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Tourell M, Jin J, Bachrata B, Stewart A, Ropele S, Enzinger C, Bollmann S, Bollmann S, Robinson SD, O'Brien K, Barth M. Three-dimensional EPI with shot-selective CAIPIRIHANA for rapid high-resolution quantitative susceptibility mapping at 3 T. Magn Reson Med 2024; 92:997-1010. [PMID: 38778631 DOI: 10.1002/mrm.30101] [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: 10/03/2023] [Revised: 03/14/2024] [Accepted: 03/16/2024] [Indexed: 05/25/2024]
Abstract
PURPOSE QSM provides insight into healthy brain aging and neuropathologies such as multiple sclerosis (MS), traumatic brain injuries, brain tumors, and neurodegenerative diseases. Phase data for QSM are usually acquired from 3D gradient-echo (3D GRE) scans with long acquisition times that are detrimental to patient comfort and susceptible to patient motion. This is particularly true for scans requiring whole-brain coverage and submillimeter resolutions. In this work, we use a multishot 3D echo plannar imaging (3D EPI) sequence with shot-selective 2D CAIPIRIHANA to acquire high-resolution, whole-brain data for QSM with minimal distortion and blurring. METHODS To test clinical viability, the 3D EPI sequence was used to image a cohort of MS patients at 1-mm isotropic resolution at 3 T. Additionally, 3D EPI data of healthy subjects were acquired at 1-mm, 0.78-mm, and 0.65-mm isotropic resolution with varying echo train lengths (ETLs) and compared with a reference 3D GRE acquisition. RESULTS The appearance of the susceptibility maps and the susceptibility values for segmented regions of interest were comparable between 3D EPI and 3D GRE acquisitions for both healthy and MS participants. Additionally, all lesions visible in the MS patients on the 3D GRE susceptibility maps were also visible on the 3D EPI susceptibility maps. The interplay among acquisition time, resolution, echo train length, and the effect of distortion on the calculated susceptibility maps was investigated. CONCLUSION We demonstrate that the 3D EPI sequence is capable of rapidly acquiring submillimeter resolutions and providing high-quality, clinically relevant susceptibility maps.
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Affiliation(s)
- Monique Tourell
- Centre for Advanced Imaging, University of Queensland, Brisbane, Queensland, Australia
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Queensland, Australia
| | - Jin Jin
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Queensland, Australia
- Siemens Healthineers Pty Ltd, Bowen Hills, Queensland, Australia
| | - Beata Bachrata
- High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
- Karl Landsteiner Institute for Clinical Molecular MR in Musculoskeletal Imaging, Vienna, Austria
- Department of Medical Engineering, Carinthia University of Applied Sciences, Klagenfurt, Austria
| | - Ashley Stewart
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Queensland, Australia
- School of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, Australia
| | - Stefan Ropele
- Department of Neurology, Medical University of Graz, Graz, Austria
| | | | - Saskia Bollmann
- School of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, Australia
| | - Steffen Bollmann
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Queensland, Australia
- School of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, Australia
| | - Simon Daniel Robinson
- Centre for Advanced Imaging, University of Queensland, Brisbane, Queensland, Australia
- High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
- Department of Neurology, Medical University of Graz, Graz, Austria
- Christian Doppler Laboratory for MR Imaging Biomarkers, Department for Biomedical Imaging and Image-Guided Therapy, University of Vienna, Vienna, Austria
| | - Kieran O'Brien
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Queensland, Australia
- Siemens Healthineers Pty Ltd, Bowen Hills, Queensland, Australia
| | - Markus Barth
- Centre for Advanced Imaging, University of Queensland, Brisbane, Queensland, Australia
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Queensland, Australia
- School of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, Australia
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Ji S, Jang J, Kim M, Lee H, Kim W, Lee J, Shin HG. Comparison between R2'-based and R2*-based χ-separation methods: A clinical evaluation in individuals with multiple sclerosis. NMR IN BIOMEDICINE 2024; 37:e5167. [PMID: 38697612 DOI: 10.1002/nbm.5167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Revised: 03/28/2024] [Accepted: 04/02/2024] [Indexed: 05/05/2024]
Abstract
Susceptibility source separation, or χ-separation, estimates diamagnetic (χdia) and paramagnetic susceptibility (χpara) signals in the brain using local field and R2' (= R2* - R2) maps. Recently proposed R2*-based χ-separation methods allow for χ-separation using only multi-echo gradient echo (ME-GRE) data, eliminating the need for additional data acquisition for R2 mapping. Although this approach reduces scan time and enhances clinical utility, the impact of missing R2 information remains a subject of exploration. In this study, we evaluate the viability of two previously proposed R2*-based χ-separation methods as alternatives to their R2'-based counterparts: model-based R2*-χ-separation versus χ-separation and deep learning-based χ-sepnet-R2* versus χ-sepnet-R2'. Their performances are assessed in individuals with multiple sclerosis (MS), comparing them with their corresponding R2'-based counterparts (i.e., R2*-χ-separation vs. χ-separation and χ-sepnet-R2* vs. χ-sepnet-R2'). The evaluations encompass qualitative visual assessments by experienced neuroradiologists and quantitative analyses, including region of interest analyses and linear regression analyses. Qualitatively, R2*-χ-separation tends to report higher χpara and χdia values compared with χ-separation, leading to less distinct lesion contrasts, while χ-sepnet-R2* closely aligns with χ-sepnet-R2'. Quantitative analysis reveals a robust correlation between both R2*-based methods and their R2'-based counterparts (r ≥ 0.88). Specifically, in the whole-brain voxels, χ-sepnet-R2* exhibits higher correlation and better linearity than R2*-χ-separation (χdia/χpara from R2*-χ-separation: r = 0.88/0.90, slope = 0.79/0.86; χdia/χpara from χ-sepnet-R2*: r = 0.90/0.92, slope = 0.99/0.97). In MS lesions, both R2*-based methods display comparable correlation and linearity (χdia/χpara from R2*-χ-separation: r = 0.90/0.91, slope = 0.98/0.91; χdia/χpara from χ-sepnet-R2*: r = 0.88/0.88, slope = 0.91/0.95). Notably, χ-sepnet-R2* demonstrates negligible offsets, whereas R2*-χ-separation exhibits relatively large offsets (0.02 ppm in the whole brain and 0.01 ppm in the MS lesions), potentially indicating the false presence of myelin or iron in MS lesions. Overall, both R2*-based χ-separation methods demonstrated their viability as alternatives to their R2'-based counterparts. χ-sepnet-R2* showed better alignment with its R2'-based counterpart with minimal susceptibility offsets, compared with R2*-χ-separation that reported higher χpara and χdia values compared with R2'-based χ-separation.
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Affiliation(s)
- Sooyeon Ji
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, South Korea
| | - Jinhee Jang
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Minjun Kim
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, South Korea
| | - Hyebin Lee
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Woojun Kim
- Department of Neurology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Jongho Lee
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, South Korea
| | - Hyeong-Geol Shin
- Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
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Xie Y, Zhang S, Wu D, Yao Y, Cho J, Lu J, Zhu H, Wang Y, Zhang Y, Zhu W. The changes of oxygen extraction fraction in different types of lesions in relapsing-remitting multiple sclerosis: A cross-sectional and longitudinal study. Neurol Sci 2024; 45:3939-3949. [PMID: 38492126 DOI: 10.1007/s10072-024-07463-2] [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: 12/17/2023] [Accepted: 03/11/2024] [Indexed: 03/18/2024]
Abstract
OBJECTIVES To explore the oxygen metabolism level of different types of lesions in relapsing-remitting multiple sclerosis (RRMS) patients by oxygen extraction fraction (OEF) both cross-sectionally and longitudinally. METHODS Forty-six RRMS patients and forty-one healthy controls (HC) went MRI examination. The quantitative susceptibility mapping (QSM) and OEF map were reconstructed from a 3D multi-echo gradient echo sequence. MS lesions in white matter were classified as contrast-enhancing lesions (CELs) on post-gadolinium T1-weighted sequence, paramagnetic rim lesions (PRLs), hyperintense lesions and non-hyperintense lesions on QSM, respectively. The susceptibility and OEF of different types of lesions were compared. The susceptibility and OEF values were measured and compared among different types of lesions. Among these RRMS patients, seventeen had follow-up MRI and 232 lesions, and baseline to follow-up longitudinal changes in susceptibility and OEF were measured. RESULTS PRLs had higher susceptibility and lower OEF than CELs, hyperintense lesions, and non-hyperintense lesions. The hyperintense lesions had higher susceptibility and lower OEF than non-hyperintense lesions. In longitudinal changes, PRLs had susceptibility increased (P < 0.001) and OEF decreased (P < 0.001). The hyperintense lesions showed significant decreases in susceptibility (P = 0.020), and non-hyperintense lesions showed significant increases in OEF during follow-up (P = 0.005). Notably, hyperintense lesions may convert to PRLs or non-hyperintense lesions as time progresses, accompanied by changes of OEF and susceptibility in the lesions. CONCLUSION This study revealed tissue damage and oxygen metabolism level in different types of MS lesions. The OEF may contribute to further understanding the evolution of MS lesions.
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Affiliation(s)
- Yan Xie
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, People's Republic of China
| | - Shun Zhang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, People's Republic of China
| | - Di Wu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, People's Republic of China
| | - Yihao Yao
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, People's Republic of China
| | - Junghun Cho
- Department of Biomedical Engineering, University at Buffalo, The State University of New York, Buffalo, NY, 14260, USA
| | - Jun Lu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, People's Republic of China
| | - Hongquan Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, People's Republic of China
| | - Yi Wang
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
- Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA
| | - Yan Zhang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, People's Republic of China.
| | - Wenzhen Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, People's Republic of China.
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Rimkus CDM, Otsuka FS, Nunes DM, Chaim KT, Otaduy MCG. Central Vein Sign and Paramagnetic Rim Lesions: Susceptibility Changes in Brain Tissues and Their Implications for the Study of Multiple Sclerosis Pathology. Diagnostics (Basel) 2024; 14:1362. [PMID: 39001252 PMCID: PMC11240827 DOI: 10.3390/diagnostics14131362] [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: 05/03/2024] [Revised: 05/29/2024] [Accepted: 06/03/2024] [Indexed: 07/16/2024] Open
Abstract
Multiple sclerosis (MS) is the most common acquired inflammatory and demyelinating disease in adults. The conventional diagnostic of MS and the follow-up of inflammatory activity is based on the detection of hyperintense foci in T2 and fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) and lesions with brain-blood barrier (BBB) disruption in the central nervous system (CNS) parenchyma. However, T2/FLAIR hyperintense lesions are not specific to MS and the MS pathology and inflammatory processes go far beyond focal lesions and can be independent of BBB disruption. MRI techniques based on the magnetic susceptibility properties of the tissue, such as T2*, susceptibility-weighted images (SWI), and quantitative susceptibility mapping (QSM) offer tools for advanced MS diagnostic, follow-up, and the assessment of more detailed features of MS dynamic pathology. Susceptibility-weighted techniques are sensitive to the paramagnetic components of biological tissues, such as deoxyhemoglobin. This capability enables the visualization of brain parenchymal veins. Consequently, it presents an opportunity to identify veins within the core of multiple sclerosis (MS) lesions, thereby affirming their venocentric characteristics. This advancement significantly enhances the accuracy of the differential diagnostic process. Another important paramagnetic component in biological tissues is iron. In MS, the dynamic trafficking of iron between different cells, such as oligodendrocytes, astrocytes, and microglia, enables the study of different stages of demyelination and remyelination. Furthermore, the accumulation of iron in activated microglia serves as an indicator of latent inflammatory activity in chronic MS lesions, termed paramagnetic rim lesions (PRLs). PRLs have been correlated with disease progression and degenerative processes, underscoring their significance in MS pathology. This review will elucidate the underlying physical principles of magnetic susceptibility and their implications for the formation and interpretation of T2*, SWI, and QSM sequences. Additionally, it will explore their applications in multiple sclerosis (MS), particularly in detecting the central vein sign (CVS) and PRLs, and assessing iron metabolism. Furthermore, the review will discuss their role in advancing early and precise MS diagnosis and prognostic evaluation, as well as their utility in studying chronic active inflammation and degenerative processes.
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Affiliation(s)
- Carolina de Medeiros Rimkus
- Department of Radiology and Oncology, Hospital das Clínicas da Faculdade de Medicina da Universidade de Sao Paulo (HCFMUSP), Sao Paulo 05403-010, SP, Brazil
- Laboratory of Medical Investigation in Magnetic Resonance-44 (LIM 44), University of Sao Paulo, Sao Paulo 05403-000, SP, Brazil
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam UMC, Location VUmc, 1081 HV Amsterdam, The Netherlands
- Instituto D'Or de Ensino e Pesquisa (IDOR), Sao Paulo 01401-002, SP, Brazil
| | - Fábio Seiji Otsuka
- Laboratory of Medical Investigation in Magnetic Resonance-44 (LIM 44), University of Sao Paulo, Sao Paulo 05403-000, SP, Brazil
| | - Douglas Mendes Nunes
- Department of Radiology and Oncology, Hospital das Clínicas da Faculdade de Medicina da Universidade de Sao Paulo (HCFMUSP), Sao Paulo 05403-010, SP, Brazil
- Grupo Fleury, Sao Paulo 04701-200, SP, Brazil
| | - Khallil Taverna Chaim
- Laboratory of Medical Investigation in Magnetic Resonance-44 (LIM 44), University of Sao Paulo, Sao Paulo 05403-000, SP, Brazil
| | - Maria Concepción Garcia Otaduy
- Department of Radiology and Oncology, Hospital das Clínicas da Faculdade de Medicina da Universidade de Sao Paulo (HCFMUSP), Sao Paulo 05403-010, SP, Brazil
- Laboratory of Medical Investigation in Magnetic Resonance-44 (LIM 44), University of Sao Paulo, Sao Paulo 05403-000, SP, Brazil
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Voon CC, Wiltgen T, Wiestler B, Schlaeger S, Mühlau M. Quantitative susceptibility mapping in multiple sclerosis: A systematic review and meta-analysis. Neuroimage Clin 2024; 42:103598. [PMID: 38582068 PMCID: PMC11002889 DOI: 10.1016/j.nicl.2024.103598] [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: 12/21/2023] [Revised: 03/07/2024] [Accepted: 03/24/2024] [Indexed: 04/08/2024]
Abstract
BACKGROUND Quantitative susceptibility mapping (QSM) is a quantitative measure based on magnetic resonance imaging sensitive to iron and myelin content. This makes QSM a promising non-invasive tool for multiple sclerosis (MS) in research and clinical practice. OBJECTIVE We performed a systematic review and meta-analysis on the use of QSM in MS. METHODS Our review was prospectively registered on PROSPERO (CRD42022309563). We searched five databases for studies published between inception and 30th April 2023. We identified 83 English peer-reviewed studies that applied QSM images on MS cohorts. Fifty-five included studies had at least one of the following outcome measures: deep grey matter QSM values in MS, either compared to healthy controls (HC) (k = 13) or correlated with the score on the Expanded Disability Status Scale (EDSS) (k = 7), QSM lesion characteristics (k = 22) and their clinical correlates (k = 17), longitudinal correlates (k = 11), histological correlates (k = 7), or correlates with other imaging techniques (k = 12). Two meta-analyses on deep grey matter (DGM) susceptibility data were performed, while the remaining findings could only be analyzed descriptively. RESULTS After outlier removal, meta-analyses demonstrated a significant increase in the basal ganglia susceptibility (QSM values) in MS compared to HC, caudate (k = 9, standardized mean difference (SDM) = 0.54, 95 % CI = 0.39-0.70, I2 = 46 %), putamen (k = 9, SDM = 0.38, 95 % CI = 0.19-0.57, I2 = 59 %), and globus pallidus (k = 9, SDM = 0.48, 95 % CI = 0.28-0.67, I2 = 60 %), whereas thalamic QSM values exhibited a significant reduction (k = 12, SDM = -0.39, 95 % CI = -0.66--0.12, I2 = 84 %); these susceptibility differences in MS were independent of age. Further, putamen QSM values positively correlated with EDSS (k = 4, r = 0.36, 95 % CI = 0.16-0.53, I2 = 0 %). Regarding rim lesions, four out of seven studies, representing 73 % of all patients, reported rim lesions to be associated with more severe disability. Moreover, lesion development from initial detection to the inactive stage is paralleled by increasing, plateauing (after about two years), and gradually decreasing QSM values, respectively. Only one longitudinal study provided clinical outcome measures and found no association. Histological data suggest iron content to be the primary source of QSM values in DGM and at the edges of rim lesions; further, when also considering data from myelin water imaging, the decrease of myelin is likely to drive the increase of QSM values within WM lesions. CONCLUSIONS We could provide meta-analytic evidence for DGM susceptibility changes in MS compared to HC; basal ganglia susceptibility is increased and, in the putamen, associated with disability, while thalamic susceptibility is decreased. Beyond these findings, further investigations are necessary to establish the role of QSM in MS for research or even clinical routine.
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Affiliation(s)
- Cui Ci Voon
- Dept. of Neurology, School of Medicine and Health, Technical University of Munich, Munich, Germany; TUM-Neuroimaging Center, School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Tun Wiltgen
- Dept. of Neurology, School of Medicine and Health, Technical University of Munich, Munich, Germany; TUM-Neuroimaging Center, School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Benedikt Wiestler
- Dept. of Neuroradiology, School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Sarah Schlaeger
- Dept. of Neuroradiology, School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Mark Mühlau
- Dept. of Neurology, School of Medicine and Health, Technical University of Munich, Munich, Germany; TUM-Neuroimaging Center, School of Medicine and Health, Technical University of Munich, Munich, Germany.
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Choi S, Lake S, Harrison DM. Evaluation of the Blood-Brain Barrier, Demyelination, and Neurodegeneration in Paramagnetic Rim Lesions in Multiple Sclerosis on 7 Tesla MRI. J Magn Reson Imaging 2024; 59:941-951. [PMID: 37276054 PMCID: PMC10754232 DOI: 10.1002/jmri.28847] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 05/22/2023] [Accepted: 05/23/2023] [Indexed: 06/07/2023] Open
Abstract
BACKGROUND Paramagnetic rim lesions (PRLs) are associated with chronic inflammation in multiple sclerosis (MS). 7-Tesla (7T) magnetic resonance imaging (MRI) can evaluate the integrity of the blood-brain barrier (BBB) in addition to the tissue myelination status and cell loss. PURPOSE To use MRI metrics to investigate underlying physiology and clinical importance of PRLs. STUDY TYPE Prospective. SUBJECTS Thirty-six participants (mean-age 47, 23 females, 13 males) of mixed MS subtypes. FIELD STRENGTH/SEQUENCE 7T, MP2RAGE, MULTI-ECHO 3D-GRE, FLAIR. ASSESSMENT Lesion heterogeneity; longitudinal changes in lesion counts; comparison of T1, R2*, and χ; association between baseline lesion types and disease progression (2-3 annual MRI visits with additional years of annual clinical follow-up). STATISTICAL TESTS Two-sample t-test, Wilcoxon Rank-Sum test, Pearson's chi-square test, two-group comparison with linear-mixed-effect model, mixed-effect ANOVA, logistic regression. P-values <0.05 were considered significant. RESULTS A total of 58.3% of participants had at least one PRL at baseline. Higher male proportion in PRL+ group was found. Average change in PRL count was 0.20 (SD = 2.82) for PRLs and 0.00 (SD = 0.82) for mottled lesions. Mean and median pre-/post-contrast T1 were longer in PRL+ than in PRL-. No differences in mean χ were seen for lesions grouped by PRL (P = 0.310, pre-contrast; 0.086, post-contrast) or PRL/M presence (P = 0.234, pre-contrast; 0.163, post-contrast). Median χ were less negative in PRL+ and PRL/M+ than in PRL- and PRL/M-. Mean and median pre-/post-contrast R2* were slower in PRL+ compared to PRL-. Mean and median pre-/post-contrast R2* were slower in PRL/M+ than in PRL/M-. PRL presence at baseline was associated with confirmed EDSS Plus progression (OR 3.75 [1.22-7.59]) and PRL/M+ at baseline with confirmed EDSS Plus progression (OR 3.63 [1.14-7.43]). DATA CONCLUSION Evidence of BBB breakdown in PRLs was not seen. Quantitative metrics confirmed prior results suggesting greater demyelination, cell loss, and possibly disruption of tissue anisotropy in PRLs. EVIDENCE LEVEL 2 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Seongjin Choi
- Department of Neurology, University of Maryland School of Medicine, Baltimore Maryland
| | - Sarah Lake
- Hasbro Children’s Hospital, Brown University
| | - Daniel M. Harrison
- Department of Neurology, University of Maryland School of Medicine, Baltimore Maryland
- Department of Neurology, Baltimore Veterans Affairs Medical Center, Baltimore, Maryland
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Motegi H, Kufukihara K, Kitagawa S, Sekiguchi K, Hata J, Fujiwara H, Jinzaki M, Okano H, Nakamura M, Iguchi Y, Nakahara J. Non-lesional white matter changes depicted by q-space diffusional MRI correlate with clinical disabilities in multiple sclerosis. J Neurol Sci 2024; 456:122851. [PMID: 38181653 DOI: 10.1016/j.jns.2023.122851] [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: 07/12/2023] [Revised: 11/20/2023] [Accepted: 12/17/2023] [Indexed: 01/07/2024]
Abstract
BACKGROUND We previously developed an optimized q-space diffusional MRI technique (normalized leptokurtic diffusion [NLD] map) to delineate the demyelinated lesions of multiple sclerosis (MS) patients. Herein, we evaluated the utility of NLD maps to discern the white matter abnormalities in normal-appearing white matter (NAWM) and the abnormalities' possible associations with physical and cognitive disabilities in MS. METHODS We conducted a retrospective observational study of MS patients treated at our hospital (Jan. 2012 to Dec. 2022). Clinical and MRI data were collected; Processing Speed Test (PST) data were obtained when possible. For a quantitative analysis of the NLD maps, we calculated the NLD index as GVROI/GVREF, where GV is a mean grayscale value in the regions of interest (ROIs) and the reference area (REF; cerebrospinal fluid). RESULTS One hundred-one individuals with MS were included. The lower corpus callosum and non-lesional WM NLD index were associated with worse Expanded Disability Status Scale (EDSS) and PST scores. The NLD indexes in the corpus callosum (p < 0.0001) and non-lesional white matter (p < 0.0001) were significantly reduced in progressive MS compared to relapsing-remitting MS. We categorized MS severity as moderate/severe (EDSS score ≥ 4 points) and mild (EDSS score < 4 points). The NLD indexes in the corpus callosum (p < 0.0001) and non-lesional white matter (p < 0.0001) were significantly lower in the moderate/severe MS group compared to the mild MS group. CONCLUSION The NLD map revealed abnormalities in the non-lesional white matter, providing valuable insights for evaluating manifestations in MS patients.
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Affiliation(s)
- Haruhiko Motegi
- Department of Neurology, Keio University School of Medicine, Tokyo, Japan; Department of Neurology, The Jikei University School of Medicine, Tokyo, Japan.
| | - Kenji Kufukihara
- Department of Neurology, Keio University School of Medicine, Tokyo, Japan; Department of Neurology, National Hospital Organization Tokyo Medical Center, Tokyo, Japan.
| | - Satoshi Kitagawa
- Department of Neurology, Keio University School of Medicine, Tokyo, Japan.
| | - Koji Sekiguchi
- Department of Neurology, Keio University School of Medicine, Tokyo, Japan.
| | - Junichi Hata
- Graduate School of Human Health Sciences, Tokyo Metropolitan University, Tokyo, Japan; Department of Physiology, Keio University School of Medicine, Tokyo, Japan; Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science, Wako, Japan.
| | - Hirokazu Fujiwara
- Center of Preventive Medicine, Keio University School of Medicine, Tokyo, Japan.
| | - Masahiro Jinzaki
- Department of Radiology, Keio University School of Medicine, Tokyo, Japan.
| | - Hideyuki Okano
- Department of Physiology, Keio University School of Medicine, Tokyo, Japan; Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science, Wako, Japan.
| | - Masaya Nakamura
- Department of Orthopedic Surgery, Keio University School of Medicine, Tokyo, Japan.
| | - Yasuyuki Iguchi
- Department of Neurology, The Jikei University School of Medicine, Tokyo, Japan.
| | - Jin Nakahara
- Department of Neurology, Keio University School of Medicine, Tokyo, Japan.
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Zhou W, Xi J, Bao L. A latent code based multi-variable modulation network for susceptibility mapping. Front Neurosci 2023; 17:1308829. [PMID: 38188033 PMCID: PMC10771344 DOI: 10.3389/fnins.2023.1308829] [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: 10/07/2023] [Accepted: 12/04/2023] [Indexed: 01/09/2024] Open
Abstract
Quantitative susceptibility mapping (QSM) is a technique for obtaining quantitative information on tissue susceptibility and has shown promising potential for clinical applications, in which the magnetic susceptibility is calculated by solving an ill-posed inverse problem. Recently, deep learning-based methods are proposed to address this issue, but the diversity of data distribution was not well considered, and thus the model generalization is limited in clinical applications. In this paper, we propose a Latent Code based Multi-Variable modulation network for QSM reconstruction (LCMnet). Particularly, a specific modulation module is exploited to incorporate three variables, i.e., field map, magnitude image, and initial susceptibility. The latent code in the modulated convolution is learned from feature maps of the field data using the encoder-decoder framework. The susceptibility map pre-estimated from simple thresholding is the constant input of the module, thereby enhancing the network stability and accelerating training convergence. As another input, multi-level features generated by a cross-fusion block integrate the information of field and magnitude data effectively. Experimental results on in vivo human brain data, challenge data, clinical data and synthetic data demonstrate that the proposed method LCMnet can achieve outstanding performance on accurate susceptibility measurement and also excellent generalization.
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Affiliation(s)
| | | | - Lijun Bao
- Department of Electronic Science, Xiamen University, Xiamen, China
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Khormi I, Al-Iedani O, Alshehri A, Ramadan S, Lechner-Scott J. MR myelin imaging in multiple sclerosis: A scoping review. J Neurol Sci 2023; 455:122807. [PMID: 38035651 DOI: 10.1016/j.jns.2023.122807] [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: 07/24/2023] [Revised: 10/20/2023] [Accepted: 11/19/2023] [Indexed: 12/02/2023]
Abstract
The inability of disease-modifying therapies to stop the progression of multiple sclerosis (MS), has led to the development of a new therapeutic strategy focussing on myelin repair. While conventional MRI lacks sensitivity for quantifying myelin damage, advanced MRI techniques are proving effective. The development of targeted therapeutics requires histological validation of myelin imaging results, alongside the crucial task of establishing correlations between myelin imaging results and clinical assessments, so that the effectiveness of therapeutic interventions can be evaluated. The aims of this scoping review were to identify myelin imaging methods - some of which have been histologically validated, and to determine how these approaches correlate with clinical assessments of people with MS (pwMS), thus allowing for effective therapeutic evaluation. A search of two databases was undertaken for publications relating to studies on adults MS using either MRI/MR-histology of the MS brain in the range 1990-to-2022. The myelin imaging methods specified were relaxometry, magnetization transfer, and quantitative susceptibility. Relaxometry was used most frequently, with myelin water fraction (MWF) being the primary metric. Studies conducted on tissue from various regions of the brain showed that MWF was significantly lower in pwMS than in healthy controls. Magnetization transfer ratio indicated that the macromolecular content of lesions was lower than that of normal-appearing tissue. Higher magnetic susceptibility of lesions were indicative of myelin breakdown and iron accumulation. Several myelin imaging metrics were correlated with disability, disease severity and duration. Many studies showed a good correlation between myelin measured histologically and by MR myelin imaging techniques.
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Affiliation(s)
- Ibrahim Khormi
- School of Health Sciences, College of Health, Medicine and Wellbeing, University of Newcastle, Newcastle, Australia; Hunter Medical Research Institute, New Lambton Heights, Australia; College of Applied Medical Sciences, University of Jeddah, Jeddah, Saudi Arabia
| | - Oun Al-Iedani
- Hunter Medical Research Institute, New Lambton Heights, Australia; School of Biomedical Sciences and Pharmacy, College of Health, Medicine and Wellbeing, University of Newcastle, Newcastle, Australia
| | - Abdulaziz Alshehri
- School of Health Sciences, College of Health, Medicine and Wellbeing, University of Newcastle, Newcastle, Australia; Hunter Medical Research Institute, New Lambton Heights, Australia; Department of Radiology, King Fahd Hospital of the University, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - Saadallah Ramadan
- School of Health Sciences, College of Health, Medicine and Wellbeing, University of Newcastle, Newcastle, Australia; Hunter Medical Research Institute, New Lambton Heights, Australia.
| | - Jeannette Lechner-Scott
- Hunter Medical Research Institute, New Lambton Heights, Australia; Department of Neurology, John Hunter Hospital, New Lambton Heights, Australia; School of Medicine and Public Health, College of Health, Medicine and Wellbeing, University of Newcastle, Newcastle, Australia
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10
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Tozlu C, Olafson E, Jamison KW, Demmon E, Kaunzner U, Marcille M, Zinger N, Michaelson N, Safi N, Nguyen T, Gauthier S, Kuceyeski A. The sequence of regional structural disconnectivity due to multiple sclerosis lesions. Brain Commun 2023; 5:fcad332. [PMID: 38107503 PMCID: PMC10724045 DOI: 10.1093/braincomms/fcad332] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 09/07/2023] [Accepted: 12/05/2023] [Indexed: 12/19/2023] Open
Abstract
Prediction of disease progression is challenging in multiple sclerosis as the sequence of lesion development and retention of inflammation within a subset of chronic lesions is heterogeneous among patients. We investigated the sequence of lesion-related regional structural disconnectivity across the spectrum of disability and cognitive impairment in multiple sclerosis. In a full cohort of 482 multiple sclerosis patients (age: 41.83 ± 11.63 years, 71.57% females), the Expanded Disability Status Scale was used to classify patients into (i) no or mild (Expanded Disability Status Scale <3) versus (ii) moderate or severe disability groups (Expanded Disability Status Scale ≥3). In 363 out of 482 patients, quantitative susceptibility mapping was used to identify paramagnetic rim lesions, which are maintained by a rim of iron-laden innate immune cells. In 171 out of 482 patients, Brief International Cognitive Assessment was used to identify subjects as being cognitively preserved or impaired. Network Modification Tool was used to estimate the regional structural disconnectivity due to multiple sclerosis lesions. Discriminative event-based modelling was applied to investigate the sequence of regional structural disconnectivity due to (i) all representative T2 fluid-attenuated inversion recovery lesions, (ii) paramagnetic rim lesions versus non-paramagnetic rim lesions separately across disability groups ('no to mild disability' to 'moderate to severe disability'), (iii) all representative T2 fluid-attenuated inversion recovery lesions and (iv) paramagnetic rim lesions versus non-paramagnetic rim lesions separately across cognitive status ('cognitively preserved' to 'cognitively impaired'). In the full cohort, structural disconnection in the ventral attention and subcortical networks, particularly in the supramarginal and putamen regions, was an early biomarker of moderate or severe disability. The earliest biomarkers of disability progression were structural disconnections due to paramagnetic rim lesions in the motor-related regions. Subcortical structural disconnection, particularly in the ventral diencephalon and thalamus regions, was an early biomarker of cognitive impairment. Our data-driven model revealed that the structural disconnection in the subcortical regions, particularly in the thalamus, is an early biomarker for both disability and cognitive impairment in multiple sclerosis. Paramagnetic rim lesions-related structural disconnection in the motor cortex may identify the patients at risk for moderate or severe disability in multiple sclerosis. Such information might be used to identify people with multiple sclerosis who have an increased risk of disability progression or cognitive decline in order to provide personalized treatment plans.
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Affiliation(s)
- Ceren Tozlu
- Department of Radiology, Weill Cornell Medicine, NewYork, NY, 10065, USA
| | - Emily Olafson
- Department of Radiology, Weill Cornell Medicine, NewYork, NY, 10065, USA
| | - Keith W Jamison
- Department of Radiology, Weill Cornell Medicine, NewYork, NY, 10065, USA
| | - Emily Demmon
- Department of Neurology, Weill Cornell Medical College, NewYork, NY, 10065, USA
| | - Ulrike Kaunzner
- Department of Neurology, Weill Cornell Medical College, NewYork, NY, 10065, USA
| | - Melanie Marcille
- Department of Neurology, Weill Cornell Medical College, NewYork, NY, 10065, USA
| | - Nicole Zinger
- Department of Neurology, Weill Cornell Medical College, NewYork, NY, 10065, USA
| | - Nara Michaelson
- Department of Neurology, Weill Cornell Medical College, NewYork, NY, 10065, USA
| | - Neha Safi
- Department of Neurology, Weill Cornell Medical College, NewYork, NY, 10065, USA
| | - Thanh Nguyen
- Department of Radiology, Weill Cornell Medicine, NewYork, NY, 10065, USA
| | - Susan Gauthier
- Department of Radiology, Weill Cornell Medicine, NewYork, NY, 10065, USA
- Department of Neurology, Weill Cornell Medical College, NewYork, NY, 10065, USA
| | - Amy Kuceyeski
- Department of Radiology, Weill Cornell Medicine, NewYork, NY, 10065, USA
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11
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Cacciaguerra L, Rocca MA, Filippi M. Understanding the Pathophysiology and Magnetic Resonance Imaging of Multiple Sclerosis and Neuromyelitis Optica Spectrum Disorders. Korean J Radiol 2023; 24:1260-1283. [PMID: 38016685 DOI: 10.3348/kjr.2023.0360] [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: 04/26/2023] [Revised: 08/09/2023] [Accepted: 08/21/2023] [Indexed: 11/30/2023] Open
Abstract
Magnetic resonance imaging (MRI) has been extensively applied in the study of multiple sclerosis (MS), substantially contributing to diagnosis, differential diagnosis, and disease monitoring. MRI studies have significantly contributed to the understanding of MS through the characterization of typical radiological features and their clinical or prognostic implications using conventional MRI pulse sequences and further with the application of advanced imaging techniques sensitive to microstructural damage. Interpretation of results has often been validated by MRI-pathology studies. However, the application of MRI techniques in the study of neuromyelitis optica spectrum disorders (NMOSD) remains an emerging field, and MRI studies have focused on radiological correlates of NMOSD and its pathophysiology to aid in diagnosis, improve monitoring, and identify relevant prognostic factors. In this review, we discuss the main contributions of MRI to the understanding of MS and NMOSD, focusing on the most novel discoveries to clarify differences in the pathophysiology of focal inflammation initiation and perpetuation, involvement of normal-appearing tissue, potential entry routes of pathogenic elements into the CNS, and existence of primary or secondary mechanisms of neurodegeneration.
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Affiliation(s)
- Laura Cacciaguerra
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milano, Italy
| | - Maria A Rocca
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milano, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milano, Italy
- Vita-Salute San Raffaele University, Milano, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milano, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milano, Italy
- Vita-Salute San Raffaele University, Milano, Italy
- Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milano, Italy
- Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milano, Italy.
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12
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Zierfuss B, Wang Z, Jackson AN, Moezzi D, Yong VW. Iron in multiple sclerosis - Neuropathology, immunology, and real-world considerations. Mult Scler Relat Disord 2023; 78:104934. [PMID: 37579645 DOI: 10.1016/j.msard.2023.104934] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Revised: 07/30/2023] [Accepted: 08/08/2023] [Indexed: 08/16/2023]
Abstract
Iron is an essential element involved in a multitude of bodily processes. It is tightly regulated, as elevated deposition in tissues is associated with diseases such as multiple sclerosis (MS). Iron accumulation in the central nervous system (CNS) of MS patients is linked to neurotoxicity through mechanisms including oxidative stress, glutamate excitotoxicity, misfolding of proteins, and ferroptosis. In the past decade, the combination of MRI and histopathology has enhanced our understanding of iron deposition in MS pathophysiology, including in the pro-inflammatory and neurotoxicity of iron-laden rims of chronic active lesions. In this regard, iron accumulation may not only have an impact on different CNS-resident cells but may also promote the innate and adaptive immune dysfunctions in MS. Although there are discordant results, most studies indicate lower levels of iron but higher amounts of the iron storage molecule ferritin in the circulation of people with MS. Considering the importance of iron, there is a need for evidence-guided recommendation for dietary intake in people living with MS. Potential novel therapeutic approaches include the regulation of iron levels using next generation iron chelators, as well as therapies to interfere with toxic consequences of iron overload including antioxidants in MS.
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Affiliation(s)
- Bettina Zierfuss
- The Research Center of the Centre Hospitalier de l'Université de Montréal (CRCHUM), Department of Neuroscience, Faculty of Medicine, Université de Montréal, Montréal H2X 0A9, Québec, Canada
| | - Zitong Wang
- Department of Psychiatry, College of Health Sciences, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta T6G 2B7, Canada
| | - Alexandra N Jackson
- School of Rehabilitation Therapy, Faculty of Health Sciences, Queen's University, Kingston, Ontario K7L 3N6, Canada
| | - Dorsa Moezzi
- The Hotchkiss Brain Institute and the Department of Clinical Neurosciences, University of Calgary, 3330 Hospital Dr NW, Calgary, Alberta T2N 4N1, Canada
| | - V Wee Yong
- The Hotchkiss Brain Institute and the Department of Clinical Neurosciences, University of Calgary, 3330 Hospital Dr NW, Calgary, Alberta T2N 4N1, Canada.
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13
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Fang Z, Lai KW, van Zijl P, Li X, Sulam J. DeepSTI: Towards tensor reconstruction using fewer orientations in susceptibility tensor imaging. Med Image Anal 2023; 87:102829. [PMID: 37146440 PMCID: PMC10288385 DOI: 10.1016/j.media.2023.102829] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 03/11/2023] [Accepted: 04/18/2023] [Indexed: 05/07/2023]
Abstract
Susceptibility tensor imaging (STI) is an emerging magnetic resonance imaging technique that characterizes the anisotropic tissue magnetic susceptibility with a second-order tensor model. STI has the potential to provide information for both the reconstruction of white matter fiber pathways and detection of myelin changes in the brain at mm resolution or less, which would be of great value for understanding brain structure and function in healthy and diseased brain. However, the application of STI in vivo has been hindered by its cumbersome and time-consuming acquisition requirement of measuring susceptibility induced MR phase changes at multiple head orientations. Usually, sampling at more than six orientations is required to obtain sufficient information for the ill-posed STI dipole inversion. This complexity is enhanced by the limitation in head rotation angles due to physical constraints of the head coil. As a result, STI has not yet been widely applied in human studies in vivo. In this work, we tackle these issues by proposing an image reconstruction algorithm for STI that leverages data-driven priors. Our method, called DeepSTI, learns the data prior implicitly via a deep neural network that approximates the proximal operator of a regularizer function for STI. The dipole inversion problem is then solved iteratively using the learned proximal network. Experimental results using both simulation and in vivo human data demonstrate great improvement over state-of-the-art algorithms in terms of the reconstructed tensor image, principal eigenvector maps and tractography results, while allowing for tensor reconstruction with MR phase measured at much less than six different orientations. Notably, promising reconstruction results are achieved by our method from only one orientation in human in vivo, and we demonstrate a potential application of this technique for estimating lesion susceptibility anisotropy in patients with multiple sclerosis.
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Affiliation(s)
- Zhenghan Fang
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA; Johns Hopkins Kavli Neuroscience Discovery Institute, Baltimore, MD 21218, USA
| | - Kuo-Wei Lai
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Peter van Zijl
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD 21205, USA; Department of Radiology and Radiological Sciences, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Xu Li
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD 21205, USA; Department of Radiology and Radiological Sciences, Johns Hopkins University, Baltimore, MD 21205, USA.
| | - Jeremias Sulam
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA; Johns Hopkins Kavli Neuroscience Discovery Institute, Baltimore, MD 21218, USA.
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14
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Si W, Guo Y, Zhang Q, Zhang J, Wang Y, Feng Y. Quantitative susceptibility mapping using multi-channel convolutional neural networks with dipole-adaptive multi-frequency inputs. Front Neurosci 2023; 17:1165446. [PMID: 37383103 PMCID: PMC10293650 DOI: 10.3389/fnins.2023.1165446] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 05/17/2023] [Indexed: 06/30/2023] Open
Abstract
Quantitative susceptibility mapping (QSM) quantifies the distribution of magnetic susceptibility and shows great potential in assessing tissue contents such as iron, myelin, and calcium in numerous brain diseases. The accuracy of QSM reconstruction was challenged by an ill-posed field-to-susceptibility inversion problem, which is related to the impaired information near the zero-frequency response of the dipole kernel. Recently, deep learning methods demonstrated great capability in improving the accuracy and efficiency of QSM reconstruction. However, the construction of neural networks in most deep learning-based QSM methods did not take the intrinsic nature of the dipole kernel into account. In this study, we propose a dipole kernel-adaptive multi-channel convolutional neural network (DIAM-CNN) method for the dipole inversion problem in QSM. DIAM-CNN first divided the original tissue field into high-fidelity and low-fidelity components by thresholding the dipole kernel in the frequency domain, and it then inputs the two components as additional channels into a multichannel 3D Unet. QSM maps from the calculation of susceptibility through multiple orientation sampling (COSMOS) were used as training labels and evaluation reference. DIAM-CNN was compared with two conventional model-based methods [morphology enabled dipole inversion (MEDI) and improved sparse linear equation and least squares (iLSQR) and one deep learning method (QSMnet)]. High-frequency error norm (HFEN), peak signal-to-noise-ratio (PSNR), normalized root mean squared error (NRMSE), and the structural similarity index (SSIM) were reported for quantitative comparisons. Experiments on healthy volunteers demonstrated that the DIAM-CNN results had superior image quality to those of the MEDI, iLSQR, or QSMnet results. Experiments on data with simulated hemorrhagic lesions demonstrated that DIAM-CNN produced fewer shadow artifacts around the bleeding lesion than the compared methods. This study demonstrates that the incorporation of dipole-related knowledge into the network construction has a potential to improve deep learning-based QSM reconstruction.
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Affiliation(s)
- Wenbin Si
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
| | - Yihao Guo
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, Hainan, China
| | - Qianqian Zhang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
| | - Jinwei Zhang
- Department of Biomedical Engineering, College of Engineering, Cornell University, Ithaca, NY, United States
- Department of Radiology, Weill Cornell Medicine, Cornell University, New York, NY, United States
| | - Yi Wang
- Department of Biomedical Engineering, College of Engineering, Cornell University, Ithaca, NY, United States
- Department of Radiology, Weill Cornell Medicine, Cornell University, New York, NY, United States
| | - Yanqiu Feng
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
- Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence and Key Laboratory of Mental Health of the Ministry of Education, Southern Medical University, Guangzhou, China
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15
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Dimov AV, Li J, Nguyen TD, Roberts AG, Spincemaille P, Straub S, Zun Z, Prince MR, Wang Y. QSM Throughout the Body. J Magn Reson Imaging 2023; 57:1621-1640. [PMID: 36748806 PMCID: PMC10192074 DOI: 10.1002/jmri.28624] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 01/19/2023] [Accepted: 01/20/2023] [Indexed: 02/08/2023] Open
Abstract
Magnetic materials in tissue, such as iron, calcium, or collagen, can be studied using quantitative susceptibility mapping (QSM). To date, QSM has been overwhelmingly applied in the brain, but is increasingly utilized outside the brain. QSM relies on the effect of tissue magnetic susceptibility sources on the MR signal phase obtained with gradient echo sequence. However, in the body, the chemical shift of fat present within the region of interest contributes to the MR signal phase as well. Therefore, correcting for the chemical shift effect by means of water-fat separation is essential for body QSM. By employing techniques to compensate for cardiac and respiratory motion artifacts, body QSM has been applied to study liver iron and fibrosis, heart chamber blood and placenta oxygenation, myocardial hemorrhage, atherosclerotic plaque, cartilage, bone, prostate, breast calcification, and kidney stone.
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Affiliation(s)
- Alexey V. Dimov
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | - Jiahao Li
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | - Thanh D. Nguyen
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | | | - Pascal Spincemaille
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | - Sina Straub
- Department of Radiology, Mayo Clinic, Jacksonville, FL, United States
| | - Zungho Zun
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | - Martin R. Prince
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | - Yi Wang
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
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16
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Silva J, Milovic C, Lambert M, Montalba C, Arrieta C, Irarrazaval P, Uribe S, Tejos C. Toward a realistic in silico abdominal phantom for QSM. Magn Reson Med 2023; 89:2402-2418. [PMID: 36695213 PMCID: PMC10952412 DOI: 10.1002/mrm.29597] [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: 08/09/2022] [Revised: 12/18/2022] [Accepted: 01/06/2023] [Indexed: 01/26/2023]
Abstract
PURPOSE QSM outside the brain has recently gained interest, particularly in the abdominal region. However, the absence of reliable ground truths makes difficult to assess reconstruction algorithms, whose quality is already compromised by additional signal contributions from fat, gases, and different kinds of motion. This work presents a realistic in silico phantom for the development, evaluation and comparison of abdominal QSM reconstruction algorithms. METHODS Synthetic susceptibility andR 2 * $$ {R}_2^{\ast } $$ maps were generated by segmenting and postprocessing the abdominal 3T MRI data from a healthy volunteer. Susceptibility andR 2 * $$ {R}_2^{\ast } $$ values in different tissues/organs were assigned according to literature and experimental values and were also provided with realistic textures. The signal was simulated using as input the synthetic QSM andR 2 * $$ {R}_2^{\ast } $$ maps and fat contributions. Three susceptibility scenarios and two acquisition protocols were simulated to compare different reconstruction algorithms. RESULTS QSM reconstructions show that the phantom allows to identify the main strengths and limitations of the acquisition approaches and reconstruction algorithms, such as in-phase acquisitions, water-fat separation methods, and QSM dipole inversion algorithms. CONCLUSION The phantom showed its potential as a ground truth to evaluate and compare reconstruction pipelines and algorithms. The publicly available source code, designed in a modular framework, allows users to easily modify the susceptibility,R 2 * $$ {R}_2^{\ast } $$ and TEs, and thus creates different abdominal scenarios.
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Affiliation(s)
- Javier Silva
- Department of Electrical EngineeringPontificia Universidad Católica de Chile
SantiagoChile
- Biomedical Imaging CenterPontificia Universidad Católica de ChileSantiagoChile
- Millennium Institute for Intelligent Healthcare Engineering (iHEALTH)SantiagoChile
| | - Carlos Milovic
- Millennium Institute for Intelligent Healthcare Engineering (iHEALTH)SantiagoChile
- School of Electrical EngineeringPontificia Universidad Católica de ValparaísoValparaísoChile
| | - Mathias Lambert
- Department of Electrical EngineeringPontificia Universidad Católica de Chile
SantiagoChile
- Biomedical Imaging CenterPontificia Universidad Católica de ChileSantiagoChile
- Millennium Institute for Intelligent Healthcare Engineering (iHEALTH)SantiagoChile
| | - Cristian Montalba
- Biomedical Imaging CenterPontificia Universidad Católica de ChileSantiagoChile
- Millennium Institute for Intelligent Healthcare Engineering (iHEALTH)SantiagoChile
- Department of Radiology, School of MedicinePontificia Universidad Católica de ChileSantiagoChile
| | - Cristóbal Arrieta
- Biomedical Imaging CenterPontificia Universidad Católica de ChileSantiagoChile
- Millennium Institute for Intelligent Healthcare Engineering (iHEALTH)SantiagoChile
| | - Pablo Irarrazaval
- Department of Electrical EngineeringPontificia Universidad Católica de Chile
SantiagoChile
- Biomedical Imaging CenterPontificia Universidad Católica de ChileSantiagoChile
- Millennium Institute for Intelligent Healthcare Engineering (iHEALTH)SantiagoChile
- Institute for Biological and Medical Engineering, Pontificia Universidad Católica de ChileSantiagoChile
| | - Sergio Uribe
- Biomedical Imaging CenterPontificia Universidad Católica de ChileSantiagoChile
- Millennium Institute for Intelligent Healthcare Engineering (iHEALTH)SantiagoChile
- Department of Radiology, School of MedicinePontificia Universidad Católica de ChileSantiagoChile
| | - Cristian Tejos
- Department of Electrical EngineeringPontificia Universidad Católica de Chile
SantiagoChile
- Biomedical Imaging CenterPontificia Universidad Católica de ChileSantiagoChile
- Millennium Institute for Intelligent Healthcare Engineering (iHEALTH)SantiagoChile
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17
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He J, Peng Y, Fu B, Zhu Y, Wang L, Wang R. msQSM: Morphology-based Self-supervised Deep Learning for Quantitative Susceptibility Mapping. Neuroimage 2023; 275:120181. [PMID: 37220799 DOI: 10.1016/j.neuroimage.2023.120181] [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: 02/11/2023] [Revised: 04/20/2023] [Accepted: 05/19/2023] [Indexed: 05/25/2023] Open
Abstract
Quantitative susceptibility mapping (QSM) has been applied to the measurement of iron deposition and the auxiliary diagnosis of neurodegenerative disease. There still exists a dipole inversion problem in QSM reconstruction. Recently, deep learning approaches have been proposed to resolve this problem. However, most of these approaches are supervised methods that need pairs of the input phase and ground-truth. It remains a challenge to train a model for all resolutions without using the ground-truth and only using one resolution data. To address this, we proposed a self-supervised QSM deep learning method based on morphology. It consists of a morphological QSM builder to decouple the dependency of the QSM on acquisition resolution, and a morphological loss to reduce artifacts effectively and save training time efficiently. The proposed method can reconstruct arbitrary resolution QSM on both human data and animal data, regardless of whether the resolution is higher or lower than that of the training set. Our method outperforms the previous best unsupervised method with a 3.6% higher peak signal-to-noise ratio, 16.2% lower normalized root mean square error, and 22.1% lower high-frequency error norm. The morphological loss reduces training time by 22.1% with respect to the cycle gradient loss used in the previous unsupervised methods. Experimental results show that the proposed method accurately measures QSM with arbitrary resolutions, and achieves state-of-the-art results among unsupervised deep learning methods. Research on applications in neurodegenerative diseases found that our method is robust enough to measure significant increase in striatal magnetic susceptibility in patients during Alzheimer's disease progression, as well as significant increase in substantia nigra susceptibility in Parkinson's disease patients, and can be used as an auxiliary differential diagnosis tool for Alzheimer's disease and Parkinson's disease.
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Affiliation(s)
- Junjie He
- Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, No. 2288, Huaxi Avenue, Guiyang, 550002, Guizhou, China; Department of Radiology, International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment, Guizhou Provincial People's Hospital, No. 83, Zhongshan Dong Road, Guiyang, 550002, Guizhou, China
| | - Yunsong Peng
- Department of Radiology, International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment, Guizhou Provincial People's Hospital, No. 83, Zhongshan Dong Road, Guiyang, 550002, Guizhou, China
| | - Bangkang Fu
- Department of Radiology, International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment, Guizhou Provincial People's Hospital, No. 83, Zhongshan Dong Road, Guiyang, 550002, Guizhou, China
| | - Yuemin Zhu
- CREATIS, IRP Metislab, University of Lyon, INSA Lyon, CNRS UMR 5220, Inserm U1294, Lyon, France
| | - Lihui Wang
- Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, No. 2288, Huaxi Avenue, Guiyang, 550002, Guizhou, China
| | - Rongpin Wang
- Department of Radiology, International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment, Guizhou Provincial People's Hospital, No. 83, Zhongshan Dong Road, Guiyang, 550002, Guizhou, China.
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18
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Stellingwerff MD, Pouwels PJW, Roosendaal SD, Barkhof F, van der Knaap MS. Quantitative MRI in leukodystrophies. Neuroimage Clin 2023; 38:103427. [PMID: 37150021 PMCID: PMC10193020 DOI: 10.1016/j.nicl.2023.103427] [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: 12/14/2022] [Revised: 04/27/2023] [Accepted: 04/28/2023] [Indexed: 05/09/2023]
Abstract
Leukodystrophies constitute a large and heterogeneous group of genetic diseases primarily affecting the white matter of the central nervous system. Different disorders target different white matter structural components. Leukodystrophies are most often progressive and fatal. In recent years, novel therapies are emerging and for an increasing number of leukodystrophies trials are being developed. Objective and quantitative metrics are needed to serve as outcome measures in trials. Quantitative MRI yields information on microstructural properties, such as myelin or axonal content and condition, and on the chemical composition of white matter, in a noninvasive fashion. By providing information on white matter microstructural involvement, quantitative MRI may contribute to the evaluation and monitoring of leukodystrophies. Many distinct MR techniques are available at different stages of development. While some are already clinically applicable, others are less far developed and have only or mainly been applied in healthy subjects. In this review, we explore the background, current status, potential and challenges of available quantitative MR techniques in the context of leukodystrophies.
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Affiliation(s)
- Menno D Stellingwerff
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Child Neurology, Emma Children's Hospital, and Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, the Netherlands
| | - Petra J W Pouwels
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Department of Radiology and Nuclear Medicine, and Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, the Netherlands
| | - Stefan D Roosendaal
- Amsterdam UMC Location University of Amsterdam, Department of Radiology, Meibergdreef 9, Amsterdam, the Netherlands
| | - Frederik Barkhof
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Department of Radiology and Nuclear Medicine, and Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, the Netherlands; University College London, Institutes of Neurology and Healthcare Engineering, London, UK
| | - Marjo S van der Knaap
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Child Neurology, Emma Children's Hospital, and Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, the Netherlands; Vrije Universiteit Amsterdam, Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, De Boelelaan 1105, Amsterdam, the Netherlands.
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19
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Perosa V, Rotta J, Yakupov R, Kuijf HJ, Schreiber F, Oltmer JT, Mattern H, Heinze HJ, Düzel E, Schreiber S. Implications of quantitative susceptibility mapping at 7 Tesla MRI for microbleeds detection in cerebral small vessel disease. Front Neurol 2023; 14:1112312. [PMID: 37006483 PMCID: PMC10050564 DOI: 10.3389/fneur.2023.1112312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 02/20/2023] [Indexed: 03/17/2023] Open
Abstract
BackgroundCerebral microbleeds (MBs) are a hallmark of cerebral small vessel disease (CSVD) and can be found on T2*-weighted sequences on MRI. Quantitative susceptibility mapping (QSM) is a postprocessing method that also enables MBs identification and furthermore allows to differentiate them from calcifications.AimsWe explored the implications of using QSM at submillimeter resolution for MBs detection in CSVD.MethodsBoth 3 and 7 Tesla (T) MRI were performed in elderly participants without MBs and patients with CSVD. MBs were quantified on T2*-weighted imaging and QSM. Differences in the number of MBs were assessed, and subjects were classified in CSVD subgroups or controls both on 3T T2*-weighted imaging and 7T QSM.Results48 participants [mean age (SD) 70.9 (8.8) years, 48% females] were included: 31 were healthy controls, 6 probable cerebral amyloid angiopathy (CAA), 9 mixed CSVD, and 2 were hypertensive arteriopathy [HA] patients. After accounting for the higher number of MBs detected at 7T QSM (Median = Mdn; Mdn7T−QSM = 2.5; Mdn3T−T2 = 0; z = 4.90; p < 0.001) and false positive MBs (6.1% calcifications), most healthy controls (80.6%) demonstrated at least one MB and more MBs were discovered in the CSVD group.ConclusionsOur observations suggest that QSM at submillimeter resolution improves the detection of MBs in the elderly human brain. A higher prevalence of MBs than so far known in healthy elderly was revealed.
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Affiliation(s)
- Valentina Perosa
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Boston, MA, United States
- *Correspondence: Valentina Perosa
| | - Johanna Rotta
- Department of Neurology, Otto-von-Guericke University, Magdeburg, Germany
| | - Renat Yakupov
- Institute of Cognitive Neurology and Dementia Research (IKND), Magdeburg, Germany
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Hugo J. Kuijf
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands
| | - Frank Schreiber
- Department of Neurology, Otto-von-Guericke University, Magdeburg, Germany
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Jan T. Oltmer
- Athinoula A. Martinos Center, Massachusetts General Hospital, Department of Radiology, Boston, MA, United States
| | - Hendrik Mattern
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Institute of Physics, Otto-von-Guericke University, Magdeburg, Germany
| | - Hans-Jochen Heinze
- Department of Neurology, Otto-von-Guericke University, Magdeburg, Germany
- Institute of Cognitive Neurology and Dementia Research (IKND), Magdeburg, Germany
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Center for Behavioral Brain Sciences, Magdeburg, Germany
| | - Emrah Düzel
- Institute of Cognitive Neurology and Dementia Research (IKND), Magdeburg, Germany
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Center for Behavioral Brain Sciences, Magdeburg, Germany
- Institute of Cognitive Neuroscience, University College London, London, United Kingdom
| | - Stefanie Schreiber
- Department of Neurology, Otto-von-Guericke University, Magdeburg, Germany
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Center for Behavioral Brain Sciences, Magdeburg, Germany
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20
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Tozlu C, Olafson E, Jamison K, Demmon E, Kaunzner U, Marcille M, Zinger N, Michaelson N, Safi N, Nguyen T, Gauthier S, Kuceyeski A. The sequence of regional structural disconnectivity due to multiple sclerosis lesions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.26.525537. [PMID: 36747675 PMCID: PMC9900990 DOI: 10.1101/2023.01.26.525537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Objective Prediction of disease progression is challenging in multiple sclerosis (MS) as the sequence of lesion development and retention of inflammation within a subset of chronic lesions is heterogeneous among patients. We investigated the sequence of lesion-related regional structural disconnectivity across the spectrum of disability and cognitive impairment in MS. Methods In a full cohort of 482 patients, the Expanded Disability Status Scale was used to classify patients into (i) no or mild vs (ii) moderate or severe disability groups. In 363 out of 482 patients, Quantitative Susceptibility Mapping was used to identify paramagnetic rim lesions (PRL), which are maintained by a rim of iron-laden innate immune cells. In 171 out of 482 patients, Brief International Cognitive Assessment was used to identify subjects with cognitive impairment. Network Modification Tool was used to estimate the regional structural disconnectivity due to MS lesions. Discriminative event-based modeling was applied to investigate the sequence of regional structural disconnectivity due to all representative lesions across the spectrum of disability and cognitive impairment. Results Structural disconnection in the ventral attention and subcortical networks was an early biomarker of moderate or severe disability. The earliest biomarkers of disability progression were structural disconnections due to PRL in the motor-related regions. Subcortical structural disconnection was an early biomarker of cognitive impairment. Interpretation MS lesion-related structural disconnections in the subcortex is an early biomarker for both disability and cognitive impairment in MS. PRL-related structural disconnection in the motor cortex may identify the patients at risk for moderate or severe disability in MS.
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Affiliation(s)
- Ceren Tozlu
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Emily Olafson
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Keith Jamison
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Emily Demmon
- Department of Neurology, Weill Cornell Medical College, New York, New York, USA
| | - Ulrike Kaunzner
- Department of Neurology, Weill Cornell Medical College, New York, New York, USA
| | - Melanie Marcille
- Department of Neurology, Weill Cornell Medical College, New York, New York, USA
| | - Nicole Zinger
- Department of Neurology, Weill Cornell Medical College, New York, New York, USA
| | - Nara Michaelson
- Department of Neurology, Weill Cornell Medical College, New York, New York, USA
| | - Neha Safi
- Department of Neurology, Weill Cornell Medical College, New York, New York, USA
| | - Thanh Nguyen
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Susan Gauthier
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
- Department of Neurology, Weill Cornell Medical College, New York, New York, USA
| | - Amy Kuceyeski
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
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21
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Liao H, Cai Z, Ye H, Chen Q, Zhang Y, Shaghaghi M, Lutz SE, Chen W, Cai K. Combining in vivo proton exchange rate ( k ex) MRI with quantitative susceptibility mapping to further stratify the gadolinium-negative multiple sclerosis lesions. Front Neurosci 2023; 16:1105376. [PMID: 36711150 PMCID: PMC9875136 DOI: 10.3389/fnins.2022.1105376] [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: 11/22/2022] [Accepted: 12/28/2022] [Indexed: 01/13/2023] Open
Abstract
Background Conventional gadolinium (Gd)-enhanced MRI is currently used for stratifying the lesion activity of multiple sclerosis (MS) despite limited correlation with disability and disease activity. The stratification of MS lesion activity needs further improvement to better support clinics. Purpose To investigate if the novel proton exchange rate (k ex ) MRI combined with quantitative susceptibility mapping (QSM) may help to further stratify non-enhanced (Gd-negative) MS lesions. Materials and methods From December 2017 to December 2020, clinically diagnosed relapsing-remitting MS patients who underwent MRI were consecutively enrolled in this IRB-approved retrospective study. The customized MRI protocol covered conventional T2-weighted, T2-fluid-attenuated-inversion-recovery, pre- and post-contrast T1-weighted imaging, and quantitative sequences, including k ex MRI based on direct-saturation removed omega plots and QSM. Each MS lesion was evaluated based on its Gd-enhancement as well as its susceptibility and k ex elevation compared to the normal appearing white matter. The difference and correlation concerning lesion characteristics and imaging contrasts were analyzed using the Mann-Whitney U test or Kruskal-Wallis test, and Spearman rank analysis with p < 0.05 considered significant. Results A total of 322 MS lesions from 30 patients were identified with 153 Gd-enhanced and 169 non-enhanced lesions. We found that the k ex elevation of all lesions significantly correlated with their susceptibility elevation (r = 0.30, p < 0.001). Within the 153 MS lesions with Gd-enhancement, ring-enhanced lesions showed higher k ex elevation than the nodular-enhanced ones' (p < 0.001). Similarly, lesions with ring-hyperintensity in QSM also had higher k ex elevation than the lesions with nodular-QSM-hyperintensity (p < 0.001). Of the 169 Gd-negative lesions, three radiological patterns were recognized according to lesion manifestations on the k ex map and QSM images: Pattern I (k ex + and QSM+, n = 114, 67.5%), Pattern II (only k ex + or QSM+, n = 47, 27.8%) and Pattern III (k ex - and QSM-, n = 8, 4.7%). Compared to Pattern II and III, Pattern I had higher k ex (p < 0.001) and susceptibility (p < 0.05) elevation. The percentage of Pattern I of each subject was negatively correlated with the disease duration (r = -0.45, p = 0.015). Conclusion As a potential imaging biomarker for inflammation due to oxidative stress, in vivo k ex MRI combined with QSM is promising in extending the clinical classification of MS lesions beyond conventional Gd-enhanced MRI.
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Affiliation(s)
- Huiting Liao
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zimeng Cai
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China,Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Haiqi Ye
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China,Department of Radiology, Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - QianLan Chen
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China,Experimental and Clinical Research Center, Max Delbrück Center for Molecular Medicine and Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Berlin Institute of Health, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Yan Zhang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Mehran Shaghaghi
- Department of Radiology, University of Illinois Hospital and Health Sciences System, Chicago, IL, United States
| | - Sarah E. Lutz
- Department of Anatomy and Cell Biology, University of Illinois at Chicago College of Medicine, Chicago, IL, United States
| | - Weiwei Chen
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China,*Correspondence: Weiwei Chen,
| | - Kejia Cai
- Department of Radiology, University of Illinois Hospital and Health Sciences System, Chicago, IL, United States
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22
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Quantitative Susceptibility Mapping in Cognitive Decline: A Review of Technical Aspects and Applications. Cognit Comput 2022. [DOI: 10.1007/s12559-022-10095-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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23
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Tranfa M, Pontillo G, Petracca M, Brunetti A, Tedeschi E, Palma G, Cocozza S. Quantitative MRI in Multiple Sclerosis: From Theory to Application. AJNR Am J Neuroradiol 2022; 43:1688-1695. [PMID: 35680161 DOI: 10.3174/ajnr.a7536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 02/22/2022] [Indexed: 02/01/2023]
Abstract
Quantitative MR imaging techniques allow evaluating different aspects of brain microstructure, providing meaningful information about the pathophysiology of damage in CNS disorders. In the study of patients with MS, quantitative MR imaging techniques represent an invaluable tool for studying changes in myelin and iron content occurring in the context of inflammatory and neurodegenerative processes. In the first section of this review, we summarize the physics behind quantitative MR imaging, here defined as relaxometry and quantitative susceptibility mapping, and describe the neurobiological correlates of quantitative MR imaging findings. In the second section, we focus on quantitative MR imaging application in MS, reporting the main findings in both the gray and white matter compartments, separately addressing macroscopically damaged and normal-appearing parenchyma.
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Affiliation(s)
- M Tranfa
- From the Departments of Advanced Biomedical Sciences (M.T., G. Pontillo, A.B., E.T., S.C.)
| | - G Pontillo
- From the Departments of Advanced Biomedical Sciences (M.T., G. Pontillo, A.B., E.T., S.C.) .,Electrical Engineering and Information Technology (G. Pontillo), University of Naples "Federico II," Naples, Italy
| | - M Petracca
- Department of Human Neurosciences (M.P.), Sapienza University of Rome, Rome, Italy
| | - A Brunetti
- From the Departments of Advanced Biomedical Sciences (M.T., G. Pontillo, A.B., E.T., S.C.)
| | - E Tedeschi
- From the Departments of Advanced Biomedical Sciences (M.T., G. Pontillo, A.B., E.T., S.C.)
| | - G Palma
- Institute of Nanotechnology (G. Palma), National Research Council, Lecce, Italy
| | - S Cocozza
- From the Departments of Advanced Biomedical Sciences (M.T., G. Pontillo, A.B., E.T., S.C.)
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24
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Kim W, Shin HG, Lee H, Park D, Kang J, Nam Y, Lee J, Jang J. χ-Separation Imaging for Diagnosis of Multiple Sclerosis versus Neuromyelitis Optica Spectrum Disorder. Radiology 2022; 307:e220941. [PMID: 36413128 DOI: 10.1148/radiol.220941] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Background Use of χ-separation imaging can provide surrogates for iron and myelin that relate closely to abnormal changes in multiple sclerosis (MS) lesions. Purpose To evaluate the appearances of MS and neuromyelitis optica spectrum disorder (NMOSD) brain lesions on χ-separation maps and explore their diagnostic value in differentiating the two diseases in comparison with previously reported diagnostic criteria. Materials and Methods This prospective study included individuals with MS or NMOSD who underwent χ-separation imaging from October 2017 to October 2020. Positive (χpos) and negative (χneg) susceptibility were estimated separately by using local frequency shifts and calculating R2' (R2' = R2* - R2). R2 mapping was performed with a machine learning approach. For each lesion, presence of the central vein sign (CVS) and paramagnetic rim sign (PRS) and signal characteristics on χneg and χpos maps were assessed and compared. For each participant, the proportion of lesions with CVS, PRS, and hypodiamagnetism was calculated. Diagnostic performances were assessed using receiver operating characteristic (ROC) curve analysis. Results A total of 32 participants with MS (mean age, 34 years ± 10 [SD]; 25 women, seven men) and 15 with NMOSD (mean age, 52 years ± 17; 14 women, one man) were evaluated, with a total of 611 MS and 225 NMOSD brain lesions. On the χneg maps, 80.2% (490 of 611) of MS lesions were categorized as hypodiamagnetic versus 13.8% (31 of 225) of NMOSD lesions (P < .001). Lesion appearances on the χpos maps showed no evidence of a difference between the two diseases. In per-participant analysis, participants with MS showed a higher proportion of hypodiamagnetic lesions (83%; IQR, 72-93) than those with NMOSD (6%; IQR, 0-14; P < .001). The proportion of hypodiamagnetic lesions achieved excellent diagnostic performance (area under the ROC curve, 0.96; 95% CI: 0.91, 1.00). Conclusion On χ-separation maps, multiple sclerosis (MS) lesions tend to be hypodiamagnetic, which can serve as an important hallmark to differentiate MS from neuromyelitis optica spectrum disorder. © RSNA, 2022 Supplemental material is available for this article.
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Affiliation(s)
- Woojun Kim
- From the Departments of Neurology (W.K.) and Radiology (H.L., D.P., J.J.), Seoul St Mary's Hospital, College of Medicine, The Catholic University of Korea, Banpo-daero 222, Seocho-gu, Seoul 06591, Republic of Korea; Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea (H.G.S., J.L.); Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, Md (H.G.S.); F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Md (H.G.S.); and Division of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin, Republic of Korea (J.K., Y.N.)
| | - Hyeong-Geol Shin
- From the Departments of Neurology (W.K.) and Radiology (H.L., D.P., J.J.), Seoul St Mary's Hospital, College of Medicine, The Catholic University of Korea, Banpo-daero 222, Seocho-gu, Seoul 06591, Republic of Korea; Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea (H.G.S., J.L.); Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, Md (H.G.S.); F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Md (H.G.S.); and Division of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin, Republic of Korea (J.K., Y.N.)
| | - Hyebin Lee
- From the Departments of Neurology (W.K.) and Radiology (H.L., D.P., J.J.), Seoul St Mary's Hospital, College of Medicine, The Catholic University of Korea, Banpo-daero 222, Seocho-gu, Seoul 06591, Republic of Korea; Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea (H.G.S., J.L.); Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, Md (H.G.S.); F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Md (H.G.S.); and Division of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin, Republic of Korea (J.K., Y.N.)
| | - Dohoon Park
- From the Departments of Neurology (W.K.) and Radiology (H.L., D.P., J.J.), Seoul St Mary's Hospital, College of Medicine, The Catholic University of Korea, Banpo-daero 222, Seocho-gu, Seoul 06591, Republic of Korea; Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea (H.G.S., J.L.); Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, Md (H.G.S.); F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Md (H.G.S.); and Division of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin, Republic of Korea (J.K., Y.N.)
| | - Junghwa Kang
- From the Departments of Neurology (W.K.) and Radiology (H.L., D.P., J.J.), Seoul St Mary's Hospital, College of Medicine, The Catholic University of Korea, Banpo-daero 222, Seocho-gu, Seoul 06591, Republic of Korea; Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea (H.G.S., J.L.); Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, Md (H.G.S.); F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Md (H.G.S.); and Division of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin, Republic of Korea (J.K., Y.N.)
| | - Yoonho Nam
- From the Departments of Neurology (W.K.) and Radiology (H.L., D.P., J.J.), Seoul St Mary's Hospital, College of Medicine, The Catholic University of Korea, Banpo-daero 222, Seocho-gu, Seoul 06591, Republic of Korea; Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea (H.G.S., J.L.); Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, Md (H.G.S.); F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Md (H.G.S.); and Division of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin, Republic of Korea (J.K., Y.N.)
| | - Jongho Lee
- From the Departments of Neurology (W.K.) and Radiology (H.L., D.P., J.J.), Seoul St Mary's Hospital, College of Medicine, The Catholic University of Korea, Banpo-daero 222, Seocho-gu, Seoul 06591, Republic of Korea; Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea (H.G.S., J.L.); Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, Md (H.G.S.); F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Md (H.G.S.); and Division of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin, Republic of Korea (J.K., Y.N.)
| | - Jinhee Jang
- From the Departments of Neurology (W.K.) and Radiology (H.L., D.P., J.J.), Seoul St Mary's Hospital, College of Medicine, The Catholic University of Korea, Banpo-daero 222, Seocho-gu, Seoul 06591, Republic of Korea; Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea (H.G.S., J.L.); Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, Md (H.G.S.); F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Md (H.G.S.); and Division of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin, Republic of Korea (J.K., Y.N.)
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25
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Kolb H, Al-Louzi O, Beck ES, Sati P, Absinta M, Reich DS. From pathology to MRI and back: Clinically relevant biomarkers of multiple sclerosis lesions. Neuroimage Clin 2022; 36:103194. [PMID: 36170753 PMCID: PMC9668624 DOI: 10.1016/j.nicl.2022.103194] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 09/07/2022] [Accepted: 09/09/2022] [Indexed: 12/14/2022]
Abstract
Focal lesions in both white and gray matter are characteristic of multiple sclerosis (MS). Histopathological studies have helped define the main underlying pathological processes involved in lesion formation and evolution, serving as a gold standard for many years. However, histopathology suffers from an intrinsic bias resulting from over-reliance on tissue samples from late stages of the disease or atypical cases and is inadequate for routine patient assessment. Pathological-radiological correlative studies have established advanced MRI's sensitivity to several relevant MS-pathological substrates and its practicality for assessing dynamic changes and following lesions over time. This review focuses on novel imaging techniques that serve as biomarkers of critical pathological substrates of MS lesions: the central vein, chronic inflammation, remyelination and repair, and cortical lesions. For each pathological process, we address the correlative value of MRI to MS pathology, its contribution in elucidating MS pathology in vivo, and the clinical utility of the imaging biomarker.
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Affiliation(s)
- Hadar Kolb
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health (NIH), Bethesda, MD, USA,Department of Neurology, Tel Aviv Sourasky Medical Center, Tel Aviv-Yaffo, Israel,Corresponding author at: Department of Neurology, Tel Aviv Sourasky Medical Center, Tel Aviv-Yaffo, Israel.
| | - Omar Al-Louzi
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health (NIH), Bethesda, MD, USA,Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Erin S. Beck
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health (NIH), Bethesda, MD, USA,Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Pascal Sati
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health (NIH), Bethesda, MD, USA,Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Martina Absinta
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health (NIH), Bethesda, MD, USA,Institute of Experimental Neurology (INSPE), IRCSS San Raffaele Hospital and Vita-Salute San Raffaele University, Milan, Italy,Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Daniel S. Reich
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health (NIH), Bethesda, MD, USA
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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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27
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Huang W, Sweeney EM, Kaunzner UW, Wang Y, Gauthier SA, Nguyen TD. Quantitative susceptibility mapping versus phase imaging to identify multiple sclerosis iron rim lesions with demyelination. J Neuroimaging 2022; 32:667-675. [PMID: 35262241 PMCID: PMC9308704 DOI: 10.1111/jon.12987] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 02/23/2022] [Accepted: 02/24/2022] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND AND PURPOSE To compare quantitative susceptibility mapping (QSM) and high-pass-filtered (HPF) phase imaging for (1) identifying chronic active rim lesions with more myelin damage and (2) distinguishing patients with increased clinical disability in multiple sclerosis. METHODS Eighty patients were scanned with QSM for paramagnetic rim detection and Fast Acquisition with Spiral Trajectory and T2prep for myelin water fraction (MWF). Chronic lesions were classified based on the presence/absence of rim on HPF and QSM images. A lesion-level linear mixed-effects model with MWF as the outcome was used to compare myelin damage among the lesion groups. A multiple patient-level linear regression model was fit to establish the association between Expanded Disease Status Scale (EDSS) and the log of the number of rim lesions. RESULTS Of 2062 lesions, 188 (9.1%) were HPF rim+/QSM rim+, 203 (9.8%) were HPF rim+/QSM rim-, and the remainder had no rim. In the linear mixed-effects model, HPF rim+/QSM rim+ lesions had significantly lower MWF than both HPF rim+/QSM rim- (p < .001) and HPF rim-/QSM rim- (p < .001) lesions, while the MWF difference between HPF rim+/QSM rim- and HPF rim-/QSM rim- lesions was not statistically significant (p = .130). Holding all other factors constant, the log number of QSM rim+ lesion was associated with EDSS increase (p = .044). The association between the log number of HPF rim+ lesions and EDSS was not statistically significant (p = .206). CONCLUSIONS QSM identifies paramagnetic rim lesions that on average have more myelin damage and stronger association with clinical disability than those detected by phase imaging.
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Affiliation(s)
- Weiyuan Huang
- Department of Radiotherapy, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, China.,Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Elizabeth M Sweeney
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE) Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ulrike W Kaunzner
- Department of Neurology, Weill Cornell Medicine, New York, New York, USA
| | - Yi Wang
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA.,Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, USA
| | - Susan A Gauthier
- Department of Neurology, Weill Cornell Medicine, New York, New York, USA
| | - Thanh D Nguyen
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
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28
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Rahmanzadeh R, Galbusera R, Lu PJ, Bahn E, Weigel M, Barakovic M, Franz J, Nguyen TD, Spincemaille P, Schiavi S, Daducci A, La Rosa F, Absinta M, Sati P, Cuadra MB, Radue EW, Leppert D, Kuhle J, Kappos L, Brück W, Reich DS, Stadelmann C, Wang Y, Granziera C. A new advanced MRI biomarker for remyelinated lesions in Multiple Sclerosis. Ann Neurol 2022; 92:486-502. [PMID: 35713309 PMCID: PMC9527017 DOI: 10.1002/ana.26441] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 06/12/2022] [Accepted: 06/14/2022] [Indexed: 11/28/2022]
Abstract
Objectives Neuropathological studies have shown that multiple sclerosis (MS) lesions are heterogeneous in terms of myelin/axon damage and repair as well as iron content. However, it remains a challenge to identify specific chronic lesion types, especially remyelinated lesions, in vivo in patients with MS. Methods We performed 3 studies: (1) a cross‐sectional study in a prospective cohort of 115 patients with MS and 76 healthy controls, who underwent 3 T magnetic resonance imaging (MRI) for quantitative susceptibility mapping (QSM), myelin water fraction (MWF), and neurite density index (NDI) maps. White matter (WM) lesions in QSM were classified into 5 QSM lesion types (iso‐intense, hypo‐intense, hyperintense, lesions with hypo‐intense rims, and lesions with paramagnetic rim legions [PRLs]); (2) a longitudinal study of 40 patients with MS to study the evolution of lesions over 2 years; (3) a postmortem histopathology‐QSM validation study in 3 brains of patients with MS to assess the accuracy of QSM classification to identify neuropathological lesion types in 63 WM lesions. Results At baseline, hypo‐ and isointense lesions showed higher mean MWF and NDI values compared to other QSM lesion types (p < 0.0001). Further, at 2‐year follow‐up, hypo‐/iso‐intense lesions showed an increase in MWF. Postmortem analyses revealed that QSM highly accurately identifies (1) fully remyelinated areas as hypo‐/iso‐intense (sensitivity = 88.89% and specificity = 100%), (2) chronic inactive lesions as hyperintense (sensitivity = 71.43% and specificity = 92.00%), and (3) chronic active/smoldering lesions as PRLs (sensitivity = 92.86% and specificity = 86.36%). Interpretation These results provide the first evidence that it is possible to distinguish chronic MS lesions in a clinical setting, hereby supporting with new biomarkers to develop and assess remyelinating treatments. ANN NEUROL 2022;92:486–502
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Affiliation(s)
- Reza Rahmanzadeh
- Neurology 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.,Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Riccardo Galbusera
- Neurology 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.,Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Po-Jui Lu
- Neurology 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.,Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Erik Bahn
- Institute of Neuropathology, University Medical Center, Göttingen, Germany
| | - Matthias Weigel
- Neurology 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.,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
- Neurology 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.,Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Jonas Franz
- Institute of Neuropathology, University Medical Center, Göttingen, Germany.,Max Planck Institute for Experimental Medicine, Göttingen, Germany.,Campus Institute for Dynamics of Biological Networks, University of Göttingen, Göttingen, Germany
| | - Thanh D Nguyen
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Pascal Spincemaille
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Simona Schiavi
- Department of Computer Science, University of Verona, Verona, Italy
| | | | - Francesco La Rosa
- Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.,Medical Image Analysis Laboratory (MIAL), Center for Biomedical Imaging (CIBM), Lausanne, Switzerland
| | - Martina Absinta
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.,Institute of Experimental Neurology, Division of Neuroscience, Vita-Salute San Raffaele University and Hospital, Milan, Italy
| | - Pascal Sati
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, NIH, 10 Center Drive MSC 1400, Building 10 Room 5C103, Bethesda, Maryland, USA.,Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA
| | - Meritxell Bach Cuadra
- Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.,Medical Image Analysis Laboratory (MIAL), Center for Biomedical Imaging (CIBM), Lausanne, Switzerland
| | - Ernst-Wilhelm Radue
- Neurology 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
| | - David Leppert
- Neurology Clinic and Policlinic, Departments 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
| | - Jens Kuhle
- Neurology Clinic and Policlinic, Departments 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
- Neurology Clinic and Policlinic, Departments 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
| | - Wolfgang Brück
- Institute of Neuropathology, University Medical Center, Göttingen, Germany
| | - Daniel S Reich
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, NIH, 10 Center Drive MSC 1400, Building 10 Room 5C103, Bethesda, Maryland, USA
| | | | - Yi Wang
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Cristina Granziera
- Neurology 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.,Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
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29
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Wang C, Martins-Bach AB, Alfaro-Almagro F, Douaud G, Klein JC, Llera A, Fiscone C, Bowtell R, Elliott LT, Smith SM, Tendler BC, Miller KL. Phenotypic and genetic associations of quantitative magnetic susceptibility in UK Biobank brain imaging. Nat Neurosci 2022; 25:818-831. [PMID: 35606419 PMCID: PMC9174052 DOI: 10.1038/s41593-022-01074-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 04/11/2022] [Indexed: 12/17/2022]
Abstract
A key aim in epidemiological neuroscience is identification of markers to assess brain health and monitor therapeutic interventions. Quantitative susceptibility mapping (QSM) is an emerging magnetic resonance imaging technique that measures tissue magnetic susceptibility and has been shown to detect pathological changes in tissue iron, myelin and calcification. We present an open resource of QSM-based imaging measures of multiple brain structures in 35,273 individuals from the UK Biobank prospective epidemiological study. We identify statistically significant associations of 251 phenotypes with magnetic susceptibility that include body iron, disease, diet and alcohol consumption. Genome-wide associations relate magnetic susceptibility to 76 replicating clusters of genetic variants with biological functions involving iron, calcium, myelin and extracellular matrix. These patterns of associations include relationships that are unique to QSM, in particular being complementary to T2* signal decay time measures. These new imaging phenotypes are being integrated into the core UK Biobank measures provided to researchers worldwide, creating the potential to discover new, non-invasive markers of brain health.
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Affiliation(s)
- Chaoyue Wang
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
| | - Aurea B Martins-Bach
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Fidel Alfaro-Almagro
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Gwenaëlle Douaud
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Johannes C Klein
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
- Oxford Parkinson's Disease Centre, University of Oxford, Oxford, UK
| | - Alberto Llera
- Donders Institute for Brain, Cognition and Behaviour, Centre for Cognitive Neuroimaging, Nijmegen, the Netherlands
| | - Cristiana Fiscone
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Richard Bowtell
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Lloyd T Elliott
- Department of Statistics and Actuarial Science, Simon Fraser University, Vancouver, British Columbia, Canada
| | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Benjamin C Tendler
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Karla L Miller
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
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30
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Sibgatulin R, Güllmar D, Deistung A, Enzinger C, Ropele S, Reichenbach JR. Magnetic susceptibility anisotropy in normal appearing white matter in multiple sclerosis from single-orientation acquisition. Neuroimage Clin 2022; 35:103059. [PMID: 35661471 PMCID: PMC9163587 DOI: 10.1016/j.nicl.2022.103059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 05/02/2022] [Accepted: 05/21/2022] [Indexed: 11/19/2022]
Abstract
Quantitative susceptibility mapping (QSM) has been successfully applied to study changes in deep grey matter nuclei as well as in lesional tissue, but its application to white matter has been complicated by the observed orientation dependence of gradient echo signal. The anisotropic susceptibility tensor is thought to be at the origin of this orientation dependence, and magnetic susceptibility anisotropy (MSA) derived from this tensor has been proposed as a marker of the state and integrity of the myelin sheath and may therefore be of particular interest for the study of demyelinating pathologies such as multiple sclerosis (MS). Reconstruction of the susceptibility tensor, however, requires repeated measurements with multiple head orientations, rendering the approach impractical for clinical applications. In this study, we combined single-orientation QSM with fibre orientation information to assess apparent MSA in three white matter tracts, i.e., optic radiation (OR), splenium of the corpus callosum (SCC), and superior longitudinal fascicle (SLF), in two cohorts of 64 healthy controls and 89 MS patients. The apparent MSA showed a significant decrease in optic radiation in the MS cohort compared with healthy controls. It decreased in the MS cohort with increasing lesion load in OR and with disease duration in the splenium. All of this suggests demyelination in normal appearing white matter. However, the apparent MSA observed in the SLF pointed to potential systematic issues that require further exploration to realize the full potential of the presented approach. Despite the limitations of such single-orientation ROI-specific estimation, we believe that our clinically feasible approach to study degenerative changes in WM is worthy of further investigation.
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Affiliation(s)
- Renat Sibgatulin
- Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital - Friedrich Schiller University Jena, Philosophenweg 3, 07743 Jena, Germany
| | - Daniel Güllmar
- Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital - Friedrich Schiller University Jena, Philosophenweg 3, 07743 Jena, Germany
| | - Andreas Deistung
- University Clinic and Outpatient Clinic for Radiology, Department for Radiation Medicine, University Hospital Halle (Saale), Ernst-Grube-Str. 40, 06120 Halle (Saale), Germany
| | - Christian Enzinger
- Department of Neurology, Medical University of Graz, Auenbruggerplatz 22, 8036 Graz, Austria
| | - Stefan Ropele
- Department of Neurology, Medical University of Graz, Auenbruggerplatz 22, 8036 Graz, Austria
| | - Jürgen R Reichenbach
- Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital - Friedrich Schiller University Jena, Philosophenweg 3, 07743 Jena, Germany; Michael Stifel Center Jena for Data-Driven and Simulation Science, Friedrich-Schiller-University Jena, Jena, Germany
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31
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Oh G, Bae H, Ahn HS, Park SH, Moon WJ, Ye JC. Unsupervised Resolution-Agnostic Quantitative Susceptibility Mapping using Adaptive Instance Normalization. Med Image Anal 2022; 79:102477. [DOI: 10.1016/j.media.2022.102477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 05/04/2022] [Accepted: 05/06/2022] [Indexed: 11/30/2022]
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32
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Coffman CH, White R, Subramanian K, Buch S, Bernitsas E, Haacke EM. Quantitative susceptibility mapping of both ring and non-ring white matter lesions in relapsing remitting multiple sclerosis. Magn Reson Imaging 2022; 91:45-51. [DOI: 10.1016/j.mri.2022.05.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 05/12/2022] [Accepted: 05/16/2022] [Indexed: 11/25/2022]
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33
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Jung W, Bollmann S, Lee J. Overview of quantitative susceptibility mapping using deep learning: Current status, challenges and opportunities. NMR IN BIOMEDICINE 2022; 35:e4292. [PMID: 32207195 DOI: 10.1002/nbm.4292] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Revised: 02/04/2020] [Accepted: 02/25/2020] [Indexed: 06/10/2023]
Abstract
Quantitative susceptibility mapping (QSM) has gained broad interest in the field by extracting bulk tissue magnetic susceptibility, predominantly determined by myelin, iron and calcium from magnetic resonance imaging (MRI) phase measurements in vivo. Thereby, QSM can reveal pathological changes of these key components in a variety of diseases. QSM requires multiple processing steps such as phase unwrapping, background field removal and field-to-source inversion. Current state-of-the-art techniques utilize iterative optimization procedures to solve the inversion and background field correction, which are computationally expensive and require a careful choice of regularization parameters. With the recent success of deep learning using convolutional neural networks for solving ill-posed reconstruction problems, the QSM community also adapted these techniques and demonstrated that the QSM processing steps can be solved by efficient feed forward multiplications not requiring either iterative optimization or the choice of regularization parameters. Here, we review the current status of deep learning-based approaches for processing QSM, highlighting limitations and potential pitfalls, and discuss the future directions the field may take to exploit the latest advances in deep learning for QSM.
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Affiliation(s)
- Woojin Jung
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, South Korea
| | - Steffen Bollmann
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia
- Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia
| | - Jongho Lee
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, South Korea
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34
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Caruana G, Auger C, Pessini LM, Calderon W, de Barros A, Salerno A, Sastre-Garriga J, Montalban X, Rovira À. SWI as an Alternative to Contrast-Enhanced Imaging to Detect Acute MS Lesions. AJNR Am J Neuroradiol 2022; 43:534-539. [PMID: 35332015 PMCID: PMC8993188 DOI: 10.3174/ajnr.a7474] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 02/04/2022] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Acute inflammatory activity of MS lesions is traditionally assessed through contrast-enhanced T1-weighted MR images. The aim of our study was to determine whether a qualitative evaluation of non-contrast-enhanced SWI of new T2-hyperintense lesions might help distinguish acute and chronic lesions and whether it could be considered a possible alternative to gadolinium-based contrast agents for this purpose. MATERIALS AND METHODS Serial MR imaging studies from 55 patients with MS were reviewed to identify 169 new T2-hyperintense lesions. Two blinded neuroradiologists determined their signal pattern on SWI, considering 5 categories (hypointense rings, marked hypointensity, mild hypointensity, iso-/hyperintensity, indeterminate). Two different blinded neuroradiologists evaluated the presence or absence of enhancement in postcontrast T1-weighted images of the lesions. The Fisher exact test was used to determine whether each category of signal intensity on SWI was associated with gadolinium enhancement. RESULTS The presence of hypointense rings or marked hypointensity showed a strong association with the absence of gadolinium enhancement (P < .001), with a sensitivity of 93.0% and a specificity of 82.9%. The presence of mild hypointensity or isohyperintensity showed a strong association with the presence of gadolinium enhancement (P < .001), with a sensitivity of 68.3% and a specificity of 99.2%. CONCLUSIONS A qualitative analysis of the signal pattern on SWI of new T2-hyperintense MS lesions allows determining the likelihood that the lesions will enhance after administration of a gadolinium contrast agent, with high specificity albeit with a moderate sensitivity. While it cannot substitute for the use of contrast agent, it can be useful in some clinical settings in which the contrast agent cannot be administered.
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Affiliation(s)
- G Caruana
- From the Neuroradiology Section (G.C., C.A., L.M.P., W.C., A.d.B., A.S., À.R.)
| | - C Auger
- From the Neuroradiology Section (G.C., C.A., L.M.P., W.C., A.d.B., A.S., À.R.)
| | - L M Pessini
- From the Neuroradiology Section (G.C., C.A., L.M.P., W.C., A.d.B., A.S., À.R.)
| | - W Calderon
- From the Neuroradiology Section (G.C., C.A., L.M.P., W.C., A.d.B., A.S., À.R.)
| | - A de Barros
- From the Neuroradiology Section (G.C., C.A., L.M.P., W.C., A.d.B., A.S., À.R.)
| | - A Salerno
- From the Neuroradiology Section (G.C., C.A., L.M.P., W.C., A.d.B., A.S., À.R.)
| | - J Sastre-Garriga
- Department of Radiology, and Servei de Neurologia-Neuroimmunologia (J.S.-G., X.M.). Centre d'Esclerosi Múltiple de Catalunya, Institut de Recerca Vall d'Hebron, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - X Montalban
- Department of Radiology, and Servei de Neurologia-Neuroimmunologia (J.S.-G., X.M.). Centre d'Esclerosi Múltiple de Catalunya, Institut de Recerca Vall d'Hebron, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - À Rovira
- From the Neuroradiology Section (G.C., C.A., L.M.P., W.C., A.d.B., A.S., À.R.)
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Disease correlates of rim lesions on quantitative susceptibility mapping in multiple sclerosis. Sci Rep 2022; 12:4411. [PMID: 35292734 PMCID: PMC8924224 DOI: 10.1038/s41598-022-08477-6] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 03/08/2022] [Indexed: 12/26/2022] Open
Abstract
Quantitative susceptibility mapping (QSM), an imaging technique sensitive to brain iron, has been used to detect paramagnetic rims of iron-laden active microglia and macrophages in a subset of multiple sclerosis (MS) lesions, known as rim+ lesions, that are consistent with chronic active lesions. Because of the potential impact of rim+ lesions on disease progression and tissue damage, investigating their influence on disability and neurodegeneration is critical to establish the impact of these lesions on the disease course. This study aimed to explore the relationship between chronic active rim+ lesions, identified as having a hyperintense rim on QSM, and both clinical disability and imaging measures of neurodegeneration in patients with MS. The patient cohort was composed of 159 relapsing-remitting multiple sclerosis patients. The Expanded Disability Status Scale (EDSS) and Brief International Cognitive Assessment for Multiple Sclerosis, which includes both the Symbol Digit Modalities Test and California Verbal Learning Test-II, were used to assess clinical disability. Cortical thickness and thalamic volume were evaluated as imaging measures of neurodegeneration. A total of 4469 MS lesions were identified, of which 171 QSM rim+ (3.8%) lesions were identified among 57 patients (35.8%). In a multivariate regression model, as the overall total lesion burden increased, patients with at least one rim+ lesion on QSM performed worse on both physical disability and cognitive assessments, specifically the Symbol Digit Modalities Test (p = 0.010), California Verbal Learning Test-II (p = 0.030), and EDSS (p = 0.001). In a separate univariate regression model, controlling for age (p < 0.001) and having at least one rim+ lesion was related to more cortical thinning (p = 0.03) in younger patients (< 45 years). Lower thalamic volume was associated with older patients (p = 0.038) and larger total lesion burden (p < 0.001); however, the association did not remain significant with rim+ lesions (p = 0.10). Our findings demonstrate a novel observation that chronic active lesions, as identified on QSM, modify the impact of lesion burden on clinical disability in MS patients. These results support further exploration of rim+ lesions for therapeutic targeting in MS to reduce disability and subsequent neurodegeneration.
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Zinger N, Ponath G, Sweeney E, Nguyen TD, Lo CH, Diaz I, Dimov A, Teng L, Zexter L, Comunale J, Wang Y, Pitt D, Gauthier SA. Dimethyl Fumarate Reduces Inflammation in Chronic Active Multiple Sclerosis Lesions. NEUROLOGY(R) NEUROIMMUNOLOGY & NEUROINFLAMMATION 2022; 9:9/2/e1138. [PMID: 35046083 PMCID: PMC8771666 DOI: 10.1212/nxi.0000000000001138] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Accepted: 12/10/2021] [Indexed: 12/14/2022]
Abstract
Background and Objectives To determine the effects of dimethyl fumarate (DMF) and glatiramer acetate on iron content in chronic active lesions in patients with multiple sclerosis (MS) and in human microglia in vitro. Methods This was a retrospective observational study of 34 patients with relapsing-remitting MS and clinically isolated syndrome treated with DMF or glatiramer acetate. Patients had lesions with hyperintense rims on quantitative susceptibility mapping, were treated with DMF or glatiramer acetate (GA), and had a minimum of 2 on-treatment scans. Changes in susceptibility in rim lesions were compared among treatment groups in a linear mixed effects model. In a separate in vitro study, induced pluripotent stem cell–derived human microglia were treated with DMF or GA, and treatment-induced changes in iron content and activation state of microglia were compared. Results Rim lesions in patients treated with DMF had on average a 2.77-unit reduction in susceptibility per year over rim lesions in patients treated with GA (bootstrapped 95% CI −5.87 to −0.01), holding all other variables constant. Moreover, DMF but not GA reduced inflammatory activation and concomitantly iron content in human microglia in vitro. Discussion Together, our data indicate that DMF-induced reduction of susceptibility in MS lesions is associated with a decreased activation state in microglial cells. We have demonstrated that a specific disease modifying therapy, DMF, decreases glial activity in chronic active lesions. Susceptibility changes in rim lesions provide an in vivo biomarker for the effect of DMF on microglial activity. Classification of Evidence This study provided Class III evidence that DMF is superior to GA in the presence of iron as a marker of inflammation as measured by MRI quantitative susceptibility mapping.
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Affiliation(s)
- Nicole Zinger
- From the Department of Neurology (N.Z., L.Z., S.A.G.), Weill Cornell Medicine, New York; Department of Neurology (G.P., C.H.L., D.P.), Yale School of Medicine, New Haven, CT; Department of Population Health Sciences (E.S., I.D.), and Department of Radiology (T.D.N., A.D., J.C., Y.W., S.A.G.), Weil Cornell Medicine, New York; Department of Medicine (L.T.), Yale New Haven Hospital, New Haven, CT; Feil Family Brain and Mind Institute (S.A.G.), Weill Cornell Medicine, New York; and Lee Kong Chian School of Medicine (C.H.L.), Nanyang Technological University, Singapore
| | - Gerald Ponath
- From the Department of Neurology (N.Z., L.Z., S.A.G.), Weill Cornell Medicine, New York; Department of Neurology (G.P., C.H.L., D.P.), Yale School of Medicine, New Haven, CT; Department of Population Health Sciences (E.S., I.D.), and Department of Radiology (T.D.N., A.D., J.C., Y.W., S.A.G.), Weil Cornell Medicine, New York; Department of Medicine (L.T.), Yale New Haven Hospital, New Haven, CT; Feil Family Brain and Mind Institute (S.A.G.), Weill Cornell Medicine, New York; and Lee Kong Chian School of Medicine (C.H.L.), Nanyang Technological University, Singapore
| | - Elizabeth Sweeney
- From the Department of Neurology (N.Z., L.Z., S.A.G.), Weill Cornell Medicine, New York; Department of Neurology (G.P., C.H.L., D.P.), Yale School of Medicine, New Haven, CT; Department of Population Health Sciences (E.S., I.D.), and Department of Radiology (T.D.N., A.D., J.C., Y.W., S.A.G.), Weil Cornell Medicine, New York; Department of Medicine (L.T.), Yale New Haven Hospital, New Haven, CT; Feil Family Brain and Mind Institute (S.A.G.), Weill Cornell Medicine, New York; and Lee Kong Chian School of Medicine (C.H.L.), Nanyang Technological University, Singapore
| | - Thanh D Nguyen
- From the Department of Neurology (N.Z., L.Z., S.A.G.), Weill Cornell Medicine, New York; Department of Neurology (G.P., C.H.L., D.P.), Yale School of Medicine, New Haven, CT; Department of Population Health Sciences (E.S., I.D.), and Department of Radiology (T.D.N., A.D., J.C., Y.W., S.A.G.), Weil Cornell Medicine, New York; Department of Medicine (L.T.), Yale New Haven Hospital, New Haven, CT; Feil Family Brain and Mind Institute (S.A.G.), Weill Cornell Medicine, New York; and Lee Kong Chian School of Medicine (C.H.L.), Nanyang Technological University, Singapore
| | - Chih Hung Lo
- From the Department of Neurology (N.Z., L.Z., S.A.G.), Weill Cornell Medicine, New York; Department of Neurology (G.P., C.H.L., D.P.), Yale School of Medicine, New Haven, CT; Department of Population Health Sciences (E.S., I.D.), and Department of Radiology (T.D.N., A.D., J.C., Y.W., S.A.G.), Weil Cornell Medicine, New York; Department of Medicine (L.T.), Yale New Haven Hospital, New Haven, CT; Feil Family Brain and Mind Institute (S.A.G.), Weill Cornell Medicine, New York; and Lee Kong Chian School of Medicine (C.H.L.), Nanyang Technological University, Singapore
| | - Ivan Diaz
- From the Department of Neurology (N.Z., L.Z., S.A.G.), Weill Cornell Medicine, New York; Department of Neurology (G.P., C.H.L., D.P.), Yale School of Medicine, New Haven, CT; Department of Population Health Sciences (E.S., I.D.), and Department of Radiology (T.D.N., A.D., J.C., Y.W., S.A.G.), Weil Cornell Medicine, New York; Department of Medicine (L.T.), Yale New Haven Hospital, New Haven, CT; Feil Family Brain and Mind Institute (S.A.G.), Weill Cornell Medicine, New York; and Lee Kong Chian School of Medicine (C.H.L.), Nanyang Technological University, Singapore
| | - Alexey Dimov
- From the Department of Neurology (N.Z., L.Z., S.A.G.), Weill Cornell Medicine, New York; Department of Neurology (G.P., C.H.L., D.P.), Yale School of Medicine, New Haven, CT; Department of Population Health Sciences (E.S., I.D.), and Department of Radiology (T.D.N., A.D., J.C., Y.W., S.A.G.), Weil Cornell Medicine, New York; Department of Medicine (L.T.), Yale New Haven Hospital, New Haven, CT; Feil Family Brain and Mind Institute (S.A.G.), Weill Cornell Medicine, New York; and Lee Kong Chian School of Medicine (C.H.L.), Nanyang Technological University, Singapore
| | - Leilei Teng
- From the Department of Neurology (N.Z., L.Z., S.A.G.), Weill Cornell Medicine, New York; Department of Neurology (G.P., C.H.L., D.P.), Yale School of Medicine, New Haven, CT; Department of Population Health Sciences (E.S., I.D.), and Department of Radiology (T.D.N., A.D., J.C., Y.W., S.A.G.), Weil Cornell Medicine, New York; Department of Medicine (L.T.), Yale New Haven Hospital, New Haven, CT; Feil Family Brain and Mind Institute (S.A.G.), Weill Cornell Medicine, New York; and Lee Kong Chian School of Medicine (C.H.L.), Nanyang Technological University, Singapore
| | - Lily Zexter
- From the Department of Neurology (N.Z., L.Z., S.A.G.), Weill Cornell Medicine, New York; Department of Neurology (G.P., C.H.L., D.P.), Yale School of Medicine, New Haven, CT; Department of Population Health Sciences (E.S., I.D.), and Department of Radiology (T.D.N., A.D., J.C., Y.W., S.A.G.), Weil Cornell Medicine, New York; Department of Medicine (L.T.), Yale New Haven Hospital, New Haven, CT; Feil Family Brain and Mind Institute (S.A.G.), Weill Cornell Medicine, New York; and Lee Kong Chian School of Medicine (C.H.L.), Nanyang Technological University, Singapore
| | - Joseph Comunale
- From the Department of Neurology (N.Z., L.Z., S.A.G.), Weill Cornell Medicine, New York; Department of Neurology (G.P., C.H.L., D.P.), Yale School of Medicine, New Haven, CT; Department of Population Health Sciences (E.S., I.D.), and Department of Radiology (T.D.N., A.D., J.C., Y.W., S.A.G.), Weil Cornell Medicine, New York; Department of Medicine (L.T.), Yale New Haven Hospital, New Haven, CT; Feil Family Brain and Mind Institute (S.A.G.), Weill Cornell Medicine, New York; and Lee Kong Chian School of Medicine (C.H.L.), Nanyang Technological University, Singapore
| | - Yi Wang
- From the Department of Neurology (N.Z., L.Z., S.A.G.), Weill Cornell Medicine, New York; Department of Neurology (G.P., C.H.L., D.P.), Yale School of Medicine, New Haven, CT; Department of Population Health Sciences (E.S., I.D.), and Department of Radiology (T.D.N., A.D., J.C., Y.W., S.A.G.), Weil Cornell Medicine, New York; Department of Medicine (L.T.), Yale New Haven Hospital, New Haven, CT; Feil Family Brain and Mind Institute (S.A.G.), Weill Cornell Medicine, New York; and Lee Kong Chian School of Medicine (C.H.L.), Nanyang Technological University, Singapore
| | - David Pitt
- From the Department of Neurology (N.Z., L.Z., S.A.G.), Weill Cornell Medicine, New York; Department of Neurology (G.P., C.H.L., D.P.), Yale School of Medicine, New Haven, CT; Department of Population Health Sciences (E.S., I.D.), and Department of Radiology (T.D.N., A.D., J.C., Y.W., S.A.G.), Weil Cornell Medicine, New York; Department of Medicine (L.T.), Yale New Haven Hospital, New Haven, CT; Feil Family Brain and Mind Institute (S.A.G.), Weill Cornell Medicine, New York; and Lee Kong Chian School of Medicine (C.H.L.), Nanyang Technological University, Singapore
| | - Susan A Gauthier
- From the Department of Neurology (N.Z., L.Z., S.A.G.), Weill Cornell Medicine, New York; Department of Neurology (G.P., C.H.L., D.P.), Yale School of Medicine, New Haven, CT; Department of Population Health Sciences (E.S., I.D.), and Department of Radiology (T.D.N., A.D., J.C., Y.W., S.A.G.), Weil Cornell Medicine, New York; Department of Medicine (L.T.), Yale New Haven Hospital, New Haven, CT; Feil Family Brain and Mind Institute (S.A.G.), Weill Cornell Medicine, New York; and Lee Kong Chian School of Medicine (C.H.L.), Nanyang Technological University, Singapore.
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He J, Wang L, Cao Y, Wang R, Zhu Y. Learn Less, Infer More: Learning in the Fourier Domain for Quantitative Susceptibility Mapping. Front Neurosci 2022; 16:837721. [PMID: 35250469 PMCID: PMC8888664 DOI: 10.3389/fnins.2022.837721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 01/17/2022] [Indexed: 12/02/2022] Open
Abstract
Quantitative susceptibility mapping (QSM) aims to evaluate the distribution of magnetic susceptibility from magnetic resonance phase measurements by solving the ill-conditioned dipole inversion problem. Removing the artifacts and preserving the anisotropy of tissue susceptibility simultaneously is still a challenge in QSM. To deal with this issue, a novel k-QSM network is proposed to resolve dipole inversion issues in QSM reconstruction. The k-QSM network converts the results obtained by truncated k-space division (TKD) into the Fourier domain as inputs. After passing through several convolutional and residual blocks, the ill-posed signals of TKD are corrected by making the network output close to the calculation of susceptibility through multiple orientation sampling (COSMOS)-labeled QSM. To evaluate the superiority of k-QSM, comparisons with several state-of-the-art methods are performed in terms of QSM artifacts removing, anisotropy preserving, generalization ability, and clinical applications. Compared to existing methods, the k-QSM achieves a 22.31% lower normalized root mean square error, 10.30% higher peak signal-to-noise ratio (PSNR), 33.10% lower high-frequency error norm, and 1.06% higher structural similarity. In addition, the orientation-dependent susceptibility variation obtained by k-QSM is significant, verifying that k-QSM has the ability to preserve susceptibility anisotropy. When the trained models are tested on the dataset from different centers, our k-QSM shows a strong generalization ability with the highest PSNR. Moreover, by comparing the susceptibility maps between healthy controls and drug addicts with different methods, we found the proposed k-QSM is more sensitive to the susceptibility abnormality in the patients. The proposed k-QSM method learns less—only to fix the ill-posed signals of TKD, but infers more—both COSMOS-like and anisotropy-preserving QSM results. Its generalization ability and great sensitivity to susceptibility changes can make it a potential method for distinguishing some diseases.
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Affiliation(s)
- Junjie He
- Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, College of Computer Science and Technology, Guizhou University, Guiyang, China
- International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment, Department of Radiology, Guizhou Provincial People's Hospital, Guiyang, China
| | - Lihui Wang
- Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, College of Computer Science and Technology, Guizhou University, Guiyang, China
- *Correspondence: Lihui Wang
| | - Ying Cao
- Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, College of Computer Science and Technology, Guizhou University, Guiyang, China
| | - Rongpin Wang
- International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment, Department of Radiology, Guizhou Provincial People's Hospital, Guiyang, China
- Rongpin Wang
| | - Yuemin Zhu
- CREATIS, IRP Metislab, University of Lyon, INSA Lyon, CNRS UMR 5220, Inserm U1294, Lyon, France
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Longitudinal Observation of Asymmetric Iron Deposition in an Intracerebral Hemorrhage Model Using Quantitative Susceptibility Mapping. Symmetry (Basel) 2022. [DOI: 10.3390/sym14020350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
Quantitative susceptibility mapping (QSM) is used to obtain quantitative magnetic susceptibility maps of materials from magnitude and phase images acquired by three-dimensional gradient-echo using inverse problem-solving. Few preclinical studies have evaluated the intracerebral hemorrhage (ICH) model and asymmetric iron deposition. We created a rat model of ICH and compared QSM and conventional magnetic resonance imaging (MRI) during the longitudinal evaluation of ICH. Collagenase was injected in the right striatum of 12-week-old Wistar rats. QSM and conventional MRI were performed on days 0, 1, 7, and 28 after surgery using 7-Tesla MRI. Susceptibility, normalized signal value, and area of the hemorrhage site were statistically compared during image analysis. Susceptibility decreased monotonically up to day 7 but increased on day 28. Other imaging methods showed a significant increase in signal from day 0 to day 1 but a decreasing trend after day 1. During the area evaluation, conventional MRI methods showed an increase from day 0 to day 1; however, decreases were observed thereafter. QSM showed a significant increase from day 0 to day 1. The temporal evaluation of ICH by QSM suggested the possibility of detecting of asymmetric iron deposition for normal brain site.
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Cho J, Nguyen TD, Huang W, Sweeney EM, Luo X, Kovanlikaya I, Zhang S, Gillen KM, Spincemaille P, Gupta A, Gauthier SA, Wang Y. Brain oxygen extraction fraction mapping in patients with multiple sclerosis. J Cereb Blood Flow Metab 2022; 42:338-348. [PMID: 34558996 PMCID: PMC9122515 DOI: 10.1177/0271678x211048031] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
We aimed to demonstrate the feasibility of whole brain oxygen extraction fraction (OEF) mapping for measuring lesion specific and regional OEF abnormalities in multiple sclerosis (MS) patients. In 22 MS patients and 11 healthy controls (HC), OEF and neural tissue susceptibility (χn) maps were computed from MRI multi-echo gradient echo data. In MS patients, 80 chronic active lesions with hyperintense rim on quantitative susceptibility mapping were identified, and the mean OEF and χn within the rim and core were compared using linear mixed-effect model analysis. The rim showed higher OEF and χn than the core: relative to their adjacent normal appearing white matter, OEF contrast = -6.6 ± 7.0% vs. -9.8 ± 7.8% (p < 0.001) and χn contrast = 33.9 ± 20.3 ppb vs. 25.7 ± 20.5 ppb (p = 0.017). Between MS and HC, OEF and χn were compared using a linear regression model in subject-based regions of interest. In the whole brain, compared to HC, MS had lower OEF, 30.4 ± 3.3% vs. 21.4 ± 4.4% (p < 0.001), and higher χn, -23.7 ± 7.0 ppb vs. -11.3 ± 7.7 ppb (p = 0.018). Our feasibility study suggests that OEF may serve as a useful quantitative marker of tissue oxygen utilization in MS.
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Affiliation(s)
- Junghun Cho
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Thanh D Nguyen
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Weiyuan Huang
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Elizabeth M Sweeney
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Xianfu Luo
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | | | - Shun Zhang
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Kelly M Gillen
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | | | - Ajay Gupta
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Susan A Gauthier
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA.,Department of Neurology, Weill Cornell Medicine, New York, NY, USA
| | - Yi Wang
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA.,Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA
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Liao H, Chen Q, Zhang M, Chen W. MRI features and evolution of autoimmune glial fibrillary acidic protein astrocytopathy: A retrospective cross-sectional and longitudinal study. Mult Scler Relat Disord 2022; 58:103512. [DOI: 10.1016/j.msard.2022.103512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Revised: 11/23/2021] [Accepted: 01/08/2022] [Indexed: 11/16/2022]
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Magnetic susceptibility in the deep gray matter may be modulated by apolipoprotein E4 and age with regional predilections: a quantitative susceptibility mapping study. Neuroradiology 2022; 64:1331-1342. [PMID: 34981175 DOI: 10.1007/s00234-021-02859-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 11/09/2021] [Indexed: 10/19/2022]
Abstract
PURPOSE To examine the relationship between apolipoprotein E gene (APOE) mutation status and iron accumulation in the deep gray matter of subjects with cognitive symptoms using quantitative susceptibility mapping (QSM). METHODS A total of 105 patients with cognitive symptoms were enrolled. QSM data were generated from 3D gradient-echo data using an STI Suite algorithm. A region of interest-based analysis with QSM was performed in the deep gray matter. Differences between APOE4 carriers and non-carriers were assessed by analysis of covariance. Multiple regression analysis was performed to identify the factors associated with magnetic susceptibility. RESULTS Clinical characters such as age, education, MMSE, vascular risk burden, and systolic blood pressure differ between APOE4 carrier and non-carrier groups. The APOE4 carrier group had higher magnetic susceptibility values than the non-carrier group, with significant differences in the caudate (p = 0.004), putamen (p < 0.0001), and globus pallidus (p < 0.0001) which imply higher iron accumulation. In a multiple regression analysis, APOE4 status was found to be a predictor of magnetic susceptibility value in the globus pallidus (p = 0.03); age for magnetic susceptibility value in the caudate nucleus (p = 0.0064); and age and hippocampal atrophy for magnetic susceptibility value in the putamen (p < 0.05). CONCLUSION Our study demonstrates that magnetic susceptibility in globus pallidus is related to APOE4 status while those of caudate and putamen are related to other factors including age. It suggests that brain iron accumulation in the deep gray matter is modulated by APOE4 and age with differential regional predilection.
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Alsharidah MM, Uzair M, Alseneidi SS, Alkharan AA, Bunyan RF, Bashir S. The Role of Transcranial Magnetic Stimulation as a Surrogate Marker of Disease Activity in Patients with Multiple Sclerosis: A Literature Review. INNOVATIONS IN CLINICAL NEUROSCIENCE 2022; 19:8-14. [PMID: 35382066 PMCID: PMC8970240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
OBJECTIVE Multiple sclerosis (MS) is a chronic, immune-mediated inflammatory disease of the central nervous system (CNS) characterized by demyelination, axonal degeneration, and cognitive impairment. It also has an important impact on the quality of life of patients and their family members. We sought to determine if transcranial magnetic stimulation (TMS) is a valid instrument for determining disease progression activity in patients with MS. METHODS A literature search of the PubMed database was conducted using the terms "multiple sclerosis," "transcranial magnetic stimulation," and "neurophysiological parameters." RESULTS Neurophysiological parameters, such as sensitivity to demyelination and the strength of excitatory and inhibitory synaptic interactions in the cerebral cortex, can be identified through TMS in patients affected by MS. These objective parameters can be correlated with the progression of disease and provide reliable indices for the severity of illness and the efficacy of drugs used to treat MS in clinical trials. CONCLUSION The discovery of specific and detailed neurophysiological parameters as surrogate endpoints for disease activity could represent an important step in clinical trials. Changes in cortical connectivity have already been demonstrated in MS, but in clinical practice, other measures are typically used to evaluate disease activity. We speculate that TMS might be more effective in identifying disease progression that leads to long-term disability, compared to standard surrogate markers, since it represents a direct measure of synaptic transmission(s) in MS.
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Affiliation(s)
- Muhannad M Alsharidah
- Drs. Alsharidah and Alseneidi are with the College of Medicine, King Saud University in Riyadh, Saudi Arabia
- Mr. Uzair is with the Department of Biological Sciences, Faculty of Basic and Applied Sciences, International Islamic University in Islamabad, Pakistan
- Dr. Alkharan is with the College of Medicine, Princess Nourah Bint Abdulrahman University in Riyadh, Saudi Arabia
- Drs. Bunyan and Bashir are with the Neuroscience Center, King Fahad Specialist Hospital in Dammam, Saudi Arabia
| | - Mohammad Uzair
- Drs. Alsharidah and Alseneidi are with the College of Medicine, King Saud University in Riyadh, Saudi Arabia
- Mr. Uzair is with the Department of Biological Sciences, Faculty of Basic and Applied Sciences, International Islamic University in Islamabad, Pakistan
- Dr. Alkharan is with the College of Medicine, Princess Nourah Bint Abdulrahman University in Riyadh, Saudi Arabia
- Drs. Bunyan and Bashir are with the Neuroscience Center, King Fahad Specialist Hospital in Dammam, Saudi Arabia
| | - Sarah S Alseneidi
- Drs. Alsharidah and Alseneidi are with the College of Medicine, King Saud University in Riyadh, Saudi Arabia
- Mr. Uzair is with the Department of Biological Sciences, Faculty of Basic and Applied Sciences, International Islamic University in Islamabad, Pakistan
- Dr. Alkharan is with the College of Medicine, Princess Nourah Bint Abdulrahman University in Riyadh, Saudi Arabia
- Drs. Bunyan and Bashir are with the Neuroscience Center, King Fahad Specialist Hospital in Dammam, Saudi Arabia
| | - Afnan A Alkharan
- Drs. Alsharidah and Alseneidi are with the College of Medicine, King Saud University in Riyadh, Saudi Arabia
- Mr. Uzair is with the Department of Biological Sciences, Faculty of Basic and Applied Sciences, International Islamic University in Islamabad, Pakistan
- Dr. Alkharan is with the College of Medicine, Princess Nourah Bint Abdulrahman University in Riyadh, Saudi Arabia
- Drs. Bunyan and Bashir are with the Neuroscience Center, King Fahad Specialist Hospital in Dammam, Saudi Arabia
| | - Reem Fahd Bunyan
- Drs. Alsharidah and Alseneidi are with the College of Medicine, King Saud University in Riyadh, Saudi Arabia
- Mr. Uzair is with the Department of Biological Sciences, Faculty of Basic and Applied Sciences, International Islamic University in Islamabad, Pakistan
- Dr. Alkharan is with the College of Medicine, Princess Nourah Bint Abdulrahman University in Riyadh, Saudi Arabia
- Drs. Bunyan and Bashir are with the Neuroscience Center, King Fahad Specialist Hospital in Dammam, Saudi Arabia
| | - Shahid Bashir
- Drs. Alsharidah and Alseneidi are with the College of Medicine, King Saud University in Riyadh, Saudi Arabia
- Mr. Uzair is with the Department of Biological Sciences, Faculty of Basic and Applied Sciences, International Islamic University in Islamabad, Pakistan
- Dr. Alkharan is with the College of Medicine, Princess Nourah Bint Abdulrahman University in Riyadh, Saudi Arabia
- Drs. Bunyan and Bashir are with the Neuroscience Center, King Fahad Specialist Hospital in Dammam, Saudi Arabia
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Matrosova MS, Bryukhov VV, Belskaya GN, Krotenkova MV. [Quantitative susceptibility mapping in assessment of inflammation and neurodegeneration in multiple sclerosis]. Zh Nevrol Psikhiatr Im S S Korsakova 2022; 122:16-22. [PMID: 36537626 DOI: 10.17116/jnevro202212212116] [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: 06/17/2023]
Abstract
Quantitative susceptibility mapping (QSM) is a relatively new MRI technique that may potentially help estimate iron concentrations in the brain. It plays a big role in diagnosis of many pathological processes, including multiple sclerosis (MS). Iron metabolism in the brain is a complex and not fully understood process. It is known that the content of iron in the brain increases with age; in addition, its accumulation is often observed in many neurodegenerative diseases, including MS foci, and its amount changes over time. In this regard, the values of magnetic susceptibility obtained using QSM can potentially become a convenient biomarker that reflects the latent activity and progression of MS, which, in turn, can influence the choice of therapy and the tactics of treating patients.
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Stewart AW, Robinson SD, O'Brien K, Jin J, Widhalm G, Hangel G, Walls A, Goodwin J, Eckstein K, Tourell M, Morgan C, Narayanan A, Barth M, Bollmann S. QSMxT: Robust masking and artifact reduction for quantitative susceptibility mapping. Magn Reson Med 2021; 87:1289-1300. [PMID: 34687073 DOI: 10.1002/mrm.29048] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 08/30/2021] [Accepted: 09/27/2021] [Indexed: 01/15/2023]
Abstract
PURPOSE Quantitative susceptibility mapping (QSM) estimates the spatial distribution of tissue magnetic susceptibilities from the phase of a gradient-echo signal. QSM algorithms require a signal mask to delineate regions with reliable phase for subsequent susceptibility estimation. Existing masking techniques used in QSM have limitations that introduce artifacts, exclude anatomical detail, and rely on parameter tuning and anatomical priors that narrow their application. Here, a robust masking and reconstruction procedure is presented to overcome these limitations and enable automated QSM processing. Moreover, this method is integrated within an open-source software framework: QSMxT. METHODS A robust masking technique that automatically separates reliable from less reliable phase regions was developed and combined with a two-pass reconstruction procedure that operates on the separated sources before combination, extracting more information and suppressing streaking artifacts. RESULTS Compared with standard masking and reconstruction procedures, the two-pass inversion reduces streaking artifacts caused by unreliable phase and high dynamic ranges of susceptibility sources. It is also robust across a range of acquisitions at 3 T in volunteers and phantoms, at 7 T in tumor patients, and in an in silico head phantom, with significant artifact and error reductions, greater anatomical detail, and minimal parameter tuning. CONCLUSION The two-pass masking and reconstruction procedure separates reliable from less reliable phase regions, enabling a more accurate QSM reconstruction that mitigates artifacts, operates without anatomical priors, and requires minimal parameter tuning. The technique and its integration within QSMxT makes QSM processing more accessible and robust to streaking artifacts.
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Affiliation(s)
- Ashley Wilton Stewart
- ARC Training Centre for Innovation in Biomedical Imaging Technology, University of Queensland, Brisbane, Queensland, Australia.,Centre for Advanced Imaging, University of Queensland, Brisbane, Australia
| | - Simon Daniel Robinson
- Centre for Advanced Imaging, University of Queensland, Brisbane, Australia.,Department of Neurology, Medical University of Graz, Graz, Austria.,Karl Landsteiner Institute for Clinical Molecular MR in Musculoskeletal Imaging, Vienna, Austria.,Department of Biomedical Imaging and Image-Guided Therapy, High Field MR Center, Medical University of Vienna, Vienna, Austria
| | - Kieran O'Brien
- ARC Training Centre for Innovation in Biomedical Imaging Technology, University of Queensland, Brisbane, Queensland, Australia.,Centre for Advanced Imaging, University of Queensland, Brisbane, Australia.,Siemens Healthcare Pty Ltd, Brisbane, Queensland, Australia
| | - Jin Jin
- ARC Training Centre for Innovation in Biomedical Imaging Technology, University of Queensland, Brisbane, Queensland, Australia.,Centre for Advanced Imaging, University of Queensland, Brisbane, Australia.,Siemens Healthcare Pty Ltd, Brisbane, Queensland, Australia
| | - Georg Widhalm
- Department of Neurosurgery, Medical University of Vienna, Vienna, Austria
| | - Gilbert Hangel
- Department of Biomedical Imaging and Image-Guided Therapy, High Field MR Center, Medical University of Vienna, Vienna, Austria.,Department of Neurosurgery, Medical University of Vienna, Vienna, Austria
| | - Angela Walls
- Clinical & Research Imaging Centre, South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia
| | - Jonathan Goodwin
- Department of Radiation Oncology, Calvary Mater Hospital, Newcastle, New South Wales, Australia.,School of Mathematical and Physical Science, University of Newcastle, Newcastle, New South Wales, Australia
| | - Korbinian Eckstein
- Department of Biomedical Imaging and Image-Guided Therapy, High Field MR Center, Medical University of Vienna, Vienna, Austria
| | - Monique Tourell
- ARC Training Centre for Innovation in Biomedical Imaging Technology, University of Queensland, Brisbane, Queensland, Australia.,Centre for Advanced Imaging, University of Queensland, Brisbane, Australia
| | - Catherine Morgan
- School of Psychology and Centre for Brain Research, The University of Auckland, Auckland, New Zealand.,Centre of Research Excellence, Brain Research New Zealand-Rangahau Roro Aotearoa, Auckland, New Zealand.,Centre for Advanced MRI, The University of Auckland, Auckland, New Zealand
| | - Aswin Narayanan
- Centre for Advanced Imaging, University of Queensland, Brisbane, Australia
| | - Markus Barth
- ARC Training Centre for Innovation in Biomedical Imaging Technology, University of Queensland, Brisbane, Queensland, Australia.,Centre for Advanced Imaging, University of Queensland, Brisbane, Australia.,School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Queensland, Australia
| | - Steffen Bollmann
- ARC Training Centre for Innovation in Biomedical Imaging Technology, University of Queensland, Brisbane, Queensland, Australia.,Centre for Advanced Imaging, University of Queensland, Brisbane, Australia.,School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Queensland, Australia
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Tozlu C, Jamison K, Nguyen T, Zinger N, Kaunzner U, Pandya S, Wang Y, Gauthier S, Kuceyeski A. Structural disconnectivity from paramagnetic rim lesions is related to disability in multiple sclerosis. Brain Behav 2021; 11:e2353. [PMID: 34498432 PMCID: PMC8553317 DOI: 10.1002/brb3.2353] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 07/28/2021] [Accepted: 08/19/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND In people with multiple sclerosis (pwMS), lesions with a hyperintense rim (rim+) on Quantitative Susceptibility Mapping (QSM) have been shown to have greater myelin damage compared to rim- lesions, but their association with disability has not yet been investigated. Furthermore, how QSM rim+ and rim- lesions differentially impact disability through their disruptions to structural connectivity has not been explored. We test the hypothesis that structural disconnectivity due to rim+ lesions is more predictive of disability compared to structural disconnectivity due to rim- lesions. METHODS Ninety-six pwMS were included in our study. Individuals with Expanded Disability Status Scale (EDSS) <2 were considered to have lower disability (n = 59). For each gray matter region, a Change in Connectivity (ChaCo) score, that is, the percent of connecting streamlines also passing through a rim- or rim+ lesion, was computed. Adaptive Boosting was used to classify the pwMS into lower versus greater disability groups based on ChaCo scores from rim+ and rim- lesions. Classification performance was assessed using the area under ROC curve (AUC). RESULTS The model based on ChaCo from rim+ lesions outperformed the model based on ChaCo from rim- lesions (AUC = 0.67 vs 0.63, p-value < .05). The left thalamus and left cerebellum were the most important regions in classifying pwMS into disability categories. CONCLUSION rim+ lesions may be more influential on disability through their disruptions to the structural connectome than rim- lesions. This study provides a deeper understanding of how rim+ lesion location/size and resulting disruption to the structural connectome can contribute to MS-related disability.
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Affiliation(s)
- Ceren Tozlu
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Keith Jamison
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Thanh Nguyen
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Nicole Zinger
- Department of Neurology, Weill Cornell Medicine, New York, New York, USA
| | - Ulrike Kaunzner
- Department of Neurology, Weill Cornell Medicine, New York, New York, USA
| | - Sneha Pandya
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Yi Wang
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Susan Gauthier
- Department of Neurology, Weill Cornell Medicine, New York, New York, USA
| | - Amy Kuceyeski
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA.,Brain and Mind Research Institute, Weill Cornell Medicine, New York, New York, USA
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46
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Pinto C, Cambron M, Dobai A, Vanheule E, Casselman JW. Smoldering lesions in MS: if you like it then you should put a rim on it. Neuroradiology 2021; 64:703-714. [PMID: 34498108 DOI: 10.1007/s00234-021-02800-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 08/30/2021] [Indexed: 11/30/2022]
Abstract
PURPOSE In multiple sclerosis (MS), chronic active/smoldering white matter lesions presenting with hypointense rims on susceptibility-weighted imaging (SWI) of the brain have been recognized as an important radiological feature. The aim of this work was to study the prevalence of paramagnetic rim lesions (RLs) in MS patients in a clinical setting and to assess differences in demographic and clinical variables regarding the presence of RLs. METHODS All 3 T brain magnetic resonance (MR) studies performed in MS patients between July 2020 and January 2021 were reviewed. In all patients, RLs were assessed on three-dimensional (3D) SWI images and the T2 FLAIR lesion load volume was assessed. Demographic, laboratory (oligoclonal bands in CSF), and clinical data, including functional status with Expanded Disability Status Scale (EDSS), were retrieved from the clinical files. RESULTS Of the 192 patients, 113 (59%) presented with at least 1 RL. In the RL-positive group, the mean RL count was 4.81 ranging from 1 to 37. There was no significant difference in the number of RLs between the different types of MS (p = 0.858). Regarding the presence of RLs, there were no significant differences based on gender (p = 0.083), disease duration (p = 0.520), treatment regime (p = 0.326), EDSS score (p = 0.103), and the associated T2 FLAIR lesion load volume. CONCLUSION SWI RLs were frequently detected in our cohort regardless of the MS type, T2 FLAIR lesion load volume, demographic features, disease duration, or clinical score. Our results suggest that RLs are not associated with more severe forms of the disease. Today, RLs can be seen on 3 T 3D SWI, although this is not a clinical standard sequence yet. Therefore, it should be considered an additional helpful MR sequence in the diagnostic workup of MS, although more studies are warranted to establish the role of RLs as prognostic markers.
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Affiliation(s)
- Catarina Pinto
- Neuroradiology Department, Centro Hospitalar Universitário Do Porto, Porto, Portugal
- Department of Radiology, AZ St. Jan Brugge-Oostende av, Campus Brugge, Ruddershove 10, B-8000, Brugge, Belgium
| | - Melissa Cambron
- Department of Neurology, AZ St. Jan Brugge-Oostende av, Campus Brugge, Ruddershove 10, B-8000, Brugge, Belgium
| | - Adrienn Dobai
- Department of Oral Diagnostics, Faculty of Dentistry, Semmelweis University, Szentkirályi u. 47, Budapest, 1088, Hungary
- Department of Neuroradiology, Medical Imaging Centre, Semmelweis University, Balassa street 6, Budapest, 1083, Hungary
| | - Eva Vanheule
- Department of Radiology, AZ St. Jan Brugge-Oostende av, Campus Brugge, Ruddershove 10, B-8000, Brugge, Belgium
- Department of Radiology, UZ-Gent, Gent, Belgium
| | - Jan W Casselman
- Department of Radiology, AZ St. Jan Brugge-Oostende av, Campus Brugge, Ruddershove 10, B-8000, Brugge, Belgium.
- University Ghent, Gent, Belgium.
- Department of Radiology, AZ St. Augustinus, Oosterveldlaan 24, B-2610, Antwerpen, Belgium.
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47
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Emmerich J, Bachert P, Ladd ME, Straub S. On the separation of susceptibility sources in quantitative susceptibility mapping: Theory and phantom validation with an in vivo application to multiple sclerosis lesions of different age. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2021; 330:107033. [PMID: 34303117 DOI: 10.1016/j.jmr.2021.107033] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 06/14/2021] [Accepted: 07/08/2021] [Indexed: 06/13/2023]
Abstract
PURPOSE In biological tissue, phase contrast is determined by multiple substances such as iron, myelin or calcifications. Often, these substances occur co-located within the same measurement volume. However, quantitative susceptibility mapping can solely measure the average susceptibility per voxel. To provide new insight in disease progression and mechanisms in neurological diseases, where multiple processes such as demyelination and iron accumulation occur simultaneously in the same location, a separation of susceptibility sources is desirable to disentangle the underlying susceptibility proportions. METHODS The basic concept of separating the susceptibility effects from sources with different sign within one voxel is to include information on relaxation rate ΔR2∗ in the quantitative susceptibility mapping reconstruction pipeline. The presented reconstruction algorithm is implemented as a constrained minimization problem and solved using conjugate gradients. The algorithm is evaluated using a software phantom and validated in MRI measurements with a phantom containing mixtures of microscopic positive and negative susceptibility sources. Data from three multiple sclerosis patients are used to show in vivo feasibility. RESULTS In numerical simulations, the feasibility of disentangling susceptibility sources within the same voxel was confirmed provided the critera of the static dephasing regime were fulfilled. In phantom experiments, the magnitude decay kernel, which is an essential reconstruction parameter of the algorithm, was determined to be Dm=194.5T-1s-1ppm-1, and susceptibility sources could be separated in MRI measurement data. CONCLUSIONS In conclusion, in this study a detailed description of the implementation of an algorithm for the separation of positive and negative susceptibility sources within the same volume element as well as its limitations is presented and validated quantitatively in both simulation and phantom experiments for the first time. An application to multiple sclerosis lesions shows promising results for in vivo usability.
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Affiliation(s)
- Julian Emmerich
- Division of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Physics and Astronomy, Heidelberg University, Heidelberg, Germany
| | - Peter Bachert
- Division of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Physics and Astronomy, Heidelberg University, Heidelberg, Germany
| | - Mark E Ladd
- Division of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Physics and Astronomy, Heidelberg University, Heidelberg, Germany; Faculty of Medicine, Heidelberg University, Heidelberg, Germany
| | - Sina Straub
- Division of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
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48
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Lo CH, Skarica M, Mansoor M, Bhandarkar S, Toro S, Pitt D. Astrocyte Heterogeneity in Multiple Sclerosis: Current Understanding and Technical Challenges. Front Cell Neurosci 2021; 15:726479. [PMID: 34456686 PMCID: PMC8385194 DOI: 10.3389/fncel.2021.726479] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Accepted: 07/15/2021] [Indexed: 11/16/2022] Open
Abstract
The emergence of single cell technologies provides the opportunity to characterize complex immune/central nervous system cell assemblies in multiple sclerosis (MS) and to study their cell population structures, network activation and dynamics at unprecedented depths. In this review, we summarize the current knowledge of astrocyte subpopulations in MS tissue and discuss the challenges associated with resolving astrocyte heterogeneity with single-nucleus RNA-sequencing (snRNA-seq). We further discuss multiplexed imaging techniques as tools for defining population clusters within a spatial context. Finally, we will provide an outlook on how these technologies may aid in answering unresolved questions in MS, such as the glial phenotypes that drive MS progression and/or neuropathological differences between different clinical MS subtypes.
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Affiliation(s)
- Chih Hung Lo
- Department of Neurology, Yale School of Medicine, New Haven, CT, United States
| | - Mario Skarica
- Department of Neuroscience, Yale School of Medicine, New Haven, CT, United States
| | - Mohammad Mansoor
- Department of Neurology, Yale School of Medicine, New Haven, CT, United States
| | - Shaan Bhandarkar
- Department of Neurology, Yale School of Medicine, New Haven, CT, United States
| | - Steven Toro
- Department of Neurology, Yale School of Medicine, New Haven, CT, United States
| | - David Pitt
- Department of Neurology, Yale School of Medicine, New Haven, CT, United States
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A proposal: How to study pro-myelinating proteins in MS. Autoimmun Rev 2021; 21:102924. [PMID: 34416371 DOI: 10.1016/j.autrev.2021.102924] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 08/14/2021] [Indexed: 12/15/2022]
Abstract
Multiple sclerosis (MS) is an inflammatory and degenerative disease of the CNS. An unmet need in MS is repair i.e.,promoting endogenous regeneration and remyelination after demyelinating inflammatory injury. Remyelination is critical in neuronal preservation and the prevention of clinical progression. There is a good deal of evidence for histological repair and remyelination in MS patients. Repair is driven by several prominent endogenous pro-myelinating proteinsincluding neural cellular adhesion molecule (N-CAM) and brain derived neurotrophic factor (BDNF) among others. To follow changes during acute re-myelination in vivo in MS subjects, non conventional MRI techniques are necessary such as quantitative susceptibility mapping (QSM) that detects the release of Fe from dying oligodendroglial cells and myelin water imaging (MWI) that detects water captured within newly formed myelin. The best time to monitor changes in pro-myelinating proteins and link those changes to imaging evolution is immediately after the acute inflammatory response in MS lesions (gadolinium enhancement [Gd+]) during an intense period of remyelination. We can monitor MS subjects with new Gd + lesions with periodic imaging along with sampling of blood and CSF and determine if myelin formation is linked with increases in pro-myelinating proteins. This would lead to potential therapeutic manipulation with directly administered proteins to promote CNS re-myelination in animal models and in early clinical trials.
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50
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Chen J, Gong NJ, Chaim KT, Otaduy MCG, Liu C. Decompose quantitative susceptibility mapping (QSM) to sub-voxel diamagnetic and paramagnetic components based on gradient-echo MRI data. Neuroimage 2021; 242:118477. [PMID: 34403742 PMCID: PMC8720043 DOI: 10.1016/j.neuroimage.2021.118477] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 08/13/2021] [Indexed: 12/31/2022] Open
Abstract
PURPOSE A method named DECOMPOSE-QSM is developed to decompose bulk susceptibility measured with QSM into sub-voxel paramagnetic and diamagnetic components based on a three-pool complex signal model. METHODS Multi-echo gradient echo signal is modeled as a summation of three weighted exponentials corresponding to three types of susceptibility sources: reference susceptibility, diamagnetic and paramagnetic susceptibility relative to the reference. Paramagnetic component susceptibility (PCS) and diamagnetic component susceptibility (DCS) maps are constructed to represent the sub-voxel compartments by solving for linear and nonlinear parameters in the model. RESULTS Numerical forward simulation and phantom validation confirmed the ability of DECOMPOSE-QSM to separate the mixture of paramagnetic and diamagnetic components. The PCS obtained from temperature-variant brainstem imaging follows the Curie's Law, which further validated the model and the solver. Initial in vivo investigation of human brain images showed the ability to extract sub-voxel PCS and DCS sources that produce visually enhanced contrast between brain structures comparing to threshold QSM.
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Affiliation(s)
- Jingjia Chen
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, USA
| | - Nan-Jie Gong
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, USA; Vector Lab for Intelligent Medical Imaging and Neural Engineering, International Innovation Center of Tsinghua University, Shanghai, China
| | - Khallil Taverna Chaim
- LIM44, Instituto e Departamento de Radiologia, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, Brazil
| | | | - Chunlei Liu
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, USA; Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA.
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