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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: 73] [Impact Index Per Article: 24.3] [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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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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Tang X, Cai F, Ding DX, Zhang LL, Cai XY, Fang Q. Magnetic resonance imaging relaxation time in Alzheimer's disease. Brain Res Bull 2018; 140:176-189. [PMID: 29738781 DOI: 10.1016/j.brainresbull.2018.05.004] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2018] [Revised: 04/18/2018] [Accepted: 05/04/2018] [Indexed: 12/26/2022]
Abstract
The magnetic resonance imaging (MRI) relaxation time constants, T1 and T2, are sensitive to changes in brain tissue microstructure integrity. Quantitative T1 and T2 relaxation times have been proposed to serve as non-invasive biomarkers of Alzheimer's disease (AD), in which alterations are believed to not only reflect AD-related neuropathology but also cognitive impairment. In this review, we summarize the applications and key findings of MRI techniques in the context of both AD subjects and AD transgenic mouse models. Furthermore, the possible mechanisms of relaxation time alterations in AD will be discussed. Future studies could focus on relaxation time alterations in the early stage of AD, and longitudinal studies are needed to further explore relaxation time alterations during disease progression.
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Affiliation(s)
- Xiang Tang
- Department of Neurology, The First Affiliated Hospital of Soochow University, No. 899, Pinghai Road, Suzhou, Jiangsu 215006, China
| | - Feng Cai
- Department of Orthopedics, The First Affiliated Hospital of Soochow University, No. 899, Pinghai Road, Suzhou, Jiangsu 215006, China
| | - Dong-Xue Ding
- Department of Neurology, The First Affiliated Hospital of Soochow University, No. 899, Pinghai Road, Suzhou, Jiangsu 215006, China
| | - Lu-Lu Zhang
- Department of Neurology, The First Affiliated Hospital of Soochow University, No. 899, Pinghai Road, Suzhou, Jiangsu 215006, China
| | - Xiu-Ying Cai
- Department of Neurology, The First Affiliated Hospital of Soochow University, No. 899, Pinghai Road, Suzhou, Jiangsu 215006, China.
| | - Qi Fang
- Department of Neurology, The First Affiliated Hospital of Soochow University, No. 899, Pinghai Road, Suzhou, Jiangsu 215006, China.
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Gabr RE, Pednekar AS, Govindarajan KA, Sun X, Riascos RF, Ramírez MG, Hasan KM, Lincoln JA, Nelson F, Wolinsky JS, Narayana PA. Patient-specific 3D FLAIR for enhanced visualization of brain white matter lesions in multiple sclerosis. J Magn Reson Imaging 2016; 46:557-564. [PMID: 27869333 DOI: 10.1002/jmri.25557] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2016] [Accepted: 11/01/2016] [Indexed: 12/22/2022] Open
Abstract
PURPOSE To improve the conspicuity of white matter lesions (WMLs) in multiple sclerosis (MS) using patient-specific optimization of single-slab 3D fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI). MATERIALS AND METHODS Sixteen MS patients were enrolled in a prospective 3.0T MRI study. FLAIR inversion time and echo time were automatically optimized for each patient during the same scan session based on measurements of the relative proton density and relaxation times of the brain tissues. The optimization criterion was to maximize the contrast between gray matter (GM) and white matter (WM), while suppressing cerebrospinal fluid. This criterion also helps increase the contrast between WMLs and WM. The performance of the patient-specific 3D FLAIR protocol relative to the fixed-parameter protocol was assessed both qualitatively and quantitatively. RESULTS Patient-specific optimization achieved a statistically significant 41% increase in the GM-WM contrast ratio (P < 0.05) and 32% increase in the WML-WM contrast ratio (P < 0.01) compared with fixed-parameter FLAIR. The increase in WML-WM contrast ratio correlated strongly with echo time (P < 10-11 ). Two experienced neuroradiologists indicated substantially higher lesion conspicuity on the patient-specific FLAIR images over conventional FLAIR in 3-4 cases (intrarater correlation coefficient ICC = 0.72). In no case was the image quality of patient-specific FLAIR considered inferior to conventional FLAIR by any of the raters (ICC = 0.32). CONCLUSION Changes in proton density and relaxation times render fixed-parameter FLAIR suboptimal in terms of lesion contrast. Patient-specific optimization of 3D FLAIR increases lesion conspicuity without scan time penalty, and has potential to enhance the detection of subtle and small lesions in MS. LEVEL OF EVIDENCE 1 Technical Efficacy: Stage 1 J. MAGN. RESON. IMAGING 2017;46:557-564.
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Affiliation(s)
- Refaat E Gabr
- Department of Diagnostic and Interventional Imaging, University of Texas Health Science Center at Houston (UTHealth), Houston, Texas, USA
| | | | - Koushik A Govindarajan
- Department of Diagnostic and Interventional Imaging, University of Texas Health Science Center at Houston (UTHealth), Houston, Texas, USA
| | - Xiaojun Sun
- Department of Diagnostic and Interventional Imaging, University of Texas Health Science Center at Houston (UTHealth), Houston, Texas, USA
| | - Roy F Riascos
- Department of Diagnostic and Interventional Imaging, University of Texas Health Science Center at Houston (UTHealth), Houston, Texas, USA
| | - María G Ramírez
- Department of Diagnostic and Interventional Imaging, University of Texas Health Science Center at Houston (UTHealth), Houston, Texas, USA
| | - Khader M Hasan
- Department of Diagnostic and Interventional Imaging, University of Texas Health Science Center at Houston (UTHealth), Houston, Texas, USA
| | - John A Lincoln
- Department of Neurology, University of Texas Health Science Center at Houston (UTHealth), Houston, Texas, USA
| | - Flavia Nelson
- Department of Neurology, University of Texas Health Science Center at Houston (UTHealth), Houston, Texas, USA
| | - Jerry S Wolinsky
- Department of Neurology, University of Texas Health Science Center at Houston (UTHealth), Houston, Texas, USA
| | - Ponnada A Narayana
- Department of Diagnostic and Interventional Imaging, University of Texas Health Science Center at Houston (UTHealth), Houston, Texas, USA
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Pasternak O, Kubicki M, Shenton ME. In vivo imaging of neuroinflammation in schizophrenia. Schizophr Res 2016; 173:200-212. [PMID: 26048294 PMCID: PMC4668243 DOI: 10.1016/j.schres.2015.05.034] [Citation(s) in RCA: 95] [Impact Index Per Article: 11.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2015] [Revised: 05/18/2015] [Accepted: 05/20/2015] [Indexed: 12/18/2022]
Abstract
In recent years evidence has accumulated to suggest that neuroinflammation might be an early pathology of schizophrenia that later leads to neurodegeneration, yet the exact role in the etiology, as well as the source of neuroinflammation, are still not known. The hypothesis of neuroinflammation involvement in schizophrenia is quickly gaining popularity, and thus it is imperative that we have reliable and reproducible tools and measures that are both sensitive, and, most importantly, specific to neuroinflammation. The development and use of appropriate human in vivo imaging methods can help in our understanding of the location and extent of neuroinflammation in different stages of the disorder, its natural time-course, and its relation to neurodegeneration. Thus far, there is little in vivo evidence derived from neuroimaging methods. This is likely the case because the methods that are specific and sensitive to neuroinflammation are relatively new or only just being developed. This paper provides a methodological review of both existing and emerging positron emission tomography and magnetic resonance imaging techniques that identify and characterize neuroinflammation. We describe \how these methods have been used in schizophrenia research. We also outline the shortcomings of existing methods, and we highlight promising future techniques that will likely improve state-of-the-art neuroimaging as a more refined approach for investigating neuroinflammation in schizophrenia.
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Affiliation(s)
- Ofer Pasternak
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215, USA; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215, USA; Department of Applied Mathematics, Tel Aviv University, Tel Aviv 69978, Israel.
| | - Marek Kubicki
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215, USA; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215, USA; Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02215, USA
| | - Martha E Shenton
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215, USA; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215, USA; VA Boston Healthcare System, Brockton, MA, USA
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Gabr RE, Hasan KM, Haque ME, Nelson FM, Wolinsky JS, Narayana PA. Optimal combination of FLAIR and T2-weighted MRI for improved lesion contrast in multiple sclerosis. J Magn Reson Imaging 2016; 44:1293-1300. [PMID: 27126898 DOI: 10.1002/jmri.25281] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2016] [Revised: 03/29/2016] [Accepted: 03/30/2016] [Indexed: 11/06/2022] Open
Abstract
PURPOSE Postacquisition combination of three-dimensional T2-weighted (T2w) and fluid-attenuated inversion recovery (FLAIR) images can improve the visualization of brain lesions in multiple sclerosis (MS). However, an optimal way to combine these images has not been described so far. The main objective of this study is to investigate an optimal combination of T2w and FLAIR to improve the conspicuity of MS lesions. MATERIALS AND METHODS We determined the parameters for a generalized multiplicative image combination which maximize the contrast-to-noise ratio (CNR) between lesions and normal-appearing brain tissue through simulations and verified experimentally. MRI data from 11 MS patients acquired at 3 Tesla were retrospectively analyzed using the proposed approach and compared with conventional FLAIR, and to images obtained by direct multiplication of T2w and FLAIR (FLAIR2 ). Image quality was assessed by region-of-interest analysis. In addition, to evaluate the degree of cerebrospinal fluid (CSF) suppression, CSF-to-gray matter (CSF/GM) ratio was calculated. Reduction in global image contrast was assessed by computing the reduction in the contrast of mid-level intensity values. RESULTS An optimal combination was found to be the third order expression: FLAIR3 = FLAIR1.55 × T2w1.45 . Compared with FLAIR, the lesion CNR was significantly increased by 1.9× (P < 0.005) and 2.5× (P < 0.001) using FLAIR2 and FLAIR3 , respectively. CSF/GM ratio was increased by 1.7× in FLAIR2 (P < 0.001) compared with FLAIR, while it was reduced to 0.7× on FLAIR3 (P < 0.05). The mid-intensity contrast was preserved on FLAIR2 (P = 0.2), and decreased by 29% on FLAIR3 (P < 0.001). CONCLUSION These results show that the optimized combination of FLAIR and T2w can improve MS lesion conspicuity. J. Magn. Reson. Imaging 2016;44:1293-1300.
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Affiliation(s)
- Refaat E Gabr
- Department of Diagnostic and Interventional Imaging, University of Texas Health Science Center at Houston (UTHealth), Houston, Texas, USA.
| | - Khader M Hasan
- Department of Diagnostic and Interventional Imaging, University of Texas Health Science Center at Houston (UTHealth), Houston, Texas, USA
| | - Muhammad E Haque
- Department of Neurology, University of Texas Health Science Center at Houston (UTHealth), Houston, Texas, USA
| | - Flavia M Nelson
- Department of Neurology, University of Texas Health Science Center at Houston (UTHealth), Houston, Texas, USA
| | - Jerry S Wolinsky
- Department of Neurology, University of Texas Health Science Center at Houston (UTHealth), Houston, Texas, USA
| | - Ponnada A Narayana
- Department of Diagnostic and Interventional Imaging, University of Texas Health Science Center at Houston (UTHealth), Houston, Texas, USA
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Hattingen E, Jurcoane A, Nelles M, Müller A, Nöth U, Mädler B, Mürtz P, Deichmann R, Schild HH. Quantitative MR Imaging of Brain Tissue and Brain Pathologies. Clin Neuroradiol 2015. [PMID: 26223371 DOI: 10.1007/s00062-015-0433-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Measurement of basic quantitative magnetic resonance (MR) parameters (e.g., relaxation times T1, T2*, T2 or respective rates R (1/T)) corrected for radiofrequency (RF) coil bias yields different conventional and new tissue contrasts as well as volumes for tissue segmentation. This approach also provides quantitative measures of microstructural and functional tissue changes. We herein demonstrate some prospects of quantitative MR imaging in neurological diagnostics and science.
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Affiliation(s)
- E Hattingen
- Neuroradiologie, Radiologische Klinik des Universitätsklinikums Bonn, Sigmund Freud Strasse 25, 53127, Bonn, Germany.
| | - A Jurcoane
- Neuroradiologie, Radiologische Klinik des Universitätsklinikums Bonn, Sigmund Freud Strasse 25, 53127, Bonn, Germany
| | - M Nelles
- Neuroradiologie, Radiologische Klinik des Universitätsklinikums Bonn, Sigmund Freud Strasse 25, 53127, Bonn, Germany
| | - A Müller
- Neuroradiologie, Radiologische Klinik des Universitätsklinikums Bonn, Sigmund Freud Strasse 25, 53127, Bonn, Germany
| | - U Nöth
- Brain Imaging Center, Universitätsklinikum Frankfurt, Frankfurt/Main, Germany
| | - B Mädler
- Philips Medical Systems, Philips GmbH, Hamburg, Germany
| | - P Mürtz
- Neuroradiologie, Radiologische Klinik des Universitätsklinikums Bonn, Sigmund Freud Strasse 25, 53127, Bonn, Germany
| | - R Deichmann
- Brain Imaging Center, Universitätsklinikum Frankfurt, Frankfurt/Main, Germany
| | - H H Schild
- Radiologische Klinik des Universitätsklinikums Bonn, Sigmund Freud Strasse 25, 53127, Bonn, Germany
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Multicontrast MRI Quantification of Focal Inflammation and Degeneration in Multiple Sclerosis. BIOMED RESEARCH INTERNATIONAL 2015; 2015:569123. [PMID: 26295042 PMCID: PMC4532805 DOI: 10.1155/2015/569123] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2014] [Revised: 11/07/2014] [Accepted: 11/07/2014] [Indexed: 11/18/2022]
Abstract
INTRODUCTION Local microstructural pathology in multiple sclerosis patients might influence their clinical performance. This study applied multicontrast MRI to quantify inflammation and neurodegeneration in MS lesions. We explored the impact of MRI-based lesion pathology in cognition and disability. METHODS 36 relapsing-remitting MS subjects and 18 healthy controls underwent neurological, cognitive, behavioural examinations and 3 T MRI including (i) fluid attenuated inversion recovery, double inversion recovery, and magnetization-prepared gradient echo for lesion count; (ii) T1, T2, and T2(*) relaxometry and magnetisation transfer imaging for lesion tissue characterization. Lesions were classified according to the extent of inflammation/neurodegeneration. A generalized linear model assessed the contribution of lesion groups to clinical performances. RESULTS Four lesion groups were identified and characterized by (1) absence of significant alterations, (2) prevalent inflammation, (3) concomitant inflammation and microdegeneration, and (4) prevalent tissue loss. Groups 1, 3, 4 correlated with general disability (Adj-R (2) = 0.6; P = 0.0005), executive function (Adj-R (2) = 0.5; P = 0.004), verbal memory (Adj-R (2) = 0.4; P = 0.02), and attention (Adj-R (2) = 0.5; P = 0.002). CONCLUSION Multicontrast MRI provides a new approach to infer in vivo histopathology of plaques. Our results support evidence that neurodegeneration is the major determinant of patients' disability and cognitive dysfunction.
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Cheng HLM, Stikov N, Ghugre NR, Wright GA. Practical medical applications of quantitative MR relaxometry. J Magn Reson Imaging 2013; 36:805-24. [PMID: 22987758 DOI: 10.1002/jmri.23718] [Citation(s) in RCA: 147] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Conventional MR images are qualitative, and their signal intensity is dependent on several complementary contrast mechanisms that are manipulated by the MR hardware and software. In the absence of a quantitative metric for absolute interpretation of pixel signal intensities, one that is independent of scanner hardware and sequences, it is difficult to perform comparisons of MR images across subjects or longitudinally in the same subject. Quantitative relaxometry isolates the contributions of individual MR contrast mechanisms (T1, T2, T2) and provides maps, which are independent of the MR protocol and have a physical interpretation often expressed in absolute units. In addition to providing an unbiased metric for comparing MR scans, quantitative relaxometry uses the relationship between MR maps and physiology to provide a noninvasive surrogate for biopsy and histology. This study provides an overview of some promising clinical applications of quantitative relaxometry, followed by a description of the methods and challenges of acquiring accurate and precise quantitative MR maps. It concludes with three case studies of quantitative relaxometry applied to studying multiple sclerosis, liver iron, and acute myocardial infarction.
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Affiliation(s)
- Hai-Ling Margaret Cheng
- Physiology and Experimental Medicine, Research Institute, The Hospital for Sick Children, Toronto, ON, Canada
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Tourdias T, Dousset V. Neuroinflammatory imaging biomarkers: relevance to multiple sclerosis and its therapy. Neurotherapeutics 2013; 10:111-23. [PMID: 23132327 PMCID: PMC3557362 DOI: 10.1007/s13311-012-0155-4] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Magnetic resonance imaging is an established tool in the management of multiple sclerosis (MS). Loss of blood brain barrier integrity assessed by gadolinium (Gd) enhancement is the current standard marker of MS activity. To explore the complex cascade of the inflammatory events, other magnetic resonance imaging, but also positron emission tomographic markers reviewed in this article are being developed to address active neuroinflammation with increased sensitivity and specificity. Alternative magnetic resonance contrast agents, positron emission tomographic tracers and imaging techniques could be more sensitive than Gd to early blood brain barrier alteration, and they could assess the inflammatory cell recruitment and/or the associated edema accumulation. These markers of active neuroinflammation, although some of them are limited to experimental studies, could find great relevance to complete Gd information and thereby increase our understanding of acute lesion pathophysiology and its noninvasive follow-up, especially to monitor treatment efficacy. Furthermore, such accurate markers of inflammation combined with those of neurodegeneration hold promise to provide a more complete picture of MS, which will be of great benefit for future therapeutic strategies.
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Affiliation(s)
- Thomas Tourdias
- INSERM Unit 1049 Neuroinflammation, Imagerie et Thérapie de la Sclérose en Plaques, Université de Bordeaux, 146 rue Léo Saignat, Bordeaux, F-33076, France.
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