relaxation times.
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Affiliation(s)
- Aurélien J. Trotier
- From the Centre de Résonance Magnétique des Systèmes Biologiques, UMR5536, CNRS/Université de Bordeaux
| | - Bixente Dilharreguy
- Biomedical Imaging Facility (pIBIO), UMS3767, CNRS/Université de Bordeaux, Bordeaux, France
| | - Serge Anandra
- Biomedical Imaging Facility (pIBIO), UMS3767, CNRS/Université de Bordeaux, Bordeaux, France
| | - Nadège Corbin
- From the Centre de Résonance Magnétique des Systèmes Biologiques, UMR5536, CNRS/Université de Bordeaux
- UCL Queen Square Institute of Neurology, Wellcome Centre for Human Neuroimaging, University College of London, London, United Kingdom
| | - William Lefrançois
- From the Centre de Résonance Magnétique des Systèmes Biologiques, UMR5536, CNRS/Université de Bordeaux
| | - Valery Ozenne
- From the Centre de Résonance Magnétique des Systèmes Biologiques, UMR5536, CNRS/Université de Bordeaux
| | - Sylvain Miraux
- From the Centre de Résonance Magnétique des Systèmes Biologiques, UMR5536, CNRS/Université de Bordeaux
| | - Emeline J. Ribot
- From the Centre de Résonance Magnétique des Systèmes Biologiques, UMR5536, CNRS/Université de Bordeaux
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20
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Vaneckova M, Piredda GF, Andelova M, Krasensky J, Uher T, Srpova B, Havrdova EK, Vodehnalova K, Horakova D, Hilbert T, Maréchal B, Fartaria MJ, Ravano V, Kober T. Periventricular gradient of T 1 tissue alterations in multiple sclerosis. Neuroimage Clin 2022; 34:103009. [PMID: 35561554 PMCID: PMC9112026 DOI: 10.1016/j.nicl.2022.103009] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 03/24/2022] [Accepted: 04/12/2022] [Indexed: 01/12/2023]
Abstract
T1 relaxation times alterations were assessed based on a study-specific atlas. T1 alterations depend on distance from lateral ventricles (“periventricular gradient”). Gradient parameters correlate better with disability compared to conventional MRI.
Objective Pathology in multiple sclerosis is not homogenously distributed. Recently, it has been shown that structures adjacent to CSF are more severely affected. A gradient of brain tissue involvement was shown with more severe pathology in periventricular areas and in proximity to brain surfaces such as the subarachnoid spaces and ependyma, and hence termed the “surface–in” gradient. Here, we study whether (i) the surface-in gradient of periventricular tissue alteration measured by T1 relaxometry is already present in early multiple sclerosis patients, (ii) how it differs between early and progressive multiple sclerosis patients, and (iii) whether the gradient-derived metrics in normal-appearing white matter and lesions correlate better with physical disability than conventional MRI-based metrics. Methods Forty-seven patients with early multiple sclerosis, 52 with progressive multiple sclerosis, and 92 healthy controls were included in the study. Isotropic 3D T1 relaxometry maps were obtained using the Magnetization-Prepared 2 Rapid Acquisition Gradient Echoes sequence at 3 T. After spatially normalizing the T1 maps into a study-specific common space, T1 inter-subject variability within the healthy cohort was modelled voxel-wise, yielding a normative T1 atlas. Individual comparisons of each multiple sclerosis patient against the atlas were performed by computing z-scores. Equidistant bands of voxels were defined around the ventricles in the supratentorial white matter; the z-scores in these bands were analysed and compared between the early and progressive multiple sclerosis cohorts. Correlations between both conventional and z-score-gradient-derived MRI metrics and the Expanded Disability Status Scale were assessed. Results Patients with early and progressive multiple sclerosis demonstrated a periventricular gradient of T1 relaxation time z-scores. In progressive multiple sclerosis, z-score-derived metrics reflecting the gradient of tissue abnormality in normal-appearing white matter were more strongly correlated with disability (maximal rho = 0.374) than the conventional lesion volume and count (maximal rho = 0.189 and 0.21 respectively). In early multiple sclerosis, the gradient of normal-appearing white matter volume with z-scores > 2 at baseline correlated with clinical disability assessed at two years follow-up. Conclusion Our results suggest that the surface-in white matter gradient of tissue alteration is detectable with T1 relaxometry and is already present at clinical disease onset. The periventricular gradients correlate with clinical disability. The periventricular gradient in normal-appearing white matter may thus qualify as a promising biomarker for monitoring of disease activity from an early stage in all phenotypes of multiple sclerosis.
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Affiliation(s)
- Manuela Vaneckova
- Department of Radiology, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic.
| | - Gian Franco Piredda
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland; Department of Radiology, Lausanne University Hospital and University of Lausanne, Switzerland; Signal Processing Laboratory (LTS 5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Michaela Andelova
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - Jan Krasensky
- Department of Radiology, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - Tomas Uher
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - Barbora Srpova
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - Eva Kubala Havrdova
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - Karolina Vodehnalova
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - Dana Horakova
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - Tom Hilbert
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland; Department of Radiology, Lausanne University Hospital and University of Lausanne, Switzerland; Signal Processing Laboratory (LTS 5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Bénédicte Maréchal
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland; Department of Radiology, Lausanne University Hospital and University of Lausanne, Switzerland; Signal Processing Laboratory (LTS 5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Mário João Fartaria
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland; Department of Radiology, Lausanne University Hospital and University of Lausanne, Switzerland; Signal Processing Laboratory (LTS 5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Veronica Ravano
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland; Department of Radiology, Lausanne University Hospital and University of Lausanne, Switzerland; Signal Processing Laboratory (LTS 5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Tobias Kober
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland; Department of Radiology, Lausanne University Hospital and University of Lausanne, Switzerland; Signal Processing Laboratory (LTS 5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
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21
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Li Z, Xu X, Yang Y, Feng L. Repeatability and robustness of MP-GRASP T 1 mapping. Magn Reson Med 2022; 87:2271-2286. [PMID: 34971467 PMCID: PMC10061203 DOI: 10.1002/mrm.29131] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 12/03/2021] [Accepted: 12/06/2021] [Indexed: 12/14/2022]
Abstract
PURPOSE To demonstrate the repeatability of fast 3D T1 mapping using Magnetization-Prepared Golden-angle RAdial Sparse Parallel (MP-GRASP) MRI and its robustness to variation of imaging parameters including flip angle and spatial resolution in phantoms and the brain. THEORY AND METHODS Multiple imaging experiments were performed to (1) assess the robustness of MP-GRASP T1 mapping to B1 inhomogeneity using a single tube phantom filled with uniform MnCl2 liquid; (2) compare the repeatability of T1 mapping between MP-GRASP and inversion recovery-based spin-echo (IR-SE; over 12 scans), using a commercial T1MES phantom; (3) evaluate the longitudinal variation of T1 estimation using MP-GRASP with varying imaging parameters, including spatial resolution, flip angle, TR/TE, and acceleration rate, using the T1MES phantom (106 scans performed over a period of 12 months); and (4) evaluate the variation of T1 estimation using MP-GRASP with varying imaging parameters in the brain (24 scans in a single visit). In addition, the accuracy of MP-GRASP T1 mapping was also validated against IR-SE by performing linear correlation and calculating the Lin's concordance correlation coefficient (CCC). RESULTS MP-GRASP demonstrates good robustness to B1 inhomogeneity, with intra-slice variability below 1% in the single tube phantom experiment. The longitudinal variability is good both in the phantom (below 2.5%) and in the brain (below 2%) with varying imaging parameters. The T1 values estimated from MP-GRASP are accurate compared to that from the IR-SE imaging (R2 = 0.997, Lin's CCC = 0.996). CONCLUSION MP-GRASP shows excellent repeatability of T1 estimation over time, and it is also robust to variation of different imaging parameters evaluated in this study.
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Affiliation(s)
- Zhitao Li
- Department of Radiology, Stanford University, Palo Alto, California, United States
| | - Xiang Xu
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Yang Yang
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Li Feng
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
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22
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Comparison of MRI T1, T2, and T2* mapping with histology for assessment of intervertebral disc degeneration in an ovine model. Sci Rep 2022; 12:5398. [PMID: 35354902 PMCID: PMC8967912 DOI: 10.1038/s41598-022-09348-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 03/21/2022] [Indexed: 11/08/2022] Open
Abstract
An easy, reliable, and time-efficient standardized approach for assessing lumbar intervertebral disc (IVD) degeneration with relaxation times measurements in pre-clinical and clinical studies is lacking. This prospective study aims to determine the most appropriate method for lumbar IVD degeneration (IDD) assessment in sheep by comparing three quantitative MRI sequences (variable-flip-angle T1 mapping, and multi-echo T2 and T2* mapping), correlating them with Pfirrmann grading and histology. Strong intra- and interrater agreements were found for Nucleus pulposus (NP) regions-of-interest (ROI). T1, T2, and T2* mapping correlated with Pfirrmann grading and histological scoring (p < 0.05) except for the most ventral rectangular ROI on T2 maps. Correlations were excellent for all of the T1 ROIs and the T2* NP ROIs. Highly significant differences in T1 values were found between all Pfirrmann grades except between grades I/II and between grades III/IV. Significant differences were identified in the T2 and the T2* values between all grades except between grades I/III. T1, T2, and T2* relaxation times measurements of the NP are an accurate and time-efficient tool to assess lumbar IDD in sheep. Variable-flip-angle T1 mapping may be further considered as a valuable method to investigate IDD and to assess the efficacy of regenerative treatments in longitudinal studies.
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23
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Fujita S, Cencini M, Buonincontri G, Takei N, Schulte RF, Fukunaga I, Uchida W, Hagiwara A, Kamagata K, Hagiwara Y, Matsuyama Y, Abe O, Tosetti M, Aoki S. Simultaneous relaxometry and morphometry of human brain structures with 3D magnetic resonance fingerprinting: a multicenter, multiplatform, multifield-strength study. Cereb Cortex 2022; 33:729-739. [PMID: 35271703 PMCID: PMC9890456 DOI: 10.1093/cercor/bhac096] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 02/10/2022] [Accepted: 02/12/2022] [Indexed: 02/04/2023] Open
Abstract
Relaxation times and morphological information are fundamental magnetic resonance imaging-derived metrics of the human brain that reflect the status of the underlying tissue. Magnetic resonance fingerprinting (MRF) enables simultaneous acquisition of T1 and T2 maps inherently aligned to the anatomy, allowing whole-brain relaxometry and morphometry in a single scan. In this study, we revealed the feasibility of 3D MRF for simultaneous brain structure-wise morphometry and relaxometry. Comprehensive test-retest scan analyses using five 1.5-T and three 3.0-T systems from a single vendor including different scanner types across 3 institutions demonstrated that 3D MRF-derived morphological information and relaxation times are highly repeatable at both 1.5 T and 3.0 T. Regional cortical thickness and subcortical volume values showed high agreement and low bias across different field strengths. The ability to acquire a set of regional T1, T2, thickness, and volume measurements of neuroanatomical structures with high repeatability and reproducibility facilitates the ability of longitudinal multicenter imaging studies to quantitatively monitor changes associated with underlying pathologies, disease progression, and treatments.
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Affiliation(s)
- Shohei Fujita
- Corresponding author: Department of Radiology, Juntendo University School of Medicine, 12-1 Hongo, Bunkyo, Tokyo 113-8421, Japan.
| | - Matteo Cencini
- Imago7 Foundation, Pisa, Italy,IRCCS Stella Maris, Pisa, Italy
| | | | | | | | - Issei Fukunaga
- Department of Radiology, Juntendo University, Tokyo, Japan
| | - Wataru Uchida
- Department of Radiology, Juntendo University, Tokyo, Japan
| | | | - Koji Kamagata
- Department of Radiology, Juntendo University, Tokyo, Japan
| | - Yasuhiro Hagiwara
- Department of Biostatistics, School of Public Health, The University of Tokyo, Tokyo, Japan
| | - Yutaka Matsuyama
- Department of Biostatistics, School of Public Health, The University of Tokyo, Tokyo, Japan
| | - Osamu Abe
- Department of Radiology, The University of Tokyo, Tokyo, Japan
| | - Michela Tosetti
- Imago7 Foundation, Pisa, Italy,IRCCS Stella Maris, Pisa, Italy
| | - Shigeki Aoki
- Department of Radiology, Juntendo University, Tokyo, Japan
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24
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Cao T, Ma S, Wang N, Gharabaghi S, Xie Y, Fan Z, Hogg E, Wu C, Han F, Tagliati M, Haacke EM, Christodoulou AG, Li D. Three-dimensional simultaneous brain mapping of T1, T2, T2∗ and magnetic susceptibility with MR Multitasking. Magn Reson Med 2022; 87:1375-1389. [PMID: 34708438 PMCID: PMC8776611 DOI: 10.1002/mrm.29059] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 09/08/2021] [Accepted: 10/07/2021] [Indexed: 01/24/2023]
Abstract
PURPOSE To develop a new technique that enables simultaneous quantification of whole-brain T1 , T2 , T 2 ∗ , as well as susceptibility and synthesis of six contrast-weighted images in a single 9.1-minute scan. METHODS The technique uses hybrid T2 -prepared inversion-recovery pulse modules and multi-echo gradient-echo readouts to collect k-space data with various T1, T2, and T 2 ∗ weightings. The underlying image is represented as a six-dimensional low-rank tensor consisting of three spatial dimensions and three temporal dimensions corresponding to T1 recovery, T2 decay, and multi-echo behaviors, respectively. Multiparametric maps were fitted from reconstructed image series. The proposed method was validated on phantoms and healthy volunteers, by comparing quantitative measurements against corresponding reference methods. The feasibility of generating six contrast-weighted images was also examined. RESULTS High quality, co-registered T1 , T2 , and T 2 ∗ susceptibility maps were generated that closely resembled the reference maps. Phantom measurements showed substantial consistency (R2 > 0.98) with the reference measurements. Despite the significant differences of T1 (p < .001), T2 (p = .002), and T 2 ∗ (p = 0.008) between our method and the references for in vivo studies, excellent agreement was achieved with all intraclass correlation coefficients greater than 0.75. No significant difference was found for susceptibility (p = .900). The framework is also capable of synthesizing six contrast-weighted images. CONCLUSION The MR Multitasking-based 3D brain mapping of T1 , T2 , T 2 ∗ , and susceptibility agrees well with the reference and is a promising technique for multicontrast and quantitative imaging.
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Affiliation(s)
- Tianle Cao
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
- Department of Bioengineering, University of California, Los Angeles, California, USA
| | - Sen Ma
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Nan Wang
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Sara Gharabaghi
- Magnetic Resonance Innovations, Inc., Bingham Farms, MI, USA
| | - Yibin Xie
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Zhaoyang Fan
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Elliot Hogg
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Chaowei Wu
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
- Department of Bioengineering, University of California, Los Angeles, California, USA
| | - Fei Han
- Siemens Medical Solutions USA, Inc., Los Angeles, California, USA
| | - Michele Tagliati
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - E. Mark Haacke
- Magnetic Resonance Innovations, Inc., Bingham Farms, MI, USA
- Department of Radiology, Wayne State University School of Medicine, Detroit, MI, USA
- The MRI Institute for Biomedical Research, Bingham Farms, MI, USA
| | - Anthony G. Christodoulou
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
- Department of Bioengineering, University of California, Los Angeles, California, USA
| | - Debiao Li
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
- Department of Bioengineering, University of California, Los Angeles, California, USA
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25
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O'Grady KP, Satish S, Owen QR, Box BA, Bagnato F, Combes AJE, Cook SR, Westervelt HJ, Feiler HR, Lawless RD, Sarma A, Malone SD, Ndolo JM, Yoon K, Dortch RD, Rogers BP, Smith SA. Relaxation-Compensated Chemical Exchange Saturation Transfer MRI in the Brain at 7T: Application in Relapsing-Remitting Multiple Sclerosis. Front Neurol 2022; 13:764690. [PMID: 35299614 PMCID: PMC8923037 DOI: 10.3389/fneur.2022.764690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 02/01/2022] [Indexed: 11/16/2022] Open
Abstract
Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) can probe tissue biochemistry in vivo with high resolution and sensitivity without requiring exogenous contrast agents. Applying CEST MRI at ultrahigh field provides advantages of increasing spectral resolution and improving sensitivity to metabolites with faster proton exchange rates such as glutamate, a critical neurotransmitter in the brain. Prior magnetic resonance spectroscopy and CEST MRI studies have revealed altered regulation of glutamate in patients with multiple sclerosis (MS). While CEST imaging facilitates new strategies for investigating the pathology underlying this complex and heterogeneous neurological disease, CEST signals are contaminated or diluted by concurrent effects (e.g., semi-solid magnetization transfer (MT) and direct water saturation) and are scaled by the T1 relaxation time of the free water pool which may also be altered in the context of disease. In this study of 20 relapsing-remitting MS patients and age- and sex-matched healthy volunteers, glutamate-weighted CEST data were acquired at 7.0 T. A Lorentzian fitting procedure was used to remove the asymmetric MT contribution from CEST z-spectra, and the apparent exchange-dependent relaxation (AREX) correction was applied using an R1 map derived from an inversion recovery sequence to further isolate glutamate-weighted CEST signals from concurrent effects. Associations between AREX and cognitive function were examined using the Minimal Assessment of Cognitive Function in MS battery. After isolating CEST effects from MT, direct water saturation, and T1 effects, glutamate-weighted AREX contrast remained higher in gray matter than in white matter, though the difference between these tissues decreased. Glutamate-weighted AREX in normal-appearing gray and white matter in MS patients did not differ from healthy gray and white matter but was significantly elevated in white matter lesions. AREX in some cortical regions and in white matter lesions correlated with disability and measures of cognitive function in MS patients. However, further studies with larger sample sizes are needed to confirm these relationships due to potential confounding effects. The application of MT and AREX corrections in this study demonstrates the importance of isolating CEST signals for more specific characterization of the contribution of metabolic changes to tissue pathology and symptoms in MS.
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Affiliation(s)
- Kristin P. O'Grady
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Sanjana Satish
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Quinn R. Owen
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Bailey A. Box
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Francesca Bagnato
- Neuroimaging Unit, Division of Neuroimmunology, Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Neurology, Nashville VA Medical Center, TN Valley Healthcare System, Nashville, TN, United States
| | - Anna J. E. Combes
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Sarah R. Cook
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Holly James Westervelt
- Division of Behavioral and Cognitive Neurology, Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Haley R. Feiler
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Richard D. Lawless
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Asha Sarma
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Shekinah D. Malone
- School of Medicine, Meharry Medical College, Nashville, TN, United States
| | - Josephine M. Ndolo
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Keejin Yoon
- Neuroimaging Unit, Division of Neuroimmunology, Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Richard D. Dortch
- Department of Translational Neuroscience, Barrow Neurological Institute, Phoenix, AZ, United States
| | - Baxter P. Rogers
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
- Biomedical Engineering, Vanderbilt University, Nashville, TN, United States
| | - Seth A. Smith
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
- Biomedical Engineering, Vanderbilt University, Nashville, TN, United States
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26
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Yik JT, Becquart P, Gill J, Petkau J, Traboulsee A, Carruthers R, Kolind SH, Devonshire V, Sayao AL, Schabas A, Tam R, Moore GRW, Li DKB, Stukas S, Wellington C, Quandt JA, Vavasour IM, Laule C. Serum neurofilament light chain correlates with myelin and axonal magnetic resonance imaging markers in multiple sclerosis. Mult Scler Relat Disord 2022; 57:103366. [PMID: 35158472 DOI: 10.1016/j.msard.2021.103366] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 10/08/2021] [Accepted: 11/01/2021] [Indexed: 12/16/2022]
Abstract
BACKGROUND Neurofilaments are cytoskeletal proteins that are detectable in the blood after neuroaxonal injury. Multiple sclerosis (MS) disease progression, greater lesion volume, and brain atrophy are associated with higher levels of serum neurofilament light chain (NfL), but few studies have examined the relationship between NfL and advanced magnetic resonance imaging (MRI) measures related to myelin and axons. We assessed the relationship between serum NfL and brain MRI measures in a diverse group of MS participants. METHODS AND MATERIALS 103 participants (20 clinically isolated syndrome, 33 relapsing-remitting, 30 secondary progressive, 20 primary progressive) underwent 3T MRI to obtain myelin water fraction (MWF), geometric mean T2 (GMT2), water content, T1; high angular resolution diffusion imaging (HARDI)-derived axial diffusivity (AD), radial diffusivity (RD), fractional anisotropy (FA); diffusion basis spectrum imaging (DBSI)-derived AD, RD, FA; restricted, hindered, water and fiber fractions; and volume measurements of normalized brain, lesion, thalamic, deep gray matter (GM), and cortical thickness. Multiple linear regressions assessed the strength of association between serum NfL (dependent variable) and each MRI measure in whole brain (WB), normal appearing white matter (NAWM) and T2 lesions (independent variables), while controlling for age, expanded disability status scale, and disease duration. RESULTS Serum NfL levels were significantly associated with metrics of axonal damage (FA: R2WB-HARDI = 0.29, R2NAWM-HARDI = 0.31, R2NAWM-DBSI = 0.30, R2Lesion-DBSI = 0.31; AD: R2WB-HARDI=0.31), myelin damage (MWF: R2WB = 0.29, R2NAWM = 0.30, RD: R2WB-HARDI = 0.32, R2NAWM-HARDI = 0.34, R2Lesion-DBSI = 0.30), edema and inflammation (T1: R2Lesion = 0.32; GMT2: R2WB = 0.31, R2Lesion = 0.31), and cellularity (restricted fraction R2WB = 0.30, R2NAWM = 0.32) across the entire MS cohort. Higher serum NfL levels were associated with significantly higher T2 lesion volume (R2 = 0.35), lower brain structure volumes (thalamus R2 = 0.31; deep GM R2 = 0.33; normalized brain R2 = 0.31), and smaller cortical thickness R2 = 0.31). CONCLUSION The association between NfL and myelin MRI markers suggest that elevated serum NfL is a useful biomarker that reflects not only acute axonal damage, but also damage to myelin and inflammation, likely due to the known synergistic myelin-axon coupling relationship.
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Affiliation(s)
- Jackie T Yik
- Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada; International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, BC, Canada
| | - Pierre Becquart
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Jasmine Gill
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
| | - John Petkau
- Department of Statistics, University of British Columbia, Vancouver, BC, Canada
| | - Anthony Traboulsee
- Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Robert Carruthers
- Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Shannon H Kolind
- Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada; International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, BC, Canada; Department of Medicine, University of British Columbia, Vancouver, BC, Canada; Department of Radiology, University of British Columbia, Vancouver, BC, Canada
| | - Virginia Devonshire
- Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Ana-Luiza Sayao
- Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Alice Schabas
- Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Roger Tam
- Department of Radiology, University of British Columbia, Vancouver, BC, Canada
| | - G R Wayne Moore
- International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, BC, Canada; Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
| | - David K B Li
- Department of Medicine, University of British Columbia, Vancouver, BC, Canada; Department of Radiology, University of British Columbia, Vancouver, BC, Canada
| | - Sophie Stukas
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Cheryl Wellington
- International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, BC, Canada; Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Jacqueline A Quandt
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Irene M Vavasour
- International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, BC, Canada; Department of Radiology, University of British Columbia, Vancouver, BC, Canada
| | - Cornelia Laule
- Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada; International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, BC, Canada; Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada; Department of Radiology, University of British Columbia, Vancouver, BC, Canada.
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27
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Maier IL, Heide M, Hofer S, Dechent P, Fiss I, von der Brelie C, Rohde V, Frahm J, Bähr M, Liman J. High Periventricular T1 Relaxation Times Predict Gait Improvement After Spinal Tap in Patients with Idiopathic Normal Pressure Hydrocephalus. Clin Neuroradiol 2022; 32:1067-1076. [PMID: 35391549 PMCID: PMC9744711 DOI: 10.1007/s00062-022-01155-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Accepted: 03/02/2022] [Indexed: 01/18/2023]
Abstract
PURPOSE The diagnosis of idiopathic normal pressure hydrocephalus (iNPH) can be challenging. Aim of this study was to use a novel T1 mapping method to enrich the diagnostic work-up of patients with suspected iNPH. METHODS Using 3T magnetic resonance imaging (MRI) we prospectively evaluated rapid high-resolution T1 mapping at 0.5 mm resolution and 4 s acquisition time in 15 patients with suspected iNPH and 8 age-matched, healthy controls. T1 mapping in axial sections of the cerebrum, clinical and neuropsychological testing were performed prior to and after cerebrospinal fluid tap test (CSF-TT). T1 relaxation times were measured in 5 predefined periventricular regions. RESULTS All 15 patients with suspected iNPH showed gait impairment, 13 (86.6%) showed signs of cognitive impairment and 8 (53.3%) patients had urinary incontinence. Gait improvement was noted in 12 patients (80%) after CSF-TT. T1 relaxation times in all periventricular regions were elevated in patients with iNPH compared to controls with the most pronounced differences in the anterior (1006 ± 93 ms vs. 911 ± 77 ms; p = 0.023) and posterior horns (983 ± 103 ms vs. 893 ± 68 ms; p = 0.037) of the lateral ventricles. Montreal cognitive assessment (MoCA) scores at baseline were negatively correlated with T1 relaxation times (r < -0.5, p < 0.02). Higher T1 relaxation times were significantly correlated with an improvement of the 3‑m timed up and go test (r > 0.6 and p < 0.03) after CSF-TT. CONCLUSION In iNPH-patients, periventricular T1 relaxation times are increased compared to age-matched controls and predict gait improvement after CSF-TT. T1 mapping might enrich iNPH work-up and might be useful to indicate permanent shunting.
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Affiliation(s)
- Ilko L. Maier
- Department of Neurology, University Medical Center Göttingen, Robert-Koch-Str. 40, 37075 Göttingen, Germany
| | - Marielle Heide
- Department of Neurology, University Medical Center Göttingen, Robert-Koch-Str. 40, 37075 Göttingen, Germany
| | - Sabine Hofer
- Biomedizinische NMR, Max-Planck-Institut für biophysikalische Chemie, Göttingen, Germany
| | - Peter Dechent
- Institute for Cognitive Neurology, University Medical Center Göttingen, Göttingen, Germany
| | - Ingo Fiss
- Department of Neurosurgery, University Medical Center Göttingen, Göttingen, Germany
| | | | - Veit Rohde
- Department of Neurosurgery, University Medical Center Göttingen, Göttingen, Germany
| | - Jens Frahm
- Biomedizinische NMR, Max-Planck-Institut für biophysikalische Chemie, Göttingen, Germany
| | - Mathias Bähr
- Department of Neurology, University Medical Center Göttingen, Robert-Koch-Str. 40, 37075 Göttingen, Germany
| | - Jan Liman
- Department of Neurology, University Medical Center Göttingen, Robert-Koch-Str. 40, 37075 Göttingen, Germany
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28
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Baucher G, Rasoanandrianina H, Levy S, Pini L, Troude L, Roche PH, Callot V. T1 Mapping for Microstructural Assessment of the Cervical Spinal Cord in the Evaluation of Patients with Degenerative Cervical Myelopathy. AJNR Am J Neuroradiol 2021; 42:1348-1357. [PMID: 33985954 DOI: 10.3174/ajnr.a7157] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Accepted: 02/07/2021] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Although current radiologic evaluation of degenerative cervical myelopathy by conventional MR imaging accurately demonstrates spondylosis or degenerative disc disease causing spinal cord dysfunction, conventional MR imaging still fails to provide satisfactory anatomic and clinical correlations. In this context, we assessed the potential value of quantitative cervical spinal cord T1 mapping regarding the evaluation of patients with degenerative cervical myelopathy. MATERIALS AND METHODS Twenty patients diagnosed with mild and moderate-to-severe degenerative cervical myelopathy and 10 healthy subjects were enrolled in a multiparametric MR imaging protocol. Cervical spinal cord T1 mapping was performed with the MP2RAGE sequence procedure. Retrieved data were processed and analyzed regarding the global spinal cord and white and anterior gray matter on the basis of the clinical severity and the spinal canal stenosis grading. RESULTS Noncompressed levels in healthy controls demonstrated significantly lower T1 values than noncompressed, mild, moderate, and severe stenotic levels in patients. Concerning the entire spinal cord T1 mapping, patients with moderate-to-severe degenerative cervical myelopathy had higher T1 values compared with healthy controls. Regarding the specific levels, patients with moderate-to-severe degenerative cervical myelopathy demonstrated a T1 value increase at C1, C7, and the level of maximal compression compared with healthy controls. Patients with mild degenerative cervical myelopathy had lower T1 values than those with moderate-to-severe degenerative cervical myelopathy at the level of maximal compression. Analyses of white and anterior gray matter confirmed similar results. Strong negative correlations between individual modified Japanese Orthopaedic Association scores and T1 values were also observed. CONCLUSIONS In this preliminary study, 3D-MP2RAGE T1 mapping demonstrated increased T1 values in the pathology tissue samples, with diffuse medullary alterations in all patients with degenerative cervical myelopathy, especially relevant at C1 (nonstenotic level) and at the maximal compression level. Encouraging correlations observed with the modified Japanese Orthopaedic Association score make this novel approach a potential quantitative biomarker related to clinical severity in degenerative cervical myelopathy. Nevertheless, patients with mild degenerative cervical myelopathy demonstrated nonsignificant results compared with healthy controls and should now be studied in multicenter studies with larger patient populations.
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Affiliation(s)
- G Baucher
- From the Neurochirurgie adulte (G.B., L.T., P.-H.R.), Assistance Publique-Hôpitaux de Marseille, Hôpital Universitaire Nord, Marseille, France
- Center for Magnetic Resonance in Biology and Medicine (G.B., H.R., L.P., S.L., V.C.), Assistance Publique-Hôpitaux de Marseille, Hôpital Universitaire Timone, Marseille, France
- iLab-Spine International Associated Laboratory (G.B., H.R., S.L., P.-H.R., V.C.), Marseille-Montreal, France-Canada
| | - H Rasoanandrianina
- Center for Magnetic Resonance in Biology and Medicine (G.B., H.R., L.P., S.L., V.C.), Assistance Publique-Hôpitaux de Marseille, Hôpital Universitaire Timone, Marseille, France
- Center for Magnetic Resonance in Biology and Medicine (H.R., L.P., S.L., V.C.), Aix-Marseille Université, Center National de la Recherche Scientifique, Marseille, France
- iLab-Spine International Associated Laboratory (G.B., H.R., S.L., P.-H.R., V.C.), Marseille-Montreal, France-Canada
| | - S Levy
- Center for Magnetic Resonance in Biology and Medicine (G.B., H.R., L.P., S.L., V.C.), Assistance Publique-Hôpitaux de Marseille, Hôpital Universitaire Timone, Marseille, France
- Center for Magnetic Resonance in Biology and Medicine (H.R., L.P., S.L., V.C.), Aix-Marseille Université, Center National de la Recherche Scientifique, Marseille, France
- iLab-Spine International Associated Laboratory (G.B., H.R., S.L., P.-H.R., V.C.), Marseille-Montreal, France-Canada
| | - L Pini
- Center for Magnetic Resonance in Biology and Medicine (G.B., H.R., L.P., S.L., V.C.), Assistance Publique-Hôpitaux de Marseille, Hôpital Universitaire Timone, Marseille, France
- Center for Magnetic Resonance in Biology and Medicine (H.R., L.P., S.L., V.C.), Aix-Marseille Université, Center National de la Recherche Scientifique, Marseille, France
| | - L Troude
- From the Neurochirurgie adulte (G.B., L.T., P.-H.R.), Assistance Publique-Hôpitaux de Marseille, Hôpital Universitaire Nord, Marseille, France
| | - P-H Roche
- From the Neurochirurgie adulte (G.B., L.T., P.-H.R.), Assistance Publique-Hôpitaux de Marseille, Hôpital Universitaire Nord, Marseille, France
- iLab-Spine International Associated Laboratory (G.B., H.R., S.L., P.-H.R., V.C.), Marseille-Montreal, France-Canada
| | - V Callot
- Center for Magnetic Resonance in Biology and Medicine (G.B., H.R., L.P., S.L., V.C.), Assistance Publique-Hôpitaux de Marseille, Hôpital Universitaire Timone, Marseille, France
- Center for Magnetic Resonance in Biology and Medicine (H.R., L.P., S.L., V.C.), Aix-Marseille Université, Center National de la Recherche Scientifique, Marseille, France
- iLab-Spine International Associated Laboratory (G.B., H.R., S.L., P.-H.R., V.C.), Marseille-Montreal, France-Canada
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RESUME N: A flexible class of multi-parameter qMRI protocols. Phys Med 2021; 88:23-36. [PMID: 34171573 DOI: 10.1016/j.ejmp.2021.04.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 03/16/2021] [Accepted: 04/02/2021] [Indexed: 12/30/2022] Open
Abstract
PURPOSE To introduce a class of fast 3D quantitative MRI (qMRI) schemes (RESUMEN, for N=1,…,4) that allow for a thorough characterization of microstructural properties of brain tissues. METHODS An arbitrary multi-echo GRE acquisition optimized for quantitative susceptibility mapping (QSM) is complemented with an appropriate low flip-angle GRE sequence drawn from four possible choices. The acquired signals are processed to analytically derive the longitudinal relaxation (R1) and free induction decay (R2∗) rates, as well as the proton density (PD) and QSM. A comprehensive modeling of the excitation and B1- profiles and of the RF-spoiling is included in the acquisition and processing pipeline. RESULTS The RESUMEN maps appear homogeneous throughout the field-of-view and exhibit comparable values and high SNR across the considered range of N values. CONCLUSIONS The introduced schemes represent a class of robust and flexible strategies to derive a thorough and fast qMRI study, suitable for a whole-brain acquisition with isotropic voxel resolution of 700 μm in less than 15 min.
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30
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Granziera C, Wuerfel J, Barkhof F, Calabrese M, De Stefano N, Enzinger C, Evangelou N, Filippi M, Geurts JJG, Reich DS, Rocca MA, Ropele S, Rovira À, Sati P, Toosy AT, Vrenken H, Gandini Wheeler-Kingshott CAM, Kappos L. Quantitative magnetic resonance imaging towards clinical application in multiple sclerosis. Brain 2021; 144:1296-1311. [PMID: 33970206 PMCID: PMC8219362 DOI: 10.1093/brain/awab029] [Citation(s) in RCA: 68] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 10/25/2020] [Accepted: 11/16/2020] [Indexed: 12/11/2022] Open
Abstract
Quantitative MRI provides biophysical measures of the microstructural integrity of the CNS, which can be compared across CNS regions, patients, and centres. In patients with multiple sclerosis, quantitative MRI techniques such as relaxometry, myelin imaging, magnetization transfer, diffusion MRI, quantitative susceptibility mapping, and perfusion MRI, complement conventional MRI techniques by providing insight into disease mechanisms. These include: (i) presence and extent of diffuse damage in CNS tissue outside lesions (normal-appearing tissue); (ii) heterogeneity of damage and repair in focal lesions; and (iii) specific damage to CNS tissue components. This review summarizes recent technical advances in quantitative MRI, existing pathological validation of quantitative MRI techniques, and emerging applications of quantitative MRI to patients with multiple sclerosis in both research and clinical settings. The current level of clinical maturity of each quantitative MRI technique, especially regarding its integration into clinical routine, is discussed. We aim to provide a better understanding of how quantitative MRI may help clinical practice by improving stratification of patients with multiple sclerosis, and assessment of disease progression, and evaluation of treatment response.
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Affiliation(s)
- Cristina Granziera
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Jens Wuerfel
- Medical Image Analysis Center, Basel, Switzerland
- Quantitative Biomedical Imaging Group (qbig), Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, multiple sclerosis Center Amsterdam, Amsterdam University Medical Center, Amsterdam, The Netherlands
- UCL Institutes of Healthcare Engineering and Neurology, London, UK
| | - Massimiliano Calabrese
- Neurology B, Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | - Nicola De Stefano
- Neurology, Department of Medicine, Surgery and Neuroscience, University of Siena, Italy
| | - Christian Enzinger
- Department of Neurology and Division of Neuroradiology, Medical University of Graz, Graz, Austria
| | - Nikos Evangelou
- Division of Clinical Neuroscience, University of Nottingham, Nottingham, UK
| | - Massimo Filippi
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, and Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Jeroen J G Geurts
- Department of Anatomy and Neurosciences, multiple sclerosis Center Amsterdam, Neuroscience Amsterdam, Amsterdam University Medical Centers, location VUmc, Amsterdam, The Netherlands
| | - Daniel S Reich
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health (NIH), Bethesda, MD, USA
| | - Maria A Rocca
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, and Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Stefan Ropele
- Neuroimaging Research Unit, Department of Neurology, Medical University of Graz, Graz, Austria
| | - Àlex Rovira
- Section of Neuroradiology (Department of Radiology), Vall d'Hebron University Hospital and Research Institute, Barcelona, Spain
| | - Pascal Sati
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health (NIH), Bethesda, MD, USA
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Ahmed T Toosy
- Queen Square multiple sclerosis Centre, Department of Neuroinflammation, Queen Square Institute of Neurology, University College London, London, UK
| | - Hugo Vrenken
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, multiple sclerosis Center Amsterdam, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Claudia A M Gandini Wheeler-Kingshott
- Queen Square multiple sclerosis Centre, Department of Neuroinflammation, Queen Square Institute of Neurology, University College London, London, UK
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
- Brain MRI 3T Research Centre, IRCCS Mondino Foundation, Pavia, Italy
| | - Ludwig Kappos
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
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31
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Brier MR, Snyder AZ, Tanenbaum A, Rudick RA, Fisher E, Jones S, Shimony JS, Cross AH, Benzinger TLS, Naismith RT. Quantitative signal properties from standardized MRIs correlate with multiple sclerosis disability. Ann Clin Transl Neurol 2021; 8:1096-1109. [PMID: 33943045 PMCID: PMC8108425 DOI: 10.1002/acn3.51354] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 03/11/2021] [Accepted: 03/16/2021] [Indexed: 01/21/2023] Open
Abstract
OBJECTIVE To enable use of clinical magnetic resonance images (MRIs) to quantify abnormalities in normal appearing (NA) white matter (WM) and gray matter (GM) in multiple sclerosis (MS) and to determine associations with MS-related disability. Identification of these abnormalities heretofore has required specialized scans not routinely available in clinical practice. METHODS We developed an analytic technique which normalizes image intensities based on an intensity atlas for quantification of WM and GM abnormalities in standardized MRIs obtained with clinical sequences. Gaussian mixture modeling is applied to summarize image intensity distributions from T1-weighted and 3D-FLAIR (T2-weighted) images from 5010 participants enrolled in a multinational database of MS patients which collected imaging, neuroperformance and disability measures. RESULTS Intensity distribution metrics distinguished MS patients from control participants based on normalized non-lesional signal differences. This analysis revealed non-lesional differences between relapsing MS versus progressive MS subtypes. Further, the correlation between our non-lesional measures and disability was approximately three times greater than that between total lesion volume and disability, measured using the patient derived disease steps. Multivariate modeling revealed that measures of extra-lesional tissue integrity and atrophy contribute uniquely, and approximately equally, to the prediction of MS-related disability. INTERPRETATION These results support the notion that non-lesional abnormalities correlate more strongly with MS-related disability than lesion burden and provide new insight into the basis of abnormalities in NA WM. Non-lesional abnormalities distinguish relapsing from progressive MS but do not distinguish between progressive subtypes suggesting a common progressive pathophysiology. Image intensity parameters and existing biomarkers each independently correlate with MS-related disability.
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Affiliation(s)
- Matthew R. Brier
- Department of NeurologyWashington University in St. LouisSt. LouisMissouriUSA
| | - Abraham Z. Snyder
- Malinckrodt Institute of RadiologyWashington University in St. LouisSt. LouisMissouriUSA
| | - Aaron Tanenbaum
- Department of NeurologyWashington University in St. LouisSt. LouisMissouriUSA
| | | | | | | | - Joshua S. Shimony
- Malinckrodt Institute of RadiologyWashington University in St. LouisSt. LouisMissouriUSA
| | - Anne H. Cross
- Department of NeurologyWashington University in St. LouisSt. LouisMissouriUSA
| | - Tammie L. S. Benzinger
- Malinckrodt Institute of RadiologyWashington University in St. LouisSt. LouisMissouriUSA
| | - Robert T. Naismith
- Department of NeurologyWashington University in St. LouisSt. LouisMissouriUSA
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Yee S, Fadell M. System-specific evaluation of the dual flip angle MRI technique for quantitative T 1 measurement. Med Phys 2021; 48:2790-2799. [PMID: 33772828 DOI: 10.1002/mp.14864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Revised: 03/07/2021] [Accepted: 03/19/2021] [Indexed: 11/11/2022] Open
Abstract
PURPOSE To investigate if the accuracy of the dual flip angle (DFA) technique for T1 measurement is affected by the system-specific RF excitation performance. METHODS A T1 phantom, made of 12 vials of unique T1 value ranging approximately from 200 ms to 2000 ms, was built and tested on seven different clinical scanners. For each experiment, the reference T1 of each vial was obtained by the inversion recovery-based technique, and the DFA technique was applied repeatedly with several flip angle (FA) pairs conventionally proposed as optimal. The accuracy of the DFA technique for each FA pair was then evaluated by comparing the measured T1 values for the vials to the references. Any variation of the accuracy was then evaluated across different FA pairs, and across different MRI systems. To improve accuracy with a selected FA pair, the signal ratio (SR) curve, obtained from the phantom, was utilized in a calibration strategy of the DFA technique. RESULTS When combined for all the vials, the average ratio of the measured T1 to the reference generally increased as the FA pair window gradually slid from the smaller to the larger FA values. Furthermore, among several optimal FA pairs, the pair of the best accuracy varied slightly by the MRI system. The accuracy for any FA pair could be improved when the calibration strategy was utilized. CONCLUSIONS The RF excitation performance may vary by the specific FA pair and by the specific MRI system, influencing the accuracy of the DFA technique. The system-specific evaluation, and, if needed, its calibration, would help improve the accuracy of the DFA technique.
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Affiliation(s)
- Seonghwan Yee
- Department of Radiology, University of Colorado School of Medicine, Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Michael Fadell
- Department of Radiology, University of Colorado School of Medicine, Anschutz Medical Campus, Aurora, CO, 80045, USA.,Children's Hospital of Colorado, 13123 East 16th Avenue, Aurora, CO, 80045, USA
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Li Y, Wang Y, Qi H, Hu Z, Chen Z, Yang R, Qiao H, Sun J, Wang T, Zhao X, Guo H, Chen H. Deep learning-enhanced T 1 mapping with spatial-temporal and physical constraint. Magn Reson Med 2021; 86:1647-1661. [PMID: 33821529 DOI: 10.1002/mrm.28793] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 03/07/2021] [Accepted: 03/15/2021] [Indexed: 12/20/2022]
Abstract
PURPOSE To propose a reconstruction framework to generate accurate T1 maps for a fast MR T1 mapping sequence. METHODS A deep learning-enhanced T1 mapping method with spatial-temporal and physical constraint (DAINTY) was proposed. This method explicitly imposed low-rank and sparsity constraints on the multiframe T1 -weighted images to exploit the spatial-temporal correlation. A deep neural network was used to efficiently perform T1 mapping as well as denoise and reduce undersampling artifacts. Additionally, the physical constraint was used to build a bridge between low-rank and sparsity constraint and deep learning prior, so the benefits of constrained reconstruction and deep learning can be both available. The DAINTY method was trained on simulated brain data sets, but tested on real acquired phantom, 6 healthy volunteers, and 7 atherosclerosis patients, compared with the narrow-band k-space-weighted image contrast filter conjugate-gradient SENSE (NK-CS) method, kt-sparse-SENSE (kt-SS) method, and low-rank plus sparsity (L+S) method with least-squares T1 fitting and direct deep learning mapping. RESULTS The DAINTY method can generate more accurate T1 maps and higher-quality T1 -weighted images compared with other methods. For atherosclerosis patients, the intraplaque hemorrhage can be successfully detected. The computation speed of DAINTY was 10 times faster than traditional methods. Meanwhile, DAINTY can reconstruct images with comparable quality using only 50% of k-space data. CONCLUSION The proposed method can provide accurate T1 maps and good-quality T1 -weighted images with high efficiency.
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Affiliation(s)
- Yuze Li
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Yajie Wang
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Haikun Qi
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Zhangxuan Hu
- GE Healthcare, MR Research China, Beijing, China
| | - Zhensen Chen
- Vascular Imaging Lab and BioMolecular Imaging Center, Department of Radiology, University of Washington, Seattle, Washington, USA
| | - Runyu Yang
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Huiyu Qiao
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Jie Sun
- GE Healthcare, MR Research China, Beijing, China
| | - Tao Wang
- Department of Neurology, Peking University Third Hospital, Beijing, China
| | - Xihai Zhao
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Hua Guo
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Huijun Chen
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
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Seiler A, Nöth U, Hok P, Reiländer A, Maiworm M, Baudrexel S, Meuth S, Rosenow F, Steinmetz H, Wagner M, Hattingen E, Deichmann R, Gracien RM. Multiparametric Quantitative MRI in Neurological Diseases. Front Neurol 2021; 12:640239. [PMID: 33763021 PMCID: PMC7982527 DOI: 10.3389/fneur.2021.640239] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Accepted: 02/12/2021] [Indexed: 11/27/2022] Open
Abstract
Magnetic resonance imaging (MRI) is the gold standard imaging technique for diagnosis and monitoring of many neurological diseases. However, the application of conventional MRI in clinical routine is mainly limited to the visual detection of macroscopic tissue pathology since mixed tissue contrasts depending on hardware and protocol parameters hamper its application for the assessment of subtle or diffuse impairment of the structural tissue integrity. Multiparametric quantitative (q)MRI determines tissue parameters quantitatively, enabling the detection of microstructural processes related to tissue remodeling in aging and neurological diseases. In contrast to measuring tissue atrophy via structural imaging, multiparametric qMRI allows for investigating biologically distinct microstructural processes, which precede changes of the tissue volume. This facilitates a more comprehensive characterization of tissue alterations by revealing early impairment of the microstructural integrity and specific disease-related patterns. So far, qMRI techniques have been employed in a wide range of neurological diseases, including in particular conditions with inflammatory, cerebrovascular and neurodegenerative pathology. Numerous studies suggest that qMRI might add valuable information, including the detection of microstructural tissue damage in areas appearing normal on conventional MRI and unveiling the microstructural correlates of clinical manifestations. This review will give an overview of current qMRI techniques, the most relevant tissue parameters and potential applications in neurological diseases, such as early (differential) diagnosis, monitoring of disease progression, and evaluating effects of therapeutic interventions.
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Affiliation(s)
- Alexander Seiler
- Department of Neurology, Goethe University, Frankfurt, Germany.,Brain Imaging Center, Goethe University, Frankfurt, Germany
| | - Ulrike Nöth
- Brain Imaging Center, Goethe University, Frankfurt, Germany.,Center for Personalized Translational Epilepsy Research (CePTER) Consortium, Goethe University, Frankfurt, Germany
| | - Pavel Hok
- Department of Neurology, Palacký University Olomouc and University Hospital Olomouc, Olomouc, Czechia
| | - Annemarie Reiländer
- Department of Neurology, Goethe University, Frankfurt, Germany.,Brain Imaging Center, Goethe University, Frankfurt, Germany
| | - Michelle Maiworm
- Department of Neurology, Goethe University, Frankfurt, Germany.,Brain Imaging Center, Goethe University, Frankfurt, Germany.,Center for Personalized Translational Epilepsy Research (CePTER) Consortium, Goethe University, Frankfurt, Germany
| | - Simon Baudrexel
- Department of Neurology, Goethe University, Frankfurt, Germany.,Brain Imaging Center, Goethe University, Frankfurt, Germany
| | - Sven Meuth
- Department of Neurology, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Felix Rosenow
- Department of Neurology, Goethe University, Frankfurt, Germany.,Center for Personalized Translational Epilepsy Research (CePTER) Consortium, Goethe University, Frankfurt, Germany.,Epilepsy Center Frankfurt Rhine-Main, Center of Neurology and Neurosurgery, University Hospital, Frankfurt, Germany
| | - Helmuth Steinmetz
- Department of Neurology, Goethe University, Frankfurt, Germany.,Center for Personalized Translational Epilepsy Research (CePTER) Consortium, Goethe University, Frankfurt, Germany
| | - Marlies Wagner
- Brain Imaging Center, Goethe University, Frankfurt, Germany.,Center for Personalized Translational Epilepsy Research (CePTER) Consortium, Goethe University, Frankfurt, Germany
| | - Elke Hattingen
- Center for Personalized Translational Epilepsy Research (CePTER) Consortium, Goethe University, Frankfurt, Germany.,Department of Neuroradiology, Goethe University, Frankfurt, Germany
| | - Ralf Deichmann
- Brain Imaging Center, Goethe University, Frankfurt, Germany.,Center for Personalized Translational Epilepsy Research (CePTER) Consortium, Goethe University, Frankfurt, Germany
| | - René-Maxime Gracien
- Department of Neurology, Goethe University, Frankfurt, Germany.,Brain Imaging Center, Goethe University, Frankfurt, Germany.,Center for Personalized Translational Epilepsy Research (CePTER) Consortium, Goethe University, Frankfurt, Germany
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35
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Jun Y, Shin H, Eo T, Kim T, Hwang D. Deep model-based magnetic resonance parameter mapping network (DOPAMINE) for fast T1 mapping using variable flip angle method. Med Image Anal 2021; 70:102017. [PMID: 33721693 DOI: 10.1016/j.media.2021.102017] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 02/19/2021] [Accepted: 02/22/2021] [Indexed: 11/15/2022]
Abstract
Quantitative tissue characteristics, which provide valuable diagnostic information, can be represented by magnetic resonance (MR) parameter maps using magnetic resonance imaging (MRI); however, a long scan time is necessary to acquire them, which prevents the application of quantitative MR parameter mapping to real clinical protocols. For fast MR parameter mapping, we propose a deep model-based MR parameter mapping network called DOPAMINE that combines a deep learning network with a model-based method to reconstruct MR parameter maps from undersampled multi-channel k-space data. DOPAMINE consists of two networks: 1) an MR parameter mapping network that uses a deep convolutional neural network (CNN) that estimates initial parameter maps from undersampled k-space data (CNN-based mapping), and 2) a reconstruction network that removes aliasing artifacts in the parameter maps with a deep CNN (CNN-based reconstruction) and an interleaved data consistency layer by an embedded MR model-based optimization procedure. We demonstrated the performance of DOPAMINE in brain T1 map reconstruction with a variable flip angle (VFA) model. To evaluate the performance of DOPAMINE, we compared it with conventional parallel imaging, low-rank based reconstruction, model-based reconstruction, and state-of-the-art deep-learning-based mapping methods for three different reduction factors (R = 3, 5, and 7) and two different sampling patterns (1D Cartesian and 2D Poisson-disk). Quantitative metrics indicated that DOPAMINE outperformed other methods in reconstructing T1 maps for all sampling patterns and reduction factors. DOPAMINE exhibited quantitatively and qualitatively superior performance to that of conventional methods in reconstructing MR parameter maps from undersampled multi-channel k-space data. The proposed method can thus reduce the scan time of quantitative MR parameter mapping that uses a VFA model.
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Affiliation(s)
- Yohan Jun
- School of Electrical and Electronic Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea
| | - Hyungseob Shin
- School of Electrical and Electronic Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea
| | - Taejoon Eo
- School of Electrical and Electronic Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea
| | - Taeseong Kim
- School of Electrical and Electronic Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea
| | - Dosik Hwang
- School of Electrical and Electronic Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea.
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36
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Erramuzpe A, Schurr R, Yeatman JD, Gotlib IH, Sacchet MD, Travis KE, Feldman HM, Mezer AA. A Comparison of Quantitative R1 and Cortical Thickness in Identifying Age, Lifespan Dynamics, and Disease States of the Human Cortex. Cereb Cortex 2021; 31:1211-1226. [PMID: 33095854 PMCID: PMC8485079 DOI: 10.1093/cercor/bhaa288] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 08/25/2020] [Accepted: 09/03/2020] [Indexed: 07/22/2023] Open
Abstract
Brain development and aging are complex processes that unfold in multiple brain regions simultaneously. Recently, models of brain age prediction have aroused great interest, as these models can potentially help to understand neurological diseases and elucidate basic neurobiological mechanisms. We test whether quantitative magnetic resonance imaging can contribute to such age prediction models. Using R1, the longitudinal rate of relaxation, we explore lifespan dynamics in cortical gray matter. We compare R1 with cortical thickness, a well-established biomarker of brain development and aging. Using 160 healthy individuals (6-81 years old), we found that R1 and cortical thickness predicted age similarly, but the regions contributing to the prediction differed. Next, we characterized R1 development and aging dynamics. Compared with anterior regions, in posterior regions we found an earlier R1 peak but a steeper postpeak decline. We replicate these findings: firstly, we tested a subset (N = 10) of the original dataset for whom we had additional scans at a lower resolution; and second, we verified the results on an independent dataset (N = 34). Finally, we compared the age prediction models on a subset of 10 patients with multiple sclerosis. The patients are predicted older than their chronological age using R1 but not with cortical thickness.
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Affiliation(s)
| | - R Schurr
- The Hebrew University of Jerusalem, The Edmond and Lily Safra Center for Brain Sciences, Jerusalem, Israel
| | - J D Yeatman
- Graduate School of Education, Stanford University, Stanford, CA, USA
- Division of Developmental-Behavioral Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - I H Gotlib
- Psychology, Stanford University, Stanford, CA, USA
| | - M D Sacchet
- Harvard Medical School, Center for Depression, Anxiety, and Stress Research, McLean Hospital, Belmont, MA, USA
| | - K E Travis
- Pediatrics, Stanford University, Stanford, CA, USA
| | - H M Feldman
- Development and Behavior Unit, Stanford University, Stanford, CA, USA
| | - A A Mezer
- The Hebrew University of Jerusalem, The Edmond and Lily Safra Center for Brain Sciences, Jerusalem, Israel
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37
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Gräfe D, Frahm J, Merkenschlager A, Voit D, Hirsch FW. Quantitative T1 mapping of the normal brain from early infancy to adulthood. Pediatr Radiol 2021; 51:450-456. [PMID: 33068131 PMCID: PMC7897197 DOI: 10.1007/s00247-020-04842-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Revised: 07/12/2020] [Accepted: 09/07/2020] [Indexed: 12/22/2022]
Abstract
BACKGROUND Quantitative mapping of MRI relaxation times is expected to uncover pathological processes in the brain more subtly than standard MRI techniques with weighted contrasts. So far, however, most mapping techniques suffer from a long measuring time, low spatial resolution or even sensitivity to magnetic field inhomogeneity. OBJECTIVE To obtain T1 relaxation times of the normal brain from early infancy to adulthood using a novel technique for fast and accurate T1 mapping at high spatial resolution. MATERIALS AND METHODS We performed whole-brain T1 mapping within less than 3 min in 100 patients between 2 months and 18 years of age with normal brain at a field strength of 3 T. We analyzed T1 relaxation times in several gray-matter nuclei and white matter. Subsequently, we derived regression equations for mean value and confidence interval. RESULTS T1 relaxation times of the pediatric brain rapidly decrease in all regions within the first 3 years of age, followed by a significantly weaker decrease until adulthood. These characteristics are more pronounced in white matter than in deep gray matter. CONCLUSION Regardless of age, quantitative T1 mapping of the pediatric brain is feasible in clinical practice. Normal age-dependent values should contribute to improved discrimination of subtle intracerebral alterations.
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Affiliation(s)
- Daniel Gräfe
- Department of Pediatric Radiology, University of Leipzig, Liebigstraße 20a, 04103, Leipzig, Germany.
| | - Jens Frahm
- Biomedizinische NMR, Max-Planck-Institut für biophysikalische Chemie, Göttingen, Germany
| | | | - Dirk Voit
- Biomedizinische NMR, Max-Planck-Institut für biophysikalische Chemie, Göttingen, Germany
| | - Franz Wolfgang Hirsch
- Department of Pediatric Radiology, University of Leipzig, Liebigstraße 20a, 04103, Leipzig, Germany
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38
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Lommers E, Guillemin C, Reuter G, Fouarge E, Delrue G, Collette F, Degueldre C, Balteau E, Maquet P, Phillips C. Voxel-Based quantitative MRI reveals spatial patterns of grey matter alteration in multiple sclerosis. Hum Brain Mapp 2020; 42:1003-1012. [PMID: 33155763 PMCID: PMC7856642 DOI: 10.1002/hbm.25274] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 10/10/2020] [Accepted: 10/22/2020] [Indexed: 12/22/2022] Open
Abstract
Despite robust postmortem evidence and potential clinical importance of gray matter (GM) pathology in multiple sclerosis (MS), assessing GM damage by conventional magnetic resonance imaging (MRI) remains challenging. This prospective cross‐sectional study aimed at characterizing the topography of GM microstructural and volumetric alteration in MS using, in addition to brain atrophy measures, three quantitative MRI (qMRI) parameters—magnetization transfer (MT) saturation, longitudinal (R1), and effective transverse (R2*) relaxation rates, derived from data acquired during a single scanning session. Our study involved 35 MS patients (14 relapsing–remitting MS; 21 primary or secondary progressive MS) and 36 age‐matched healthy controls (HC). The qMRI maps were computed and segmented in different tissue classes. Voxel‐based quantification (VBQ) and voxel‐based morphometry (VBM) statistical analyses were carried out using multiple linear regression models. In MS patients compared with HC, three configurations of GM microstructural/volumetric alterations were identified. (a) Co‐localization of GM atrophy with significant reduction of MT, R1, and/or R2*, usually observed in primary cortices. (b) Microstructural modifications without significant GM loss: hippocampus and paralimbic cortices, showing reduced MT and/or R1 values without significant atrophy. (c) Atrophy without significant change in microstructure, identified in deep GM nuclei. In conclusion, this quantitative multiparametric voxel‐based approach reveals three different spatially‐segregated combinations of GM microstructural/volumetric alterations in MS that might be associated with different neuropathology.
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Affiliation(s)
- Emilie Lommers
- GIGA - CRC in vivo imaging, University of Liège, Liège, Belgium.,Clinical Neuroimmunology Unit, Neurology Department, CHU Liège, Liège, Belgium
| | - Camille Guillemin
- GIGA - CRC in vivo imaging, University of Liège, Liège, Belgium.,Psychology and Neuroscience of Cognition Research Unit, University of Liège, Liège, Belgium
| | - Gilles Reuter
- GIGA - CRC in vivo imaging, University of Liège, Liège, Belgium.,Neurosurgery Department, CHU Liège, Liège, Belgium
| | - Eve Fouarge
- GIGA - CRC in vivo imaging, University of Liège, Liège, Belgium
| | - Gaël Delrue
- Clinical Neuroimmunology Unit, Neurology Department, CHU Liège, Liège, Belgium
| | - Fabienne Collette
- GIGA - CRC in vivo imaging, University of Liège, Liège, Belgium.,Psychology and Neuroscience of Cognition Research Unit, University of Liège, Liège, Belgium
| | | | - Evelyne Balteau
- GIGA - CRC in vivo imaging, University of Liège, Liège, Belgium
| | - Pierre Maquet
- GIGA - CRC in vivo imaging, University of Liège, Liège, Belgium.,Clinical Neuroimmunology Unit, Neurology Department, CHU Liège, Liège, Belgium
| | - Christophe Phillips
- GIGA - CRC in vivo imaging, University of Liège, Liège, Belgium.,GIGA - in silico medicine, University of Liège, Liège, Belgium
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Ma S, Wang N, Fan Z, Kaisey M, Sicotte NL, Christodoulou AG, Li D. Three-dimensional whole-brain simultaneous T1, T2, and T1ρ quantification using MR Multitasking: Method and initial clinical experience in tissue characterization of multiple sclerosis. Magn Reson Med 2020; 85:1938-1952. [PMID: 33107126 DOI: 10.1002/mrm.28553] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 09/22/2020] [Accepted: 09/23/2020] [Indexed: 02/06/2023]
Abstract
PURPOSE To develop a 3D whole-brain simultaneous T1/T2/T1ρ quantification method with MR Multitasking that provides high quality, co-registered multiparametric maps in 9 min. METHODS MR Multitasking conceptualizes T1/T2/T1ρ relaxations as different time dimensions, simultaneously resolving all three dimensions with a low-rank tensor image model. The proposed method was validated on a phantom and in healthy volunteers, comparing quantitative measurements against corresponding reference methods and evaluating the scan-rescan repeatability. Initial clinical validation was performed in age-matched relapsing-remitting multiple sclerosis (RRMS) patients to examine the feasibility of quantitative tissue characterization and to compare with the healthy control cohort. The feasibility of synthesizing six contrast-weighted images was also examined. RESULTS Our framework produced high quality, co-registered T1/T2/T1ρ maps that closely resemble the reference maps. Multitasking T1/T2/T1ρ measurements showed substantial agreement with reference measurements on the phantom and in healthy controls. Bland-Altman analysis indicated good in vivo repeatability of all three parameters. In RRMS patients, lesions were conspicuously delineated on all three maps and on four synthetic weighted images (T2-weighted, T2-FLAIR, double inversion recovery, and a novel "T1ρ-FLAIR" contrast). T1 and T2 showed significant differences for normal appearing white matter between patients and controls, while T1ρ showed significant differences for normal appearing white matter, cortical gray matter, and deep gray matter. The combination of three parameters significantly improved the differentiation between RRMS patients and healthy controls, compared to using any single parameter alone. CONCLUSION MR Multitasking simultaneously quantifies whole-brain T1/T2/T1ρ and is clinically promising for quantitative tissue characterization of neurological diseases, such as MS.
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Affiliation(s)
- Sen Ma
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.,Department of Bioengineering, University of California, Los Angeles, California, USA
| | - Nan Wang
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.,Department of Bioengineering, University of California, Los Angeles, California, USA
| | - Zhaoyang Fan
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.,Department of Bioengineering, University of California, Los Angeles, California, USA
| | - Marwa Kaisey
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Nancy L Sicotte
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Anthony G Christodoulou
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.,Department of Bioengineering, University of California, Los Angeles, California, USA
| | - Debiao Li
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.,Department of Bioengineering, University of California, Los Angeles, California, USA
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40
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Benedict RHB, Amato MP, DeLuca J, Geurts JJG. Cognitive impairment in multiple sclerosis: clinical management, MRI, and therapeutic avenues. Lancet Neurol 2020; 19:860-871. [PMID: 32949546 PMCID: PMC10011205 DOI: 10.1016/s1474-4422(20)30277-5] [Citation(s) in RCA: 294] [Impact Index Per Article: 73.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 07/14/2020] [Accepted: 07/21/2020] [Indexed: 12/15/2022]
Abstract
Multiple sclerosis is a chronic, demyelinating disease of the CNS. Cognitive impairment is a sometimes neglected, yet common, sign and symptom with a profound effect on instrumental activities of daily living. The prevalence of cognitive impairment in multiple sclerosis varies across the lifespan and might be difficult to distinguish from other causes in older age. MRI studies show that widespread changes to brain networks contribute to cognitive dysfunction, and grey matter atrophy is an early sign of potential future cognitive decline. Neuropsychological research suggests that cognitive processing speed and episodic memory are the most frequently affected cognitive domains. Narrowing evaluation to these core areas permits brief, routine assessment in the clinical setting. Owing to its brevity, reliability, and sensitivity, the Symbol Digit Modalities Test, or its computer-based analogues, can be used to monitor episodes of acute disease activity. The Symbol Digit Modalities Test can also be used in clinical trials, and data increasingly show that cognitive processing speed and memory are amenable to cognitive training interventions.
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Affiliation(s)
- Ralph H B Benedict
- Department of Neurology and Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA.
| | - Maria Pia Amato
- Department of Neurology, University of Florence, IRCCS Fondazione Don Carlo Gnocchi, Florence, Italy
| | | | - Jeroen J G Geurts
- Department of Anatomy and Neurosciences, Section Clinical Neuroscience, Amsterdam UMC, Location VUmc, Vrije Universiteit, Amsterdam, Netherlands
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Liu X, Gómez PA, Solana AB, Wiesinger F, Menzel MI, Menze BH. Silent 3D MR sequence for quantitative and multicontrast T1 and proton density imaging. Phys Med Biol 2020; 65:185010. [PMID: 32663809 DOI: 10.1088/1361-6560/aba5e8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
This study aims to develop a silent, fast and 3D method for T1 and proton density (PD) mapping, while generating time series of T1-weighted (T1w) images with bias-field correction. Undersampled T1w images at different effective inversion times (TIs) were acquired using the inversion recovery prepared RUFIS sequence with an interleaved k-space trajectory. Unaliased images were reconstructed by constraining the signal evolution to a temporal subspace which was learned from the signal model. Parameter maps were obtained by fitting the data to the signal model, and bias-field correction was conducted on T1w images. Accuracy and repeatability of the method was accessed in repeated experiments with phantom and volunteers. For the phantom study, T1 values obtained by the proposed method were highly consistent with values from the gold standard method, R2 = 0.9976. Coefficients of variation (CVs) ranged from 0.09% to 0.83%. For the volunteer study, T1 values from gray and white matter regions were consistent with literature values, and peaks of gray and white matter can be clearly delineated on whole-brain T1 histograms. CVs ranged from 0.01% to 2.30%. The acoustic noise measured at the scanner isocenter was 2.6 dBA higher compared to the in-bore background. Rapid and with low acoustic noise, the proposed method is shown to produce accurate T1 and PD maps with high repeatability by reconstructing sparsely sampled T1w images at different TIs using temporal subspace. Our approach can greatly enhance patient comfort during examination and therefore increase the acceptance of the procedure.
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Affiliation(s)
- Xin Liu
- Technical University Munich, Garching, Germany. GE Global Research Europe, Munich, Germany
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42
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Heule R, Bause J, Pusterla O, Scheffler K. Multi-parametric artificial neural network fitting of phase-cycled balanced steady-state free precession data. Magn Reson Med 2020; 84:2981-2993. [PMID: 32479661 DOI: 10.1002/mrm.28325] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Revised: 04/22/2020] [Accepted: 04/27/2020] [Indexed: 12/16/2022]
Abstract
PURPOSE Standard relaxation time quantification using phase-cycled balanced steady-state free precession (bSSFP), eg, motion-insensitive rapid configuration relaxometry (MIRACLE), is subject to a considerable underestimation of tissue T1 and T2 due to asymmetric intra-voxel frequency distributions. In this work, an artificial neural network (ANN) fitting approach is proposed to simultaneously extract accurate reference relaxation times (T1 , T2 ) and robust field map estimates ( B 1 + , ΔB0 ) from the bSSFP profile. METHODS Whole-brain bSSFP data acquired at 3T were used for the training of a feedforward ANN with N = 12, 6, and 4 phase-cycles. The magnitude and phase of the Fourier transformed complex bSSFP frequency response served as input and the multi-parametric reference set [T1 , T2 , B 1 + , ∆B0 ] as target. The ANN predicted relaxation times were validated against the target and MIRACLE. RESULTS The ANN prediction of T1 and T2 for trained and untrained data agreed well with the reference, even for only four acquired phase-cycles. In contrast, relaxometry based on 4-point MIRACLE was prone to severe off-resonance-related artifacts. ANN predicted B 1 + and ∆B0 maps showed the expected spatial inhomogeneity patterns in high agreement with the reference measurements for 12-point, 6-point, and 4-point bSSFP phase-cycling schemes. CONCLUSION ANNs show promise to provide accurate brain tissue T1 and T2 values as well as reliable field map estimates. Moreover, the bSSFP acquisition can be accelerated by reducing the number of phase-cycles while still delivering robust T1 , T2 , B 1 + , and ∆B0 estimates.
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Affiliation(s)
- Rahel Heule
- High Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Jonas Bause
- High Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Orso Pusterla
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland.,Division of Radiological Physics, Department of Radiology, University Hospital Basel, Basel, Switzerland.,Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | - Klaus Scheffler
- High Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,Department of Biomedical Magnetic Resonance, University of Tübingen, Tübingen, Germany
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Fooladi M, Riyahi Alam N, Sharini H, Firouznia K, Shakiba M, Harirchian M. Multiparametric qMTI Assessment and Monitoring of Normal Appearing White Matter and Classified T1 Hypointense Lesions in Relapsing-Remitting Multiple Sclerosis. Ing Rech Biomed 2020. [DOI: 10.1016/j.irbm.2020.01.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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44
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Wei Z, Ma YJ, Jang H, Yang W, Du J. To measure T 1 of short T 2 species using an inversion recovery prepared three-dimensional ultrashort echo time (3D IR-UTE) method: A phantom study. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2020; 314:106725. [PMID: 32320926 PMCID: PMC7307614 DOI: 10.1016/j.jmr.2020.106725] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Revised: 03/20/2020] [Accepted: 04/02/2020] [Indexed: 05/04/2023]
Abstract
PURPOSE To demonstrate the feasibility of a new method for measuring T1 of short T2 species based on an adiabatic inversion recovery-prepared three-dimensional ultrashort echo time Cones (3D IR-UTE-Cones) sequence. METHODS T1 values for short T2 species were quantified using 3D IR-UTE-Cones data acquired with different repetition times (TRs) and inversion times (TIs). An inversion efficiency factor Q was introduced into the fitting model to accurately calculate T1 values for short T2 species. Experiments were performed on twelve MnCl2 aqueous solution phantoms with a wide range of T1 values and T2* values on a 3 T clinical MR system to verify the efficacy of the proposed method. For comparison, a variable flip angle UTE (VFA-UTE) sequence, a variable TR UTE (VTR-UTE) sequence, and a conventional 2D IR fast spin echo (IR-FSE) sequence were also used to quantify T1 values of those phantoms. T1 values were compared between all performed sequences. RESULTS The proposed 3D IR-UTE-Cones method provided higher contrast images of short T2 phantoms and measured much shorter T1 values than the VFA-UTE, VTR-UTE and 2D IR-FSE methods. T1 values as short as 2.95 ms could be measured by the 3D IR-UTE-Cones sequence. The 3D IR-UTE-Cones methods with different TRs were applied to different ranges of T1 measurement, and the scan time was significantly decreased by using 5 TIs along the recovery curves to perform fitting with comparable accuracy. CONCLUSION The 3D IR-UTE-Cones sequence could accurately measure short T1 values while providing high contrast images of short T2 species.
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Affiliation(s)
- Zhao Wei
- Department of Radiology, University of California San Diego, CA, United States; University of Chinese Academy of Sciences, Beijing, China; Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing, China.
| | - Ya-Jun Ma
- Department of Radiology, University of California San Diego, CA, United States.
| | - Hyungseok Jang
- Department of Radiology, University of California San Diego, CA, United States.
| | - Wenhui Yang
- University of Chinese Academy of Sciences, Beijing, China; Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing, China.
| | - Jiang Du
- Department of Radiology, University of California San Diego, CA, United States.
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45
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Cafiero R, Brauer J, Anwander A, Friederici AD. The Concurrence of Cortical Surface Area Expansion and White Matter Myelination in Human Brain Development. Cereb Cortex 2020; 29:827-837. [PMID: 30462166 PMCID: PMC6319170 DOI: 10.1093/cercor/bhy277] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2018] [Accepted: 10/11/2018] [Indexed: 02/03/2023] Open
Abstract
The human brain undergoes dramatic structural changes during childhood that co-occur with behavioral development. These age-related changes are documented for the brain’s gray matter and white matter. However, their interrelation is largely unknown. In this study, we investigated age-related effects in cortical thickness (CT) and in cortical surface area (SA) as parts of the gray matter volume as well as age effects in T1 relaxation times in the white matter. Data from N = 170 children between the ages of 3 and 7 years contributed to the sample. We found a high spatial overlap of age-related correlations between SA and T1 relaxation times of the corresponding white matter connections, but no such relation between SA and CT. These results indicate that during childhood the developmental expansion of the cortical surface goes hand-in-hand with age-related increase of white matter fiber connections terminating in the cortical surface.
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Affiliation(s)
- Riccardo Cafiero
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Jens Brauer
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Alfred Anwander
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Angela D Friederici
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
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Stock B, Shrestha M, Seiler A, Foerch C, Hattingen E, Steinmetz H, Deichmann R, Wagner M, Gracien RM. Distribution of Cortical Diffusion Tensor Imaging Changes in Multiple Sclerosis. Front Physiol 2020; 11:116. [PMID: 32231581 PMCID: PMC7083109 DOI: 10.3389/fphys.2020.00116] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Accepted: 01/31/2020] [Indexed: 11/13/2022] Open
Abstract
Purpose Diffuse cortical damage in relapsing–remitting multiple sclerosis (RRMS) is clinically relevant but cannot be directly assessed with conventional MRI. In this study, it was aimed to use diffusion tensor imaging (DTI) techniques with optimized intrinsic eddy current compensation to quantify and characterize cortical mean diffusivity (MD) and fractional anisotropy (FA) changes in RRMS and to analyze the distribution of these changes across the cortex. Materials and Methods Three-Tesla MRI acquisition, mapping of the MD providing information about the integrity of microstructural barriers and of the FA reflecting axonal density and surface-based analysis with Freesurfer were performed for 24 RRMS patients and 25 control subjects. Results Across the whole cortex, MD was increased in patients (p < 0.001), while surface-based analysis revealed focal cortical FA decreases. MD and FA changes were distributed inhomogeneously across the cortex, the MD increase being more widespread than the FA decrease. Cortical MD correlated with the Expanded Disability Status Scale (EDSS, r = 0.38, p = 0.03). Conclusion Damage of microstructural barriers occurs inhomogeneously across the cortex in RRMS and might be spatially more widespread than axonal degeneration. The results and, in particular, the correlation with the clinical status indicate that DTI might be a promising technique for the monitoring of cortical damage under treatment in larger clinical studies.
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Affiliation(s)
- Benjamin Stock
- Department of Neurology, Goethe University Frankfurt, Frankfurt, Germany.,Department of Neuroradiology, Goethe University Frankfurt, Frankfurt, Germany.,Brain Imaging Center, Goethe University Frankfurt, Frankfurt, Germany
| | - Manoj Shrestha
- Brain Imaging Center, Goethe University Frankfurt, Frankfurt, Germany
| | - Alexander Seiler
- Department of Neurology, Goethe University Frankfurt, Frankfurt, Germany.,Brain Imaging Center, Goethe University Frankfurt, Frankfurt, Germany
| | - Christian Foerch
- Department of Neurology, Goethe University Frankfurt, Frankfurt, Germany
| | - Elke Hattingen
- Department of Neuroradiology, Goethe University Frankfurt, Frankfurt, Germany
| | - Helmuth Steinmetz
- Department of Neurology, Goethe University Frankfurt, Frankfurt, Germany
| | - Ralf Deichmann
- Brain Imaging Center, Goethe University Frankfurt, Frankfurt, Germany
| | - Marlies Wagner
- Department of Neuroradiology, Goethe University Frankfurt, Frankfurt, Germany
| | - René-Maxime Gracien
- Department of Neurology, Goethe University Frankfurt, Frankfurt, Germany.,Brain Imaging Center, Goethe University Frankfurt, Frankfurt, Germany
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Kirov II, Tal A. Potential clinical impact of multiparametric quantitative MR spectroscopy in neurological disorders: A review and analysis. Magn Reson Med 2020; 83:22-44. [PMID: 31393032 PMCID: PMC6814297 DOI: 10.1002/mrm.27912] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Revised: 06/06/2019] [Accepted: 06/29/2019] [Indexed: 12/12/2022]
Abstract
PURPOSE Unlike conventional MR spectroscopy (MRS), which only measures metabolite concentrations, multiparametric MRS also quantifies their longitudinal (T1 ) and transverse (T2 ) relaxation times, as well as the radiofrequency transmitter inhomogeneity (B1+ ). To test whether knowledge of these additional parameters can improve the clinical utility of brain MRS, we compare the conventional and multiparametric approaches in terms of expected classification accuracy in differentiating controls from patients with neurological disorders. THEORY AND METHODS A literature review was conducted to compile metabolic concentrations and relaxation times in a wide range of neuropathologies and regions of interest. Simulations were performed to construct receiver operating characteristic curves and compute the associated areas (area under the curve) to examine the sensitivity and specificity of MRS for detecting each pathology in each region. Classification accuracy was assessed using metabolite concentrations corrected using population-averages for T1 , T2 , and B1+ (conventional MRS); using metabolite concentrations corrected using per-subject values (multiparametric MRS); and using an optimal linear multiparametric estimator comprised of the metabolites' concentrations and relaxation constants (multiparametric MRS). Additional simulations were conducted to find the minimal intra-subject precision needed for each parameter. RESULTS Compared with conventional MRS, multiparametric approaches yielded area under the curve improvements for almost all neuropathologies and regions of interest. The median area under the curve increased by 0.14 over the entire dataset, and by 0.24 over the 10 instances with the largest individual increases. CONCLUSIONS Multiparametric MRS can substantially improve the clinical utility of MRS in diagnosing and assessing brain pathology, motivating the design and use of novel multiparametric sequences.
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Affiliation(s)
- Ivan I. Kirov
- Center for Advanced Imaging Innovation and Research (CAIR), Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, Department of Radiology, 660 1 Avenue, New York, NY 10016, United States of America
| | - Assaf Tal
- Department of Chemical and Biological Physics, Weizmann Institute of Science, 234 Herzel St., Rehovot 7610001, Israel
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48
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Du XF, Liu J, Hua QF, Wu YJ. Relapsing-Remitting Multiple Sclerosis Is Associated With Regional Brain Activity Deficits in Motor- and Cognitive-Related Brain Areas. Front Neurol 2019; 10:1136. [PMID: 31849801 PMCID: PMC6901942 DOI: 10.3389/fneur.2019.01136] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2019] [Accepted: 10/10/2019] [Indexed: 11/14/2022] Open
Abstract
Objective: To identify the abnormal regional spontaneous brain activity associated with relapsing-remitting multiple sclerosis (RRMS) using fractional amplitude of low-frequency fluctuation (fALFF) analysis and their relationships with clinical features. Methods: A total of 26 RRMS (11 males, 15 females; age, 36.58 ± 10.82 years) and 27 status-matched healthy group (HGs; 12 males, 15 females; age, 35.85 ± 12.05 years) underwent an Expanded Disability Status Scale (EDSS) examination. fALFF was applied to evaluate the abnormal regional brain activity associated with RRMS. Pearson's correlation analysis was applied to calculate the correlations between the signal values of brain areas that exhibited abnormal fALFF values and clinical features. Receiver operating characteristic (ROC) curve was performed to evaluate the sensitivity and specificity of those altered brain areas to distinguish between RRMS and HGs. Results: Compared with HGs, RRMS exhibited higher fALFF in the right cerebellum posterior lobe, left orbitofrontal cortex, left dorsolateral prefrontal cortex, bilateral supplementary motor area, and right fusiform gyrus and lower fALFF values in the left hippocampus and right precuneus. ROC revealed that these areas showed two good and five fair AUC values (0.77 ± 0.03, 0.729~0.822). However, four combinations with more than five brain regions received the same best discriminatory power with a sensitivity of 96.3% and a specificity of 88.5%. EDSS revealed a negative correlation with supplementary motor area (r = −0.395, p = 0.046). Conclusions: RRMS is associated with abnormal regional brain activity deficits of motor- and cognitive-related areas. The fALFF parameter may serve as a potential biological marker to discriminate between the two groups.
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Affiliation(s)
- Xiao-Feng Du
- Department of Radiology, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, China
| | - Jiao Liu
- Department of Radiology, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, China
| | - Qi-Feng Hua
- Department of Radiology, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, China
| | - Yi-Jiao Wu
- Department of Radiology, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, China
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49
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Swanberg KM, Landheer K, Pitt D, Juchem C. Quantifying the Metabolic Signature of Multiple Sclerosis by in vivo Proton Magnetic Resonance Spectroscopy: Current Challenges and Future Outlook in the Translation From Proton Signal to Diagnostic Biomarker. Front Neurol 2019; 10:1173. [PMID: 31803127 PMCID: PMC6876616 DOI: 10.3389/fneur.2019.01173] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2019] [Accepted: 10/21/2019] [Indexed: 01/03/2023] Open
Abstract
Proton magnetic resonance spectroscopy (1H-MRS) offers a growing variety of methods for querying potential diagnostic biomarkers of multiple sclerosis in living central nervous system tissue. For the past three decades, 1H-MRS has enabled the acquisition of a rich dataset suggestive of numerous metabolic alterations in lesions, normal-appearing white matter, gray matter, and spinal cord of individuals with multiple sclerosis, but this body of information is not free of seeming internal contradiction. The use of 1H-MRS signals as diagnostic biomarkers depends on reproducible and generalizable sensitivity and specificity to disease state that can be confounded by a multitude of influences, including experiment group classification and demographics; acquisition sequence; spectral quality and quantifiability; the contribution of macromolecules and lipids to the spectroscopic baseline; spectral quantification pipeline; voxel tissue and lesion composition; T1 and T2 relaxation; B1 field characteristics; and other features of study design, spectral acquisition and processing, and metabolite quantification about which the experimenter may possess imperfect or incomplete information. The direct comparison of 1H-MRS data from individuals with and without multiple sclerosis poses a special challenge in this regard, as several lines of evidence suggest that experimental cohorts may differ significantly in some of these parameters. We review the existing findings of in vivo1H-MRS on central nervous system metabolic abnormalities in multiple sclerosis and its subtypes within the context of study design, spectral acquisition and processing, and metabolite quantification and offer an outlook on technical considerations, including the growing use of machine learning, by future investigations into diagnostic biomarkers of multiple sclerosis measurable by 1H-MRS.
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Affiliation(s)
- Kelley M Swanberg
- Department of Biomedical Engineering, Columbia University Fu Foundation School of Engineering and Applied Science, New York, NY, United States
| | - Karl Landheer
- Department of Biomedical Engineering, Columbia University Fu Foundation School of Engineering and Applied Science, New York, NY, United States
| | - David Pitt
- Department of Neurology, Yale University School of Medicine, New Haven, CT, United States
| | - Christoph Juchem
- Department of Biomedical Engineering, Columbia University Fu Foundation School of Engineering and Applied Science, New York, NY, United States.,Department of Radiology, Columbia University College of Physicians and Surgeons, New York, NY, United States
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50
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Abstract
Magnetic resonance imaging (MRI) has been used extensively in revealing pathological changes in the central nervous system. However, to date, MRI is very much underutilized in evaluating the peripheral nervous system (PNS). This underutilization is generally due to two perceived weaknesses in MRI: first, the need for very high resolution to image the small structures within the peripheral nerves to visualize morphological changes; second, the lack of normative data in MRI of the PNS and this makes reliable interpretation of the data difficult. This article reviews current state-of-the-art capabilities in
in vivo MRI of human peripheral nerves. It aims to identify areas where progress has been made and those that still require further improvement. In particular, with many new therapies on the horizon, this review addresses how MRI can be used to provide non-invasive and objective biomarkers in the evaluation of peripheral neuropathies. Although a number of techniques are available in diagnosing and tracking pathologies in the PNS, those techniques typically target the distal peripheral nerves, and distal nerves may be completely degenerated during the patient’s first clinic visit. These techniques may also not be able to access the proximal nerves deeply embedded in the tissue. Peripheral nerve MRI would be an alternative to circumvent these problems. In order to address the pressing clinical needs, this review closes with a clinical protocol at 3T that will allow high-resolution, high-contrast, quantitative MRI of the proximal peripheral nerves.
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Affiliation(s)
- Yongsheng Chen
- Department of Neurology, Wayne State University School of Medicine, Detroit, MI, 48201, USA
| | - E Mark Haacke
- Department of Radiology, Wayne State University School of Medicine, Detroit, MI, 48201, USA
| | - Jun Li
- Department of Neurology, Wayne State University School of Medicine, Detroit, MI, 48201, USA.,Center for Molecular Medicine & Genetics, Wayne State University School of Medicine, Detroit, MI, 48201, USA.,Department of Biochemistry, Microbiology and Immunology, Wayne State University School of Medicine, Detroit, MI, 48201, USA.,John D. Dingell VA Medical Center, Detroit, MI, 48201, USA
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