1
|
Li Y, Zhuo Z, Liu C, Duan Y, Shi Y, Wang T, Li R, Wang Y, Jiang J, Xu J, Tian D, Zhang X, Shi F, Zhang X, Carass A, Barkhof F, Prince JL, Ye C, Liu Y. Deep learning enables accurate brain tissue microstructure analysis based on clinically feasible diffusion magnetic resonance imaging. Neuroimage 2024; 300:120858. [PMID: 39317273 DOI: 10.1016/j.neuroimage.2024.120858] [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: 08/05/2024] [Revised: 09/14/2024] [Accepted: 09/17/2024] [Indexed: 09/26/2024] Open
Abstract
Diffusion magnetic resonance imaging (dMRI) allows non-invasive assessment of brain tissue microstructure. Current model-based tissue microstructure reconstruction techniques require a large number of diffusion gradients, which is not clinically feasible due to imaging time constraints, and this has limited the use of tissue microstructure information in clinical settings. Recently, approaches based on deep learning (DL) have achieved promising tissue microstructure reconstruction results using clinically feasible dMRI. However, it remains unclear whether the subtle tissue changes associated with disease or age are properly preserved with DL approaches and whether DL reconstruction results can benefit clinical applications. Here, we provide the first evidence that DL approaches to tissue microstructure reconstruction yield reliable brain tissue microstructure analysis based on clinically feasible dMRI scans. Specifically, we reconstructed tissue microstructure from four different brain dMRI datasets with only 12 diffusion gradients, a clinically feasible protocol, and the neurite orientation dispersion and density imaging (NODDI) and spherical mean technique (SMT) models were considered. With these results we show that disease-related and age-dependent alterations of brain tissue were accurately identified. These findings demonstrate that DL tissue microstructure reconstruction can accurately quantify microstructural alterations in the brain based on clinically feasible dMRI.
Collapse
Affiliation(s)
- Yuxing Li
- School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, China
| | - Zhizheng Zhuo
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Chenghao Liu
- School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, China
| | - Yunyun Duan
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yulu Shi
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Tingting Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Runzhi Li
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Beijing, China
| | - Yanli Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Beijing, China
| | - Jiwei Jiang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Beijing, China
| | - Jun Xu
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Decai Tian
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Xinghu Zhang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Fudong Shi
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Beijing, China; Department of Neurology and Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, China
| | - Xiaofeng Zhang
- School of Information and Electronics, Beijing Institute of Technology, Zhuhai, China
| | - Aaron Carass
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, USA
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, Amsterdam, 1081 HV, the Netherlands
| | - Jerry L Prince
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, USA
| | - Chuyang Ye
- School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, China.
| | - Yaou Liu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
| |
Collapse
|
2
|
Sanabria-Diaz G, Cagol A, Lu PJ, Barakovic M, Ocampo-Pineda M, Chen X, Weigel M, Ruberte E, Siebenborn NDOS, Galbusera R, Schädelin S, Benkert P, Kuhle J, Kappos L, Melie-Garcia L, Granziera C. Advanced MRI Measures of Myelin and Axon Volume Identify Repair in Multiple Sclerosis. Ann Neurol 2024. [PMID: 39390658 DOI: 10.1002/ana.27102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Revised: 08/10/2024] [Accepted: 09/04/2024] [Indexed: 10/12/2024]
Abstract
OBJECTIVE Pathological studies suggest that multiple sclerosis (MS) lesions endure multiple waves of damage and repair; however, the dynamics and characteristics of these processes are poorly understood in patients living with MS. METHODS We studied 128 MS patients (75 relapsing-remitting, 53 progressive) and 72 healthy controls who underwent advanced magnetic resonance imaging and clinical examination at baseline and 2 years later. Magnetization transfer saturation and multi-shell diffusion imaging were used to quantify longitudinal changes in myelin and axon volumes within MS lesions. Lesions were grouped into 4 classes (repair, damage, mixed repair damage, and stable). The frequency of each class was correlated to clinical measures, demographic characteristics, and levels of serum neurofilament light chain (sNfL). RESULTS Stable lesions were the most frequent (n = 2,276; 44%), followed by lesions with patterns of "repair" (n = 1,352; 26.2%) and damage (n = 1,214; 23.5%). The frequency of "repair" lesion was negatively associated with disability (β = -0.04; p < 0.001) and sNfL (β = -0.16; p < 0.001) at follow-up. The frequency of the "damage" class was higher in progressive than relapsing-remitting patients (p < 0.05) and was related to disability (baseline: β = -0.078; follow-up: β = -0.076; p < 0.001) and age (baseline: β = -0.078; p < 0.001). Stable lesions were more frequent in relapsing-remitting than in progressive patients (p < 0.05), and in younger patients versus older (β = -0.07; p < 0.001) at baseline. Further, "mixed" lesions were most frequent in older patients (β = 0.004; p < 0.001) at baseline. INTERPRETATION These findings show that repair and damage processes within MS lesions occur across the entire disease spectrum and that their frequency correlates with patients disability, age, disease duration, and extent of neuroaxonal damage. ANN NEUROL 2024.
Collapse
Affiliation(s)
- Gretel Sanabria-Diaz
- Neurology Clinic and Policlinic, Department 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
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Alessandro Cagol
- Neurology Clinic and Policlinic, Department 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
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
- Department of Health Sciences, University of Genova, Genoa, Italy
| | - Po-Jui Lu
- Neurology Clinic and Policlinic, Department 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
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Muhamed Barakovic
- Neurology Clinic and Policlinic, Department 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
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Mario Ocampo-Pineda
- Neurology Clinic and Policlinic, Department 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
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Xinjie Chen
- Neurology Clinic and Policlinic, Department 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
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Matthias Weigel
- Neurology Clinic and Policlinic, Department 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
- 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
| | - Esther Ruberte
- Neurology Clinic and Policlinic, Department 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
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
- Medical Image Analysis Center (MIAC), Basel, Switzerland
| | - Nina de Oliveira S Siebenborn
- Neurology Clinic and Policlinic, Department 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
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
- Medical Image Analysis Center (MIAC), Basel, Switzerland
| | - Riccardo Galbusera
- Neurology Clinic and Policlinic, Department 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
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Sabine Schädelin
- Neurology Clinic and Policlinic, Department 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
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
- Department of Clinical Research, University Hospital and University of Basel, Basel, Switzerland
| | - Pascal Benkert
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
- Department of Clinical Research, University Hospital and University of Basel, Basel, Switzerland
| | - Jens Kuhle
- Neurology Clinic and Policlinic, Department of Medicine, Clinical Research and Biomedical Engineering, 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
| | - Ludwig Kappos
- Multiple Sclerosis Centre, Department of Neurology, Biomedicine and Clinical Research, University Hospital and University of Basel, Basel, Switzerland
- Department of Neurology, University Hospital and University of Basel, Basel, Switzerland
| | - Lester Melie-Garcia
- Neurology Clinic and Policlinic, Department 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
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Cristina Granziera
- Neurology Clinic and Policlinic, Department 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
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| |
Collapse
|
3
|
Zhang HQ, Lee JCY, Wang L, Cao P, Chan KH, Mak HKF. Dynamic Changes in Long-Standing Multiple Sclerosis Revealed by Longitudinal Structural Network Analysis Using Diffusion Tensor Imaging. AJNR Am J Neuroradiol 2024; 45:305-311. [PMID: 38302198 DOI: 10.3174/ajnr.a8115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 11/27/2023] [Indexed: 02/03/2024]
Abstract
BACKGROUND AND PURPOSE DTI can be used to derive conventional diffusion measurements, which can measure WM abnormalities in multiple sclerosis. DTI can also be used to construct structural brain networks and derive network measurements. However, few studies have compared their sensitivity in detecting brain alterations, especially in longitudinal studies. Therefore, in this study, we aimed to determine which type of measurement is more sensitive in tracking the dynamic changes over time in MS. MATERIALS AND METHODS Eighteen patients with MS were recruited at baseline and followed up at 6 and 12 months. All patients underwent MR imaging and clinical evaluation at 3 time points. Diffusion and network measurements were derived, and their brain changes were evaluated. RESULTS None of the conventional DTI measurements displayed statistically significant changes during the follow-up period; however, the nodal degree, nodal efficiency, and nodal path length of the left middle frontal gyrus and bilateral inferior frontal gyrus, opercular part showed significant longitudinal changes between baseline and at 12 months, respectively. CONCLUSIONS The nodal degree, nodal efficiency, and nodal path length of the left middle frontal gyrus and bilateral inferior frontal gyrus, opercular part may be used to monitor brain changes over time in MS.
Collapse
Affiliation(s)
- Hui-Qin Zhang
- From the Department of Diagnostic Radiology (H.-Q.Z.), National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Department of Diagnostic Radiology (H.-Q.Z., P.C., H.K.-F.M.), Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong SAR, China
| | - Jacky Chi-Yan Lee
- Department of Medicine (J.C.-Y.L., K.-H.C.), Queen Mary Hospital, Hong Kong SAR, China
| | - Lu Wang
- Department of Health Technology and Informatics (L.W.), Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Peng Cao
- Department of Diagnostic Radiology (H.-Q.Z., P.C., H.K.-F.M.), Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong SAR, China
| | - Koon-Ho Chan
- Department of Medicine (J.C.-Y.L., K.-H.C.), Queen Mary Hospital, Hong Kong SAR, China
- Alzheimer's Disease Research Network (H.K.-F.M., K.-H.C.), University of Hong Kong, Hong Kong SAR, China
| | - Henry Ka-Fung Mak
- Department of Diagnostic Radiology (H.-Q.Z., P.C., H.K.-F.M.), Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong SAR, China
- Alzheimer's Disease Research Network (H.K.-F.M., K.-H.C.), University of Hong Kong, Hong Kong SAR, China
- State Key Laboratory of Brain and Cognitive Sciences (H.K.-F.M.), University of Hong Kong, Hong Kong SAR, China
| |
Collapse
|
4
|
Minosse S, Picchi E, Conti A, di Giuliano F, di Ciò F, Sarmati L, Teti E, de Santis S, Andreoni M, Floris R, Guerrisi M, Garaci F, Toschi N. Multishell diffusion MRI reveals whole-brain white matter changes in HIV. Hum Brain Mapp 2023; 44:5113-5124. [PMID: 37647214 PMCID: PMC10502617 DOI: 10.1002/hbm.26448] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2022] [Revised: 07/15/2023] [Accepted: 07/26/2023] [Indexed: 09/01/2023] Open
Abstract
Diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) have been previously used to explore white matter related to human immunodeficiency virus (HIV) infection. While DTI and DKI suffer from low specificity, the Combined Hindered and Restricted Model of Diffusion (CHARMED) provides additional microstructural specificity. We used these three models to evaluate microstructural differences between 35 HIV-positive patients without neurological impairment and 20 healthy controls who underwent diffusion-weighted imaging using three b-values. While significant group effects were found in all diffusion metrics, CHARMED and DKI analyses uncovered wider involvement (80% vs. 20%) of all white matter tracts in HIV infection compared with DTI. In restricted fraction (FR) analysis, we found significant differences in the left corticospinal tract, middle cerebellar peduncle, right inferior cerebellar peduncle, right corticospinal tract, splenium of the corpus callosum, left superior cerebellar peduncle, left superior cerebellar peduncle, pontine crossing tract, left posterior limb of the internal capsule, and left/right medial lemniscus. These are involved in language, motor, equilibrium, behavior, and proprioception, supporting the functional integration that is frequently impaired in HIV-positivity. Additionally, we employed a machine learning algorithm (XGBoost) to discriminate HIV-positive patients from healthy controls using DTI and CHARMED metrics on an ROIwise basis, and unique contributions to this discrimination were examined using Shapley Explanation values. The CHARMED and DKI estimates produced the best performance. Our results suggest that biophysical multishell imaging, combining additional sensitivity and built-in specificity, provides further information about the brain microstructural changes in multimodal areas involved in attentive, emotional and memory networks often impaired in HIV patients.
Collapse
Affiliation(s)
- Silvia Minosse
- Diagnostic Imaging UnitUniversity Hospital Rome Tor VergataRomeItaly
| | - Eliseo Picchi
- Diagnostic Imaging UnitUniversity Hospital Rome Tor VergataRomeItaly
- Department of Biomedicine and PreventionUniversity of Rome Tor VergataRomeItaly
| | - Allegra Conti
- Department of Biomedicine and PreventionUniversity of Rome Tor VergataRomeItaly
| | - Francesca di Giuliano
- Department of Biomedicine and PreventionUniversity of Rome Tor VergataRomeItaly
- Neuroradiology UnitUniversity Hospital of Rome Tor VergataRomeItaly
| | - Francesco di Ciò
- Department of Biomedicine and PreventionUniversity of Rome Tor VergataRomeItaly
| | - Loredana Sarmati
- Clinical Infectious Diseases UnitUniversity Hospital of Rome Tor VergataRomeItaly
- Department of Systems MedicineUniversity of Rome Tor VergataRomeItaly
| | - Elisabetta Teti
- Clinical Infectious Diseases UnitUniversity Hospital of Rome Tor VergataRomeItaly
| | - Silvia de Santis
- Instituto de NeurocienciasConsejo Superior de Investigaciones Científicas and Universidad Miguel HernándezSant Joan d'AlacantSpain
| | - Massimo Andreoni
- Clinical Infectious Diseases UnitUniversity Hospital of Rome Tor VergataRomeItaly
- Department of Systems MedicineUniversity of Rome Tor VergataRomeItaly
| | - Roberto Floris
- Diagnostic Imaging UnitUniversity Hospital Rome Tor VergataRomeItaly
- Department of Biomedicine and PreventionUniversity of Rome Tor VergataRomeItaly
| | - Maria Guerrisi
- Department of Biomedicine and PreventionUniversity of Rome Tor VergataRomeItaly
| | - Francesco Garaci
- Department of Biomedicine and PreventionUniversity of Rome Tor VergataRomeItaly
- Neuroradiology UnitUniversity Hospital of Rome Tor VergataRomeItaly
- IRCSS San Raffaele CassinoFrosinoneItaly
| | - Nicola Toschi
- Neuroradiology UnitUniversity Hospital of Rome Tor VergataRomeItaly
- Athinoula A. Martinos Center for Biomedical ImagingHarvard Medical SchoolBostonMassachusettsUSA
| |
Collapse
|
5
|
Ouellette R. Advanced MRI quantification of neuroinflammatory disorders. J Neurosci Res 2022; 100:1389-1394. [PMID: 35460291 PMCID: PMC9321072 DOI: 10.1002/jnr.25054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 03/26/2022] [Accepted: 03/31/2022] [Indexed: 11/11/2022]
Affiliation(s)
- Russell Ouellette
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.,Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden
| |
Collapse
|
6
|
Hori M, Maekawa T, Kamiya K, Hagiwara A, Goto M, Takemura MY, Fujita S, Andica C, Kamagata K, Cohen-Adad J, Aoki S. Advanced Diffusion MR Imaging for Multiple Sclerosis in the Brain and Spinal Cord. Magn Reson Med Sci 2022; 21:58-70. [PMID: 35173096 PMCID: PMC9199983 DOI: 10.2463/mrms.rev.2021-0091] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Diffusion tensor imaging (DTI) has been established its usefulness in evaluating normal-appearing white matter (NAWM) and other lesions that are difficult to evaluate with routine clinical MRI in the evaluation of the brain and spinal cord lesions in multiple sclerosis (MS), a demyelinating disease. With the recent advances in the software and hardware of MRI systems, increasingly complex and sophisticated MRI and analysis methods, such as q-space imaging, diffusional kurtosis imaging, neurite orientation dispersion and density imaging, white matter tract integrity, and multiple diffusion encoding, referred to as advanced diffusion MRI, have been proposed. These are capable of capturing in vivo microstructural changes in the brain and spinal cord in normal and pathological states in greater detail than DTI. This paper reviews the current status of recent advanced diffusion MRI for assessing MS in vivo as part of an issue celebrating two decades of magnetic resonance in medical sciences (MRMS), an official journal of the Japanese Society of Magnetic Resonance in Medicine.
Collapse
Affiliation(s)
- Masaaki Hori
- Department of Radiology, Toho University Omori Medical Center.,Department of Radiology, Juntendo University School of Medicine
| | - Tomoko Maekawa
- Department of Radiology, Juntendo University School of Medicine
| | - Kouhei Kamiya
- Department of Radiology, Toho University Omori Medical Center.,Department of Radiology, Juntendo University School of Medicine
| | | | - Masami Goto
- Department of Radiological Technology, Faculty of Health Science, Juntendo University
| | | | - Shohei Fujita
- Department of Radiology, Juntendo University School of Medicine
| | | | - Koji Kamagata
- Department of Radiology, Juntendo University School of Medicine
| | | | - Shigeki Aoki
- Department of Radiology, Juntendo University School of Medicine
| |
Collapse
|
7
|
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.
Collapse
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
| |
Collapse
|
8
|
Sarmadi M, Bidel Z, Najafi F, Ramakrishnan R, Teymoori F, Zarmehri HA, Nazarzadeh M. Copper concentration in multiple sclerosis: a systematic review and meta-analysis. Mult Scler Relat Disord 2020; 45:102426. [PMID: 32799121 DOI: 10.1016/j.msard.2020.102426] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 07/26/2020] [Accepted: 07/27/2020] [Indexed: 10/23/2022]
Abstract
BACKGROUND A wide range of risk factors, from genetic to environmental, have been identified to play role in the etiology of multiple sclerosis. However, the role of trace element remains mostly unknown. We sought to combine all available evidence to assess the association between copper concentration and multiple sclerosis. METHODS This systematic review and meta-analysis was conducted based on PRISMA guidelines. PubMed, Scopus, Embase, and Web of Science were searched since inception till July 2020. Observational studies that assessed copper as exposure in serum, plasma, whole blood, and cerebrospinal fluid were included. Standardized mean differences (SMD), comparing the mean of copper concentration in multiple sclerosis patients versus healthy controls, were considered as the measure of association. The fixed-effect model with inverse variance weighting was used to combine the findings. RESULTS Twenty studies inclusive of 797 multiple sclerosis cases and 875 healthy controls were included in the meta-analysis (all case-control studies). The combined SMDs were 1.25 (95% confidence interval [CI] 0.95 to 1.55, number of included studies [n]=4) in plasma, 0.45 (CI 0.22 to 0.68, n=4) in whole blood, 0.19 (CI 0.06 to 0.33, n=12) in blood serum and 1.23 (CI 0.83 to 1.64, n=4) in cerebrospinal fluid. CONCLUSIONS We found a higher concentration of copper in multiple sclerosis patients than healthy controls. The possible causal nature of the observed associations warrants further investigation with prospective data.
Collapse
Affiliation(s)
- Mohammad Sarmadi
- Department of Environmental Health Engineering, School of Health, Torbat Heydariyeh University of Medical Sciences, Torbat Heydariyeh, Iran; Neuroscience Research Center, Torbat Heydariyeh University of Medical Sciences, Torbat Heydariyeh, Iran; Health Sciences Research Center, Torbat Heydariyeh University of Medical Sciences, Torbat Heydariyeh, Iran
| | - Zeinab Bidel
- Deep Medicine, Oxford Martin School, University of Oxford, UK; Nuffield Department of Women's and Reproductive Health, Medical Science Division, University of Oxford, UK
| | - Fereshteh Najafi
- Neuroscience Research Center, Torbat Heydariyeh University of Medical Sciences, Torbat Heydariyeh, Iran; Department of Public Health, School of Health, Torbat Heydariyeh University of Medical Sciences, Torbat Heydariyeh, Iran
| | - Rema Ramakrishnan
- Deep Medicine, Oxford Martin School, University of Oxford, UK; Nuffield Department of Women's and Reproductive Health, Medical Science Division, University of Oxford, UK
| | - Farshad Teymoori
- Nutrition and Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Department of Nutrition, School of Public Health, Iran University of Medical Sciences, Tehran, Iran
| | - Hassan Azhdari Zarmehri
- Neuroscience Research Center, Torbat Heydariyeh University of Medical Sciences, Torbat Heydariyeh, Iran; Department of Physiology, School of Paramedical Sciences, Torbat Heydariyeh University of Medical Sciences, Torbat Heydariyeh, Iran
| | - Milad Nazarzadeh
- Deep Medicine, Oxford Martin School, University of Oxford, UK; Nuffield Department of Women's and Reproductive Health, Medical Science Division, University of Oxford, UK; Department of Public Health, School of Health, Torbat Heydariyeh University of Medical Sciences, Torbat Heydariyeh, Iran.
| |
Collapse
|
9
|
De Santis S, Herranz E, Treaba CA, Barletta V, Mehndiratta A, Mainero C, Toschi N. Whole brain in vivo axonal diameter mapping in multiple sclerosis. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:204-207. [PMID: 31945878 DOI: 10.1109/embc.2019.8856433] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Traditional techniques based on diffusion MR imaging suffer from extremely low specificity in separating disease-related alterations in white matter microstructure, which can involve multiple phenomena including axonal loss, demyelination and changes in axonal size. Multi-shell diffusion MRI is able to greatly increase specificity by concomitantly exploring multiple diffusion timescales. If multi-shell acquisition is combined with an exploration of different diffusion times, diffusion data allows the estimation of sophisticated compartmental models, which provide greatly enhanced specificity to the presence of different tissue sub-compartments, as well as estimates of intra-voxel axonal diameter distributions. In this paper, we apply a multiple-b-value, high angular resolution multi-shell diffusion MRI protocol with varying diffusion times to a cohort of multiple sclerosis (MS) patients and compare them to a population of healthy controls. By fitting the AxCaliber model, we are able to extract indices for axonal diameter across the whole brain. We show that MS is associated with widespread increases of axonal diameter and that our axonal diameter estimation provides the highest discrimination power for local alterations in normal-appearing white matter in MS compared to controls. AxCaliber has the potential to disentangle microstructural alterations in MS and holds great promises to become a sensitive and specific non-invasive biomarker of irreversible disease progression.
Collapse
|
10
|
Toschi N, Gisbert RA, Passamonti L, Canals S, De Santis S. Multishell diffusion imaging reveals sex-specific trajectories of early white matter degeneration in normal aging. Neurobiol Aging 2019; 86:191-200. [PMID: 31902522 DOI: 10.1016/j.neurobiolaging.2019.11.014] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Revised: 10/08/2019] [Accepted: 11/21/2019] [Indexed: 02/08/2023]
Abstract
During aging, human white matter (WM) is subject to dynamic structural changes which have a deep impact on healthy and pathological evolution of the brain through the lifespan; characterizing this pattern is of key importance for understanding brain development, maturation, and aging as well as for studying its pathological alterations. Diffusion magnetic resonance imaging (MRI) can provide a quantitative assessment of the white-matter microstructural organization that characterizes these trajectories. Here, we use both conventional and advanced diffusion MRI in a cohort of 91 individuals (age range: 13-62 years) to study region- and sex-specific features of WM microstructural integrity in healthy aging. We focus on the age at which microstructural imaging parameters invert their development trend as the time point which marks the onset of microstructural decline in WM. Importantly, our results indicate that age-related brain changes begin earlier in males than females and affect more frontal regions-in accordance with evolutionary theories and numerous evidences across non-MRI domains. Advanced diffusion MRI reveals age-related WM modification patterns which cannot be detected using conventional diffusion tensor imaging.
Collapse
Affiliation(s)
- Nicola Toschi
- Athinoula A. Martinos Center for Biomedical Imaging and Harvard Medical School, Boston, MA, USA; Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy
| | | | - Luca Passamonti
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK; Istituto di Bioimmagini e Fisiologia Molecolare (IBFM), Consiglio Nazionale delle Ricerche (CNR), Segrate, Milano, Italia
| | - Santiago Canals
- Instituto de Neurociencias de Alicante (CSIC-UMH), San Juan de Alicante, Spain
| | - Silvia De Santis
- Instituto de Neurociencias de Alicante (CSIC-UMH), San Juan de Alicante, Spain; Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK.
| |
Collapse
|
11
|
Affiliation(s)
- Silvia De Santis
- Instituto de Neurociencias, Consejo Superior de Investigaciones Científicas-Universidad Miguel Hernández, 03550 San Juan de Alicante, Spain
| | - Santiago Canals
- Instituto de Neurociencias, Consejo Superior de Investigaciones Científicas-Universidad Miguel Hernández, 03550 San Juan de Alicante, Spain.
| |
Collapse
|