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Jang M, Dimov AV, Kapse K, Murnick J, Grinspan Z, Wu A, RoyChoudhury A, Wang Y, Spincemaille P, Nguyen TD, Limperopoulos C, Zun Z. Quantitative Susceptibility Mapping with Source Separation in Normal Brain Development of Newborns. AJNR Am J Neuroradiol 2025; 46:380-389. [PMID: 39231612 DOI: 10.3174/ajnr.a8488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2024] [Accepted: 08/13/2024] [Indexed: 09/06/2024]
Abstract
BACKGROUND AND PURPOSE Quantitative susceptibility mapping is an emerging method for characterizing tissue composition and studying myelination and iron deposition. However, accurate assessment of myelin and iron content in the neonate brain using this method is challenging because these 2 susceptibility sources of opposite signs (myelin, negative; iron, positive) occupy the same voxel, with minimal and comparable content in both sources. In this study, susceptibilities were measured in the healthy neonate brain using susceptibility source separation. MATERIALS AND METHODS Sixty-nine healthy neonates without clinical indications were prospectively recruited for MRI. All neonates underwent gradient-echo imaging for quantitative susceptibility mapping. Positive (paramagnetic) and negative (diamagnetic) susceptibility sources were separated using additional information from R2* with linear modeling performed for the neonate brain. Average susceptibility maps were generated by normalizing all susceptibility maps to an atlas space. Mean regional susceptibility measurements were obtained in the cortical GM, WM, deep GM, caudate nucleus, putamen, globus pallidus, thalamus, and the 4 brain lobes. RESULTS A total of 65 healthy neonates (mean postmenstrual age, 42.8 [SD, 2.3] weeks; 34 females) were studied. The negative susceptibility maps visually demonstrated high signals in the thalamus, brainstem, and potentially myelinated WM regions, whereas the positive susceptibility maps depicted high signals in the GM compared with all WM regions, including both myelinated and unmyelinated WM. The WM exhibited significantly lower mean positive susceptibility and significantly higher mean negative susceptibility than cortical GM and deep GM. Within the deep GM, the thalamus showed a significantly lower mean negative susceptibility than the other nuclei, and the putamen and globus pallidus showed significant associations with neonate age in positive and/or negative susceptibility. Among the 4 brain lobes, the occipital lobe showed a significantly higher mean positive susceptibility and a significantly lower mean negative susceptibility than the frontal lobe. CONCLUSIONS This study demonstrates regional variations and temporal changes in positive and negative susceptibilities of the neonate brain, potentially associated with myelination and iron deposition patterns in normal brain development. It suggests that quantitative susceptibility mapping with source separation may be used for early identification of delayed myelination or iron deficiency.
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Affiliation(s)
- MinJung Jang
- From the Department of Radiology (M.J., A.V.D., Y.W., P.S., T.D.N., Z.Z.), Weill Cornell Medicine, New York, New York
| | - Alexey V Dimov
- From the Department of Radiology (M.J., A.V.D., Y.W., P.S., T.D.N., Z.Z.), Weill Cornell Medicine, New York, New York
| | - Kushal Kapse
- Institute for the Developing Brain (K.K., J.M., C.L.), Division of Diagnostic Imaging and Radiology, Children's National Hospital, Washington, DC
| | - Jonathan Murnick
- Institute for the Developing Brain (K.K., J.M., C.L.), Division of Diagnostic Imaging and Radiology, Children's National Hospital, Washington, DC
- Department of Pediatrics (J.M., C.L.), School of Medicine and Health Sciences, George Washington University, Washington, DC
- Department of Radiology, School of Medicine and Health Sciences (J.M., C.L.), George Washington University, Washington, DC
| | - Zachary Grinspan
- Department of Pediatrics (Z.G.), Weill Cornell Medicine, New York, New York
| | - Alan Wu
- Department of Population Health Sciences (A.W., A.R.), Weill Cornell Medicine, New York, New York
| | - Arindam RoyChoudhury
- Department of Population Health Sciences (A.W., A.R.), Weill Cornell Medicine, New York, New York
| | - Yi Wang
- From the Department of Radiology (M.J., A.V.D., Y.W., P.S., T.D.N., Z.Z.), Weill Cornell Medicine, New York, New York
| | - Pascal Spincemaille
- From the Department of Radiology (M.J., A.V.D., Y.W., P.S., T.D.N., Z.Z.), Weill Cornell Medicine, New York, New York
| | - Thanh D Nguyen
- From the Department of Radiology (M.J., A.V.D., Y.W., P.S., T.D.N., Z.Z.), Weill Cornell Medicine, New York, New York
| | - Catherine Limperopoulos
- Institute for the Developing Brain (K.K., J.M., C.L.), Division of Diagnostic Imaging and Radiology, Children's National Hospital, Washington, DC
- Department of Pediatrics (J.M., C.L.), School of Medicine and Health Sciences, George Washington University, Washington, DC
- Department of Radiology, School of Medicine and Health Sciences (J.M., C.L.), George Washington University, Washington, DC
- Division of Fetal and Transitional Medicine (C.L.), Children's National Hospital, Washington, DC
| | - Zungho Zun
- From the Department of Radiology (M.J., A.V.D., Y.W., P.S., T.D.N., Z.Z.), Weill Cornell Medicine, New York, New York
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Kim M, Ji S, Kim J, Min K, Jeong H, Youn J, Kim T, Jang J, Bilgic B, Shin H, Lee J. χ-sepnet: Deep Neural Network for Magnetic Susceptibility Source Separation. Hum Brain Mapp 2025; 46:e70136. [PMID: 39835664 PMCID: PMC11748151 DOI: 10.1002/hbm.70136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2024] [Revised: 12/11/2024] [Accepted: 12/30/2024] [Indexed: 01/22/2025] Open
Abstract
Magnetic susceptibility source separation (χ-separation), an advanced quantitative susceptibility mapping (QSM) method, enables the separate estimation of paramagnetic and diamagnetic susceptibility source distributions in the brain. Similar to QSM, it requires solving the ill-conditioned problem of dipole inversion, suffering from so-called streaking artifacts. Additionally, the method utilizes reversible transverse relaxation (R 2 ' = R 2 * - R 2 $$ {R}_2^{\prime }={R}_2^{\ast }-{R}_2 $$ ) to complement frequency shift information for estimating susceptibility source concentrations, requiring time-consuming data acquisition forR 2 $$ {R}_2 $$ (e.g., multi-echo spin-echo) in addition to multi-echo GRE data forR 2 * $$ {R}_2^{\ast } $$ . To address these challenges, we develop a new deep learning network, χ-sepnet, and propose two deep learning-based susceptibility source separation pipelines, χ-sepnet-R 2 ' $$ {R}_2^{\prime } $$ for inputs with multi-echo GRE and multi-echo spin-echo (or turbo spin-echo) and χ-sepnet-R 2 * $$ {R}_2^{\ast } $$ for input with multi-echo GRE only. The neural network is trained using multiple head orientation data that provide streaking artifact-free labels, generating high-quality χ-separation maps. The evaluation of the pipelines encompasses both qualitative and quantitative assessments in healthy subjects, and visual inspection of lesion characteristics in multiple sclerosis patients. The susceptibility source-separated maps of the proposed pipelines delineate detailed brain structures with substantially reduced artifacts compared to those from the conventional regularization-based reconstruction methods. In quantitative analysis, χ-sepnet-R 2 ' $$ {R}_2^{\prime } $$ achieves the best outcomes followed by χ-sepnet-R 2 * $$ {R}_2^{\ast } $$ , outperforming the conventional methods. When the lesions of multiple sclerosis patients are classified into subtypes, most lesions are identified as the same subtype in the maps from χ-sepnet-R 2 ' $$ {R}_2^{\prime } $$ and χ-sepnet-R 2 * $$ {R}_2^{\ast } $$ (paramagnetic susceptibility: 99.6% and diamagnetic susceptibility: 98.4%; both out of 250 lesions). The χ-sepnet-R 2 * $$ {R}_2^{\ast } $$ pipeline, which only requires multi-echo GRE data, has demonstrated its potential to offer broad clinical and scientific applications, although further evaluations for various diseases and pathological conditions are necessary.
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Affiliation(s)
- Minjun Kim
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer EngineeringSeoul National UniversitySeoulRepublic of Korea
| | - Sooyeon Ji
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer EngineeringSeoul National UniversitySeoulRepublic of Korea
- Division of Computer EngineeringHankuk University of Foreign StudiesYonginRepublic of Korea
| | - Jiye Kim
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer EngineeringSeoul National UniversitySeoulRepublic of Korea
| | - Kyeongseon Min
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer EngineeringSeoul National UniversitySeoulRepublic of Korea
| | - Hwihun Jeong
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer EngineeringSeoul National UniversitySeoulRepublic of Korea
| | - Jonghyo Youn
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer EngineeringSeoul National UniversitySeoulRepublic of Korea
| | - Taechang Kim
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer EngineeringSeoul National UniversitySeoulRepublic of Korea
| | - Jinhee Jang
- Department of RadiologySeoul St Mary's Hospital, College of Medicine, The Catholic University of KoreaSeoulRepublic of Korea
- Institute for Precision HealthUniversity of CaliforniaIrvineCaliforniaUSA
| | - Berkin Bilgic
- Massachusetts General Hospital and Harvard Medical SchoolBostonMassachusettsUSA
| | - Hyeong‐Geol Shin
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer EngineeringSeoul National UniversitySeoulRepublic of Korea
- F.M. Kirby Research Center for Functional Brain ImagingKennedy Krieger InstituteBaltimoreMarylandUSA
- Russell H. Morgan Department of Radiology and Radiological ScienceThe Johns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Jongho Lee
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer EngineeringSeoul National UniversitySeoulRepublic of Korea
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Kim J, Kim M, Ji S, Min K, Jeong H, Shin HG, Oh C, Fox RJ, Sakaie KE, Lowe MJ, Oh S, Straub S, Kim SG, Lee J. In-vivo high-resolution χ-separation at 7T. Neuroimage 2025:121060. [PMID: 39884410 DOI: 10.1016/j.neuroimage.2025.121060] [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: 10/21/2024] [Revised: 12/06/2024] [Accepted: 01/27/2025] [Indexed: 02/01/2025] Open
Abstract
A recently introduced quantitative susceptibility mapping (QSM) technique, χ-separation, offers the capability to separate paramagnetic (χpara) and diamagnetic (χdia) susceptibility distribution within the brain. In-vivo high-resolution mapping of iron and myelin distribution, estimated by χ-separation, could provide a deeper understanding of brain substructures, assisting the investigation of their functions and alterations. This can be achieved using 7T MRI, which benefits from a high signal-to-noise ratio and susceptibility effects. However, applying χ-separation at 7T presents difficulties due to the requirement of an R2 map, coupled with issues such as high specific absorption rate (SAR), large B1 transmit field inhomogeneities, and prolonged scan time. To address these challenges, we developed a novel deep neural network, R2PRIMEnet7T, designed to convert a 7T R2* map into a 3T R2' map. Building on this development, we present a new pipeline for χ-separation at 7T, enabling us to generate high-resolution χ-separation maps from multi-echo gradient-echo data. The proposed method is compared with alternative pipelines, such as an end-to-end network and linearly-scaled R2', and is validated against χ-separation maps at 3T, demonstrating its accuracy. The 7T χ-separation maps generated by the proposed method exhibit similar contrasts to those from 3T, while 7T high-resolution maps offer enhanced clarity and detail. Quantitative analysis confirms that the proposed method surpasses the alternative pipelines. The proposed method results well delineate the detailed brain structures associated with iron and myelin. This new pipeline holds promise for analyzing iron and myelin concentration changes in various neurodegenerative diseases through precise structural examination.
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Affiliation(s)
- Jiye Kim
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea
| | - Minjun Kim
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea
| | - Sooyeon Ji
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea; Division of Computer Engineering, Hankuk University of Foreign Studies, Yongin, Republic of Korea
| | - Kyeongseon Min
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea
| | - Hwihun Jeong
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea
| | - Hyeong-Geol Shin
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea; Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Chungseok Oh
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea
| | - Robert J Fox
- Mellen Center for Treatment and Research in MS, Cleveland Clinic, Cleveland, OH, USA
| | - Ken E Sakaie
- Imaging Sciences, Diagnostics Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Mark J Lowe
- Imaging Sciences, Diagnostics Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Sehong Oh
- Imaging Sciences, Diagnostics Institute, Cleveland Clinic, Cleveland, OH, USA; Department of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin, Republic of Korea
| | - Sina Straub
- Department of Radiology, Mayo Clinic, Jacksonville, FL, USA
| | - Seong-Gi Kim
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea; Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Republic of Korea
| | - Jongho Lee
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea.
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Hong G, Khazaee T, Cobos SF, Christiansen SD, Liu J, Drangova M, Holdsworth DW. Characterizing diffusion-controlled release of small-molecules using quantitative MRI in view of applications to orthopedic infection. NMR IN BIOMEDICINE 2024; 37:e5254. [PMID: 39358036 DOI: 10.1002/nbm.5254] [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: 05/06/2024] [Revised: 08/14/2024] [Accepted: 08/19/2024] [Indexed: 10/04/2024]
Abstract
Calcium sulfate is an established carrier for localized drug delivery, but a means to non-invasively measure drug release, which would improve our understanding of localized delivery, remains an unmet need. We aim to quantitatively estimate the diffusion-controlled release of small molecules loaded into a calcium sulfate carrier through a gadobutrol-based contrast agent, which acts as a surrogate small molecule. A central cylindrical core made of calcium sulfate, either alone or within a metal scaffold, is loaded with contrast agents that release into agar. Multi-echo scans are acquired at multiple time points over 4 weeks and processed into R2* and quantitative susceptibility mapping (QSM) maps. Mean R2* values are fit to a known drug delivery model, which are then compared with the decrease in core QSM. Fitting R2* measurements of calcium sulfate core while constraining constants to a drug release model results in an R2-value of 0.991, yielding a diffusion constant of 4.59 × 10-11 m2 s-1. Incorporating the carrier within a metal scaffold results in a slower release. QSM shows the resulting loss of susceptibility in the non-metal core but is unreliable around metal. R2* characterizes the released gadobutrol, and QSM detects the resulting decrease in core susceptibility. The addition of a porous metal scaffold slows the release of gadobutrol, as expected.
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Affiliation(s)
- Greg Hong
- Imaging Research Laboratories, Robarts Research Institute, Western University, London, Ontario, Canada
- Bone and Joint Institute, Western University, London, Ontario, Canada
- Department of Medical Biophysics, Schulich School of Medicine & Dentistry, Western University, London, Ontario, Canada
| | - Tina Khazaee
- Imaging Research Laboratories, Robarts Research Institute, Western University, London, Ontario, Canada
- Bone and Joint Institute, Western University, London, Ontario, Canada
- Department of Medical Biophysics, Schulich School of Medicine & Dentistry, Western University, London, Ontario, Canada
| | - Santiago F Cobos
- Imaging Research Laboratories, Robarts Research Institute, Western University, London, Ontario, Canada
- Bone and Joint Institute, Western University, London, Ontario, Canada
- Department of Medical Biophysics, Schulich School of Medicine & Dentistry, Western University, London, Ontario, Canada
| | - Spencer D Christiansen
- Imaging Research Laboratories, Robarts Research Institute, Western University, London, Ontario, Canada
- Department of Medical Biophysics, Schulich School of Medicine & Dentistry, Western University, London, Ontario, Canada
| | - Junmin Liu
- Imaging Research Laboratories, Robarts Research Institute, Western University, London, Ontario, Canada
| | - Maria Drangova
- Imaging Research Laboratories, Robarts Research Institute, Western University, London, Ontario, Canada
- Bone and Joint Institute, Western University, London, Ontario, Canada
- Department of Medical Biophysics, Schulich School of Medicine & Dentistry, Western University, London, Ontario, Canada
| | - David W Holdsworth
- Imaging Research Laboratories, Robarts Research Institute, Western University, London, Ontario, Canada
- Bone and Joint Institute, Western University, London, Ontario, Canada
- Department of Medical Biophysics, Schulich School of Medicine & Dentistry, Western University, London, Ontario, Canada
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Min K, Sohn B, Kim WJ, Park CJ, Song S, Shin DH, Chang KW, Shin NY, Kim M, Shin HG, Lee PH, Lee J. A human brain atlas of χ-separation for normative iron and myelin distributions. NMR IN BIOMEDICINE 2024; 37:e5226. [PMID: 39162295 DOI: 10.1002/nbm.5226] [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: 03/28/2024] [Revised: 06/29/2024] [Accepted: 07/05/2024] [Indexed: 08/21/2024]
Abstract
Iron and myelin are primary susceptibility sources in the human brain. These substances are essential for a healthy brain, and their abnormalities are often related to various neurological disorders. Recently, an advanced susceptibility mapping technique, which is referred to as χ-separation (pronounced as "chi"-separation), has been proposed, successfully disentangling paramagnetic iron from diamagnetic myelin. This method provided a new opportunity for generating high-resolution iron and myelin maps of the brain. Utilizing this technique, this study constructs a normative χ-separation atlas from 106 healthy human brains. The resulting atlas provides detailed anatomical structures associated with the distributions of iron and myelin, clearly delineating subcortical nuclei, thalamic nuclei, and white matter fiber bundles. Additionally, susceptibility values in a number of regions of interest are reported along with age-dependent changes. This atlas may have direct applications such as localization of subcortical structures for deep brain stimulation or high-intensity focused ultrasound and also serve as a valuable resource for future research.
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Affiliation(s)
- Kyeongseon Min
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea
| | - Beomseok Sohn
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Woo Jung Kim
- Institute of Behavioral Sciences in Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
- Department of Psychiatry, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Republic of Korea
| | - Chae Jung Park
- Department of Radiology, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Republic of Korea
| | | | | | - Kyung Won Chang
- Department of Neurosurgery, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Na-Young Shin
- Department of Radiology, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Minjun Kim
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea
| | - Hyeong-Geol Shin
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Phil Hyu Lee
- Department of Neurology, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jongho Lee
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea
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Zhu Z, Naji N, Esfahani JH, Snyder J, Seres P, Emery DJ, Noga M, Blevins G, Smyth P, Wilman AH. MR Susceptibility Separation for Quantifying Lesion Paramagnetic and Diamagnetic Evolution in Relapsing-Remitting Multiple Sclerosis. J Magn Reson Imaging 2024; 60:1867-1879. [PMID: 38308397 DOI: 10.1002/jmri.29266] [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: 10/04/2023] [Revised: 01/12/2024] [Accepted: 01/16/2024] [Indexed: 02/04/2024] Open
Abstract
BACKGROUND Multiple sclerosis (MS) lesion evolution may involve changes in diamagnetic myelin and paramagnetic iron. Conventional quantitative susceptibility mapping (QSM) can provide net susceptibility distribution, but not the discrete paramagnetic and diamagnetic components. PURPOSE To apply susceptibility separation (χ separation) to follow lesion evolution in MS with comparison to R2*/R2 '/QSM. STUDY TYPE Longitudinal, prospective. SUBJECTS Twenty relapsing-remitting MS subjects (mean age: 42.5 ± 9.4 years, 13 females; mean years of symptoms: 4.3 ± 1.4 years). FIELD STRENGTH/SEQUENCE Three-dimensional multiple echo gradient echo (QSM and R2* mapping), two-dimensional dual echo fast spin echo (R2 mapping), T2-weighted fluid attenuated inversion recovery, and T1-weighted magnetization prepared gradient echo sequences at 3 T. ASSESSMENT Data were analyzed from two scans separated by a mean interval of 14.4 ± 2.0 months. White matter lesions on fluid-attenuated inversion recovery were defined by an automatic pipeline, then manually refined (by ZZ/AHW, 3/25 years' experience in MRI), and verified by a radiologist (MN, 25 years' experience in MS). Susceptibility separation yielded the paramagnetic and diamagnetic susceptibility content of each voxel. Lesions were classified into four groups based on the variation of QSM/R2* or separated into positive/negative components from χ separation. STATISTICAL TESTS Two-sample paired t tests for assessment of longitudinal differences. Spearman correlation coefficients to assess associations between χ separation and R2*/R2 '/QSM. Significant level: P < 0.005. RESULTS A total of 183 lesions were quantified. Categorizing lesions into groups based on χ separation demonstrated significant annual changes in QSM//R2*/R2 '. When lesions were grouped based on changes in QSM and R2*, both changing in unison yielded a significant dominant paramagnetic variation and both opposing yielded a dominant diamagnetic variation. Significant Spearman correlation coefficients were found between susceptibility-sensitive MRI indices and χ separation. DATA CONCLUSION Susceptibility separation changes in MS lesions may distinguish and quantify paramagnetic and diamagnetic evolution, potentially providing additional insight compared to R2* and QSM alone. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Ziyan Zhu
- Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
| | - Nashwan Naji
- Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
- Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Alberta, Canada
| | - Javad Hamidi Esfahani
- Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
| | - Jeff Snyder
- Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
- Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Alberta, Canada
| | - Peter Seres
- Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Alberta, Canada
| | - Derek J Emery
- Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Alberta, Canada
| | - Michelle Noga
- Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Alberta, Canada
| | - Gregg Blevins
- Division of Neurology, Department of Medicine, University of Alberta, Edmonton, Alberta, Canada
| | - Penelope Smyth
- Division of Neurology, Department of Medicine, University of Alberta, Edmonton, Alberta, Canada
| | - Alan H Wilman
- Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
- Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Alberta, Canada
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Williams T, John N, Calvi A, Bianchi A, De Angelis F, Doshi A, Wright S, Shatila M, Yiannakas MC, Chowdhury F, Stutters J, Ricciardi A, Prados F, MacManus D, Grussu F, Karsa A, Samson B, Battiston M, Gandini Wheeler-Kingshott CAM, Shmueli K, Ciccarelli O, Barkhof F, Chataway J. Investigating the relationship between thalamic iron concentration and disease severity in secondary progressive multiple sclerosis using quantitative susceptibility mapping: Cross-sectional analysis from the MS-STAT2 randomised controlled trial. NEUROIMAGE. REPORTS 2024; 4:100216. [PMID: 39328985 PMCID: PMC11422291 DOI: 10.1016/j.ynirp.2024.100216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/27/2024] [Revised: 08/02/2024] [Accepted: 08/22/2024] [Indexed: 09/28/2024]
Abstract
Background Deep grey matter pathology is a key driver of disability worsening in people with multiple sclerosis. Quantitative susceptibility mapping (QSM) is an advanced magnetic resonance imaging (MRI) technique which quantifies local magnetic susceptibility from variations in phase produced by changes in the local magnetic field. In the deep grey matter, susceptibility has previously been validated against tissue iron concentration. However, it currently remains unknown whether susceptibility is abnormal in older progressive MS cohorts, and whether it correlates with disability. Objectives To investigate differences in mean regional susceptibility in deep grey matter between people with secondary progressive multiple sclerosis (SPMS) and healthy controls; to examine in patients the relationships between deep grey matter susceptibility and clinical and imaging measures of disease severity. Methods Baseline data from a subgroup of the MS-STAT2 trial (simvastatin vs. placebo in SPMS, NCT03387670) were included. The subgroup underwent clinical assessments and an advanced MRI protocol at 3T. A cohort of age-matched healthy controls underwent the same MRI protocol. Susceptibility maps were reconstructed using a robust QSM pipeline from multi-echo 3D gradient-echo sequence. Regions of interest (ROIs) in the thalamus, globus pallidus and putamen were segmented from 3D T1-weighted images, and lesions segmented from 3D fluid-attenuated inversion recovery images. Linear regression was used to compare susceptibility from ROIs between patients and controls, adjusting for age and sex. Where significant differences were found, we further examined the associations between ROI susceptibility and clinical and imaging measures of MS severity. Results 149 SPMS (77% female; mean age: 53 yrs; median Expanded Disability Status Scale (EDSS): 6.0 [interquartile range 4.5-6.0]) and 33 controls (52% female, mean age: 57) were included.Thalamic susceptibility was significantly lower in SPMS compared to controls: mean (SD) 28.6 (12.8) parts per billion (ppb) in SPMS vs. 39.2 (12.7) ppb in controls; regression coefficient: -12.0 [95% confidence interval: -17.0 to -7.1], p < 0.001. In contrast, globus pallidus and putamen susceptibility were similar between both groups.In SPMS, a 10 ppb lower thalamic susceptibility was associated with a +0.13 [+0.01 to +0.24] point higher EDSS (p < 0.05), a -2.4 [-3.8 to -1.0] point lower symbol digit modality test (SDMT, p = 0.001), and a -2.4 [-3.7 to -1.1] point lower Sloan low contrast acuity, 2.5% (p < 0.01).Lower thalamic susceptibility was also strongly associated with a higher T2 lesion volume (T2LV, p < 0.001) and lower normalised whole brain, deep grey matter and thalamic volumes (all p < 0.001). Conclusions The reduced thalamic susceptibility found in SPMS compared to controls suggests that thalamic iron concentrations are lower at this advanced stage of the disease. The observed relationships between lower thalamic susceptibility and more severe physical, cognitive and visual disability suggests that reductions in thalamic iron may correlate with important mechanisms of clinical disease progression. Such mechanisms appear to intimately link reductions in thalamic iron with higher T2LV and the development of thalamic atrophy, encouraging further research into QSM-derived thalamic susceptibility as a biomarker of disease severity in SPMS.
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Affiliation(s)
- Thomas Williams
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
| | - Nevin John
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
- Monash University, Department of Medicine, School of Clinical Sciences, Clayton, Australia
| | - Alberto Calvi
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
| | - Alessia Bianchi
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
| | - Floriana De Angelis
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
| | - Anisha Doshi
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
| | - Sarah Wright
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
| | - Madiha Shatila
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
| | - Marios C Yiannakas
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
| | - Fatima Chowdhury
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
| | - Jon Stutters
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
| | - Antonio Ricciardi
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
| | - Ferran Prados
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
- University College London, Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
- Universitat Oberta de Catalunya, Barcelona, Spain
| | - David MacManus
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
| | - Francesco Grussu
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
- University College London, Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
| | - Anita Karsa
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Becky Samson
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
- University College London, Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
| | - Marco Battiston
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
- University College London, Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
| | - Claudia A M Gandini Wheeler-Kingshott
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
| | - Karin Shmueli
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Olga Ciccarelli
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
- National Institute for Health Research, Biomedical Research Centre, University College London Hospitals, London, United Kingdom
| | - Frederik Barkhof
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
- University College London, Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
- National Institute for Health Research, Biomedical Research Centre, University College London Hospitals, London, United Kingdom
- Vrije Universiteit Amsterdam, Department of Radiology & Nuclear Medicine, VU University Medical Centre, Amsterdam, Netherlands
| | - Jeremy Chataway
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
- Medical Research Council Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, United Kingdom
- National Institute for Health Research, Biomedical Research Centre, University College London Hospitals, London, United Kingdom
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8
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Agarwal N, Fan A, Huang X, Dehkharghani S, van der Kolk A. ISMRM Clinical Focus Meeting 2023: "Imaging the Fire in the Brain". J Magn Reson Imaging 2024. [PMID: 39193867 DOI: 10.1002/jmri.29587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2024] [Revised: 08/09/2024] [Accepted: 08/11/2024] [Indexed: 08/29/2024] Open
Abstract
Set during the Annual Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM), the "Clinical Focus Meeting" (CFM) aims to bridge the gap between innovative magnetic resonance imaging (MRI) scientific research and daily patient care. This initiative is dedicated to maximizing the impact of MRI technology on healthcare outcomes for patients. At the 2023 Annual Meeting, clinicians and scientists from across the globe were invited to discuss neuroinflammation from various angles (entitled "Imaging the Fire in the Brain"). Topics ranged from fundamental mechanisms and biomarkers of neuroinflammation to the role of different contrast mechanisms, including both proton and non-proton techniques, in brain tumors, autoimmune disorders, and pediatric neuroinflammatory diseases. Discussions also delved into how systemic inflammation can trigger neuroinflammation and the role of the gut-brain axis in causing brain inflammation. Neuroinflammation arises from various external and internal factors and serves as a vital mechanism to mitigate tissue damage and provide neuroprotection. Nonetheless, excessive neuroinflammatory responses can lead to significant tissue injury and subsequent neurological impairments. Prolonged neuroinflammation can result in cellular apoptosis and neurodegeneration, posing severe consequences. MRI can be used to visualize these consequences, by detecting blood-brain barrier damage, characterizing brain lesions, quantifying edema, and identifying specific metabolites. It also facilitates monitoring of chronic changes in both the brain and spinal cord over time, potentially leading to better patient outcomes. This paper represents a summary of the 2023 CFM, and is intended to guide the enthusiastic MR user to several key and novel sequences that MRI offers to image pathophysiologic processes underlying acute and chronic neuroinflammation. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 3.
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Affiliation(s)
- Nivedita Agarwal
- Diagnostic Imaging and Neuroradiology Unit, IRCCS Scientific Institute E. Medea, Bosisio Parini, Lecco, Italy
| | - Audrey Fan
- Department of Neurology, University of California Davis Health, Sacramento, California, USA
- Department of Biomedical Engineering, University of California Davis, Davis, California, USA
| | - Xiaoqi Huang
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
| | - Seena Dehkharghani
- Department of Radiology, Albert Einstein College of Medicine-Montefiore Health, New York, New York, USA
| | - Anja van der Kolk
- Department of Medical Imaging, Radboudumc, Nijmegen, The Netherlands
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9
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Kan H, Uchida Y, Kawaguchi S, Kasai H, Hiwatashi A, Ueki Y. Quantitative susceptibility mapping for susceptibility source separation with adaptive relaxometric constant estimation (QSM-ARCS) from solely gradient-echo data. Neuroimage 2024; 296:120676. [PMID: 38852804 DOI: 10.1016/j.neuroimage.2024.120676] [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: 10/30/2023] [Revised: 06/03/2024] [Accepted: 06/06/2024] [Indexed: 06/11/2024] Open
Abstract
To separate the contributions of paramagnetic and diamagnetic sources within a voxel, a magnetic susceptibility source separation method based solely on gradient-echo data has been developed. To measure the opposing susceptibility sources more accurately, we propose a novel single-orientation quantitative susceptibility mapping method with adaptive relaxometric constant estimation (QSM-ARCS) for susceptibility source separation. Moreover, opposing susceptibilities and their anisotropic effects were determined in healthy volunteers in the white matter. Multiple spoiled gradient echo and diffusion tensor imaging of ten healthy volunteers was obtained using a 3 T magnetic resonance scanner. After the opposing susceptibility and fractional anisotropy (FA) maps had been reconstructed, the parametric maps were spatially normalized. To evaluate the agreements of QSM-ARCS against the susceptibility source separation method using R2 and R2* maps (χ-separation) by Bland-Altman plots, the opposing susceptibility values were measured using white and deep gray matter atlases. We then evaluated the relationships between the opposing susceptibilities and FAs in the white matter and used a field-to-fiber angle to assess the fiber orientation dependencies of the opposing susceptibilities. The susceptibility maps in QSM-ARCS were successfully reconstructed without large artifacts. In the Bland-Altman analyses, the opposing QSM-ARCS susceptibility values excellently agreed with the χ-separation maps. Significant inverse and proportional correlations were observed between FA and the negative and positive susceptibilities estimated by QSM-ARCS. The fiber orientation dependencies of the negative susceptibility represented a nonmonotonic feature. Conversely, the positive susceptibility increased linearly with the fiber angle with respect to the B0 field. The QSM-ARCS could accurately estimate the opposing susceptibilities, which were identical values of χ-separation, even using gradient echo alone. The opposing susceptibilities might offer direct biomarkers for assessment of the myelin and iron content in glial cells and, through the underlying magnetic sources, provide biologic insights toward clinical transition.
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Affiliation(s)
- Hirohito Kan
- Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, Japan; Department of Radiology, Nagoya City University Graduate School of Medical Sciences, Japan.
| | - Yuto Uchida
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Neurology, Nagoya City University Graduate School of Medical Sciences, Japan
| | | | - Harumasa Kasai
- Department of Radiology, Nagoya City University Hospital, Japan
| | - Akio Hiwatashi
- Department of Radiology, Nagoya City University Graduate School of Medical Sciences, Japan
| | - Yoshino Ueki
- Department of Rehabilitation Medicine, Nagoya City University Graduate School of Medical Sciences, Japan
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10
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Lee J, Ji S, Oh SH. So You Want to Image Myelin Using MRI: Magnetic Susceptibility Source Separation for Myelin Imaging. Magn Reson Med Sci 2024; 23:291-306. [PMID: 38644201 PMCID: PMC11234950 DOI: 10.2463/mrms.rev.2024-0001] [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: 01/05/2024] [Accepted: 03/19/2024] [Indexed: 04/23/2024] Open
Abstract
In MRI, researchers have long endeavored to effectively visualize myelin distribution in the brain, a pursuit with significant implications for both scientific research and clinical applications. Over time, various methods such as myelin water imaging, magnetization transfer imaging, and relaxometric imaging have been developed, each carrying distinct advantages and limitations. Recently, an innovative technique named as magnetic susceptibility source separation has emerged, introducing a novel surrogate biomarker for myelin in the form of a diamagnetic susceptibility map. This paper comprehensively reviews this cutting-edge method, providing the fundamental concepts of magnetic susceptibility, susceptibility imaging, and the validation of the diamagnetic susceptibility map as a myelin biomarker that indirectly measures myelin content. Additionally, the paper explores essential aspects of data acquisition and processing, offering practical insights for readers. A comparison with established myelin imaging methods is also presented, and both current and prospective clinical and scientific applications are discussed to provide a holistic understanding of the technique. This work aims to serve as a foundational resource for newcomers entering this dynamic and rapidly expanding field.
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Affiliation(s)
- Jongho Lee
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea
| | - Sooyeon Ji
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea
| | - Se-Hong Oh
- Biomedical Engineering, Hankuk University of Foreign Studies, Yongin, Korea
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11
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Bilgic B, Costagli M, Chan KS, Duyn J, Langkammer C, Lee J, Li X, Liu C, Marques JP, Milovic C, Robinson SD, Schweser F, Shmueli K, Spincemaille P, Straub S, van Zijl P, Wang Y. Recommended implementation of quantitative susceptibility mapping for clinical research in the brain: A consensus of the ISMRM electro-magnetic tissue properties study group. Magn Reson Med 2024; 91:1834-1862. [PMID: 38247051 PMCID: PMC10950544 DOI: 10.1002/mrm.30006] [Citation(s) in RCA: 28] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 10/31/2023] [Accepted: 12/14/2023] [Indexed: 01/23/2024]
Abstract
This article provides recommendations for implementing QSM for clinical brain research. It is a consensus of the International Society of Magnetic Resonance in Medicine, Electro-Magnetic Tissue Properties Study Group. While QSM technical development continues to advance rapidly, the current QSM methods have been demonstrated to be repeatable and reproducible for generating quantitative tissue magnetic susceptibility maps in the brain. However, the many QSM approaches available have generated a need in the neuroimaging community for guidelines on implementation. This article outlines considerations and implementation recommendations for QSM data acquisition, processing, analysis, and publication. We recommend that data be acquired using a monopolar 3D multi-echo gradient echo (GRE) sequence and that phase images be saved and exported in Digital Imaging and Communications in Medicine (DICOM) format and unwrapped using an exact unwrapping approach. Multi-echo images should be combined before background field removal, and a brain mask created using a brain extraction tool with the incorporation of phase-quality-based masking. Background fields within the brain mask should be removed using a technique based on SHARP or PDF, and the optimization approach to dipole inversion should be employed with a sparsity-based regularization. Susceptibility values should be measured relative to a specified reference, including the common reference region of the whole brain as a region of interest in the analysis. The minimum acquisition and processing details required when reporting QSM results are also provided. These recommendations should facilitate clinical QSM research and promote harmonized data acquisition, analysis, and reporting.
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Affiliation(s)
- Berkin Bilgic
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA
| | - Mauro Costagli
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Sciences (DINOGMI), University of Genoa, Genoa, Italy
- Laboratory of Medical Physics and Magnetic Resonance, IRCCS Stella Maris, Pisa, Italy
| | - Kwok-Shing Chan
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Jeff Duyn
- Advanced MRI Section, NINDS, National Institutes of Health, Bethesda, Maryland, USA
| | | | - Jongho Lee
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea
| | - Xu Li
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Chunlei Liu
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California, USA
- Helen Wills Neuroscience Institute, University of California, Berkeley, California, USA
| | - José P Marques
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Carlos Milovic
- School of Electrical Engineering (EIE), Pontificia Universidad Catolica de Valparaiso, Valparaiso, Chile
| | - Simon Daniel Robinson
- High Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
- Centre of Advanced Imaging, University of Queensland, Brisbane, Australia
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences at the University at Buffalo, Buffalo, New York, USA
- Center for Biomedical Imaging, Clinical and Translational Science Institute at the University at Buffalo, Buffalo, New York, USA
| | - Karin Shmueli
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Pascal Spincemaille
- MRI Research Institute, Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Sina Straub
- Department of Radiology, Mayo Clinic, Jacksonville, Florida, USA
| | - Peter van Zijl
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Yi Wang
- MRI Research Institute, Departments of Radiology and Biomedical Engineering, Cornell University, New York, New York, USA
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12
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Guan X, Lancione M, Ayton S, Dusek P, Langkammer C, Zhang M. Neuroimaging of Parkinson's disease by quantitative susceptibility mapping. Neuroimage 2024; 289:120547. [PMID: 38373677 DOI: 10.1016/j.neuroimage.2024.120547] [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: 07/30/2023] [Revised: 02/02/2024] [Accepted: 02/17/2024] [Indexed: 02/21/2024] Open
Abstract
Parkinson's disease (PD) is a common neurodegenerative disease, and apart from a few rare genetic causes, its pathogenesis remains largely unclear. Recent scientific interest has been captured by the involvement of iron biochemistry and the disruption of iron homeostasis, particularly within the brain regions specifically affected in PD. The advent of Quantitative Susceptibility Mapping (QSM) has enabled non-invasive quantification of brain iron in vivo by MRI, which has contributed to the understanding of iron-associated pathogenesis and has the potential for the development of iron-based biomarkers in PD. This review elucidates the biochemical underpinnings of brain iron accumulation, details advancements in iron-sensitive MRI technologies, and discusses the role of QSM as a biomarker of iron deposition in PD. Despite considerable progress, several challenges impede its clinical application after a decade of QSM studies. The initiation of multi-site research is warranted for developing robust, interpretable, and disease-specific biomarkers for monitoring PD disease progression.
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Affiliation(s)
- Xiaojun Guan
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; Joint Laboratory of Clinical Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Marta Lancione
- Laboratory of Medical Physics and Magnetic Resonance, IRCCS Stella Maris Foundation, Pisa, Italy
| | - Scott Ayton
- Florey Institute, The University of Melbourne, Australia
| | - Petr Dusek
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czechia; Department of Radiology, First Faculty of Medicine, Charles University and General University Hospital in Prague, Auenbruggerplatz 22, Prague 8036, Czechia
| | | | - Minming Zhang
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; Joint Laboratory of Clinical Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China.
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13
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Bordin V, Pirastru A, Bergsland N, Cazzoli M, Baselli G, Baglio F. Optimal echo times for quantitative susceptibility mapping: A test-retest study on basal ganglia and subcortical brain nuclei. Neuroimage 2023; 278:120272. [PMID: 37437701 DOI: 10.1016/j.neuroimage.2023.120272] [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: 04/13/2023] [Revised: 06/16/2023] [Accepted: 07/09/2023] [Indexed: 07/14/2023] Open
Abstract
Quantitative Susceptibility Mapping (QSM) is a recent MRI-technique able to quantify the bulk magnetic susceptibility of myelin, iron, and calcium in the brain. Its variability across different acquisition parameters has prompted the need for standardisation across multiple centres and MRI vendors. However, a high level of agreement between repeated imaging acquisitions is equally important. With this study we aimed to assess the inter-scan repeatability of an optimised multi-echo GRE sequence in 28 healthy volunteers. We extracted and compared the susceptibility measures from the scan and rescan acquisitions across 7 bilateral brain regions (i.e., 14 regions of interest (ROIs)) relevant for neurodegeneration. Repeatability was first assessed while reconstructing QSM with a fixed number of echo times (i.e., 8). Excellent inter-scan repeatability was found for putamen, globus pallidus and caudate nucleus, while good performance characterised the remaining structures. An increased variability was instead noted for small ROIs like red nucleus and substantia nigra. Secondly, we assessed the impact exerted on repeatability by the number of echoes used to derive QSM maps. Results were impacted by this parameter, especially in smaller regions. Larger brain structures, on the other hand, showed more consistent performance. Nevertheless, with either 8 or 7 echoes we managed to obtain good inter-scan repeatability on almost all ROIs. These findings indicate that the designed acquisition/reconstruction protocol has wide applicability, particularly in clinical or research settings involving longitudinal acquisitions (e.g. rehabilitation studies).
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Affiliation(s)
- Valentina Bordin
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.
| | - Alice Pirastru
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy; IRCCS Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy
| | - Niels Bergsland
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy; Department of Neurology, Buffalo Neuroimaging Analysis Center, School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, United States
| | - Marta Cazzoli
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy
| | - Giuseppe Baselli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
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14
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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: 8] [Impact Index Per Article: 4.0] [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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15
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Cogswell PM, Fan AP. Multimodal comparisons of QSM and PET in neurodegeneration and aging. Neuroimage 2023; 273:120068. [PMID: 37003447 PMCID: PMC10947478 DOI: 10.1016/j.neuroimage.2023.120068] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 03/17/2023] [Accepted: 03/28/2023] [Indexed: 04/03/2023] Open
Abstract
Quantitative susceptibility mapping (QSM) has been used to study susceptibility changes that may occur based on tissue composition and mineral deposition. Iron is a primary contributor to changes in magnetic susceptibility and of particular interest in applications of QSM to neurodegeneration and aging. Iron can contribute to neurodegeneration through inflammatory processes and via interaction with aggregation of disease-related proteins. To better understand the local susceptibility changes observed on QSM, its signal has been studied in association with other imaging metrics such as positron emission tomography (PET). The associations of QSM and PET may provide insight into the pathophysiology of disease processes, such as the role of iron in aging and neurodegeneration, and help to determine the diagnostic utility of QSM as an indirect indicator of disease processes typically evaluated with PET. In this review we discuss the proposed mechanisms and summarize prior studies of the associations of QSM and amyloid PET, tau PET, TSPO PET, FDG-PET, 15O-PET, and F-DOPA PET in evaluation of neurologic diseases with a focus on aging and neurodegeneration.
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Affiliation(s)
- Petrice M Cogswell
- Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA.
| | - Audrey P Fan
- Department of Biomedical Engineering and Department of Neurology, University of California, Davis, 1590 Drew Avenue, Davis, CA 95618, USA
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16
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Li Z, Feng R, Liu Q, Feng J, Lao G, Zhang M, Li J, Zhang Y, Wei H. APART-QSM: an improved sub-voxel quantitative susceptibility mapping for susceptibility source separation using an iterative data fitting method. Neuroimage 2023; 274:120148. [PMID: 37127191 DOI: 10.1016/j.neuroimage.2023.120148] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 02/06/2023] [Accepted: 04/28/2023] [Indexed: 05/03/2023] Open
Abstract
The brain tissue phase contrast in MRI sequences reflects the spatial distributions of multiple substances, such as iron, myelin, calcium, and proteins. These substances with paramagnetic and diamagnetic susceptibilities often colocalize in one voxel in brain regions. Both opposing susceptibilities play vital roles in brain development and neurodegenerative diseases. Conventional QSM methods only provide voxel-averaged susceptibility value and cannot disentangle intravoxel susceptibilities with opposite signs. Advanced susceptibility imaging methods have been recently developed to distinguish the contributions of opposing susceptibility sources for QSM. The basic concept of separating paramagnetic and diamagnetic susceptibility proportions is to include the relaxation rate R2* with R2' in QSM. The magnitude decay kernel, describing the proportionality coefficient between R2' and susceptibility, is an essential reconstruction coefficient for QSM separation methods. In this study, we proposed a more comprehensive complex signal model that describes the relationship between 3D GRE signal and the contributions of paramagnetic and diamagnetic susceptibility to the frequency shift and R2* relaxation. The algorithm is implemented as a constrained minimization problem in which the voxel-wise magnitude decay kernel and sub-voxel susceptibilities are determined alternately in each iteration until convergence. The calculated voxel-wise magnitude decay kernel could realistically model the relationship between the R2' relaxation and the volume susceptibility. Thus, the proposed method effectively prevents the errors of the magnitude decay kernel from propagating to the final susceptibility separation reconstruction. Phantom studies, ex vivo macaque brain experiments, and in vivo human brain imaging studies were conducted to evaluate the ability of the proposed method to distinguish paramagnetic and diamagnetic susceptibility sources. The results demonstrate that the proposed method provides state-of-the-art performances for quantifying brain iron and myelin compared to previous QSM separation methods. Our results show that the proposed method has the potential to simultaneously quantify whole brain iron and myelin during brain development and aging. The proposed model was also deployed with multiple-orientation complex GRE data input measurements, resulting in high-quality QSM separation maps with more faithful tissue delineation between brain structures compared to those reconstructed by single-orientation QSM separation methods.
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Affiliation(s)
- Zhenghao Li
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Ruimin Feng
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Qiangqiang Liu
- Department of Neurosurgery, Clinical Neuroscience Center Comprehensive Epilepsy Unit, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jie Feng
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Guoyan Lao
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Ming Zhang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Jun Li
- School of Information Science and Technology, ShanghaiTech University, Shanghai, China
| | - Yuyao Zhang
- School of Information Science and Technology, ShanghaiTech University, Shanghai, China
| | - Hongjiang Wei
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
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17
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Straub S, El-Sanosy E, Emmerich J, Sandig FL, Ladd ME, Schlemmer HP. Quantitative magnetic resonance imaging biomarkers for cortical pathology in multiple sclerosis at 7 T. NMR IN BIOMEDICINE 2023; 36:e4847. [PMID: 36259249 DOI: 10.1002/nbm.4847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 10/08/2022] [Accepted: 10/12/2022] [Indexed: 06/16/2023]
Abstract
Substantial cortical gray matter tissue damage, which correlates with clinical disease severity, has been revealed in multiple sclerosis (MS) using advanced magnetic resonance imaging (MRI) methods at 3 T and the use of ultra-high field, as well as in histopathology studies. While clinical assessment mainly focuses on lesions using T 1 - and T 2 -weighted MRI, quantitative MRI (qMRI) methods are capable of uncovering subtle microstructural changes. The aim of this ultra-high field study is to extract possible future MR biomarkers for the quantitative evaluation of regional cortical pathology. Because of their sensitivity to iron, myelin, and in part specifically to cortical demyelination, T 1 , T 2 , R 2 * , and susceptibility mapping were performed including two novel susceptibility markers; in addition, cortical thickness as well as the volumes of 34 cortical regions were computed. Data were acquired in 20 patients and 16 age- and sex-matched healthy controls. In 18 cortical regions, large to very large effect sizes (Cohen's d ≥ 1) and statistically significant differences in qMRI values between patients and controls were revealed compared with only four regions when using more standard MR measures, namely, volume and cortical thickness. Moreover, a decrease in all susceptibility contrasts ( χ , χ + , χ - ) and R 2 * values indicates that the role of cortical demyelination might outweigh inflammatory processes in the form of iron accumulation in cortical MS pathology, and might also indicate iron loss. A significant association between susceptibility contrasts as well as R 2 * of the caudal middle frontal gyrus and disease duration was found (adjusted R2 : 0.602, p = 0.0011). Quantitative MRI parameters might be more sensitive towards regional cortical pathology compared with the use of conventional markers only and therefore may play a role in early detection of tissue damage in MS in the future.
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Affiliation(s)
- Sina Straub
- Division of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Faculty of Medicine, Heidelberg University, Heidelberg, Germany
| | - Edris El-Sanosy
- Division Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Julian Emmerich
- Division of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Frederik L Sandig
- Division Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Mark E Ladd
- Division of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Faculty of Medicine, Heidelberg University, Heidelberg, Germany
- Faculty of Physics and Astronomy, Heidelberg University, Heidelberg, Germany
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Lancione M, Bosco P, Costagli M, Nigri A, Aquino D, Carne I, Ferraro S, Giulietti G, Napolitano A, Palesi F, Pavone L, Pirastru A, Savini G, Tagliavini F, Bruzzone MG, Gandini Wheeler-Kingshott CA, Tosetti M, Biagi L. Multi-centre and multi-vendor reproducibility of a standardized protocol for quantitative susceptibility Mapping of the human brain at 3T. Phys Med 2022; 103:37-45. [DOI: 10.1016/j.ejmp.2022.09.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 09/12/2022] [Accepted: 09/27/2022] [Indexed: 11/16/2022] Open
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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: 4.0] [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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Dimov AV, Gillen KM, Nguyen TD, Kang J, Sharma R, Pitt D, Gauthier SA, Wang Y. Magnetic Susceptibility Source Separation Solely from Gradient Echo Data: Histological Validation. Tomography 2022; 8:1544-1551. [PMID: 35736875 PMCID: PMC9228115 DOI: 10.3390/tomography8030127] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 06/09/2022] [Accepted: 06/11/2022] [Indexed: 11/17/2022] Open
Abstract
Quantitative susceptibility mapping (QSM) facilitates mapping of the bulk magnetic susceptibility of tissue from the phase of complex gradient echo (GRE) MRI data. QSM phase processing combined with an R2* model of magnitude of multiecho gradient echo data (R2*QSM) allows separation of dia- and para-magnetic components (e.g., myelin and iron) that contribute constructively to R2* value but destructively to the QSM value of a voxel. This R2*QSM technique is validated against quantitative histology—optical density of myelin basic protein and Perls’ iron histological stains of rim and core of 10 ex vivo multiple sclerosis lesions, as well as neighboring normal appearing white matter. We found that R2*QSM source maps are in good qualitative agreement with histology, e.g., showing increased iron concentration at the edge of the rim+ lesions and myelin loss in the lesions’ core. Furthermore, our results indicate statistically significant correlation between paramagnetic and diamagnetic tissue components estimated with R2*QSM and optical densities of Perls’ and MPB stains. These findings provide direct support for the use of R2*QSM magnetic source separation based solely on GRE complex data to characterize MS lesion composition.
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Affiliation(s)
- Alexey V. Dimov
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA; (A.V.D.); (K.M.G.); (T.D.N.); (J.K.); (R.S.)
| | - Kelly M. Gillen
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA; (A.V.D.); (K.M.G.); (T.D.N.); (J.K.); (R.S.)
| | - Thanh D. Nguyen
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA; (A.V.D.); (K.M.G.); (T.D.N.); (J.K.); (R.S.)
| | - Jerry Kang
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA; (A.V.D.); (K.M.G.); (T.D.N.); (J.K.); (R.S.)
| | - Ria Sharma
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA; (A.V.D.); (K.M.G.); (T.D.N.); (J.K.); (R.S.)
| | - David Pitt
- Department of Neurology, Yale Medicine, New Haven, CT 06511, USA;
| | - Susan A. Gauthier
- Department of Neurology, Weill Cornell Medicine, New York, NY 10022, USA;
| | - Yi Wang
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA; (A.V.D.); (K.M.G.); (T.D.N.); (J.K.); (R.S.)
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY 14850, USA
- Correspondence:
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Dimov AV, Nguyen TD, Gillen KM, Marcille M, Spincemaille P, Pitt D, Gauthier SA, Wang Y. Susceptibility source separation from gradient echo data using magnitude decay modeling. J Neuroimaging 2022; 32:852-859. [PMID: 35668022 DOI: 10.1111/jon.13014] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Revised: 05/17/2022] [Accepted: 05/19/2022] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND AND PURPOSE The objective is to demonstrate feasibility of separating magnetic sources in quantitative susceptibility mapping (QSM) by incorporating magnitude decay rates R 2 ∗ $R_2^{\rm{*}}$ in gradient echo (GRE) MRI. METHODS Magnetic susceptibility source separation was developed using R 2 ∗ $R_2^{\rm{*}}$ and compared with a prior method using R 2 ' = R 2 ∗ - R 2 ${R^{\prime}_2} = R_2^* - {R_2}$ that required an additional sequence to measure the transverse relaxation rate R2 . Both susceptibility separation methods were compared in multiple sclerosis (MS) patients (n = 17). Susceptibility values of negative sources estimated with R 2 ∗ $R_2^{\rm{*}}$ -based source separation in a set of enhancing MS lesions (n = 44) were correlated against longitudinal myelin water fraction (MWF) changes. RESULTS In in vivo data, linear regression of the estimated χ + ${\chi}^{+}$ and χ - ${\chi}^{-}$ susceptibility values between the R 2 ∗ $R_2^*$ - and the R 2 ' ${R^{\prime}_2}$ -based separation methods performed across 182 segmented lesions revealed correlation coefficient r = .96 and slope close .99. Correlation analysis in enhancing lesions revealed a significant positive association between the χ - ${\chi}^{-}$ increase at 1-year post-onset relative to 0 year and the MWF increase at 1 year relative to 0 year (β = -0.144, 95% confidence interval: [-0.199, -0.1], p = .0008) and good agreement between R 2 ' ${R^{\prime}_2}$ and R 2 ∗ $R_2^*$ methods (r = .79, slope = .95). CONCLUSIONS Separation of magnetic sources based solely on GRE complex data is feasible by combining magnitude decay rate modeling and phase-based QSM and χ - ${\chi}^{-}$ change may serve as a biomarker for myelin recovery or damage in acute MS lesions.
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Affiliation(s)
- Alexey V Dimov
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Thanh D Nguyen
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Kelly M Gillen
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Melanie Marcille
- Department of Neurology, Weill Cornell Medicine, New York, New York, USA
| | | | - David Pitt
- Department of Neurology, Yale School of Medicine, New Haven, Connecticut, USA
| | - Susan A Gauthier
- Department of Neurology, Weill Cornell Medicine, New York, New York, USA
| | - Yi Wang
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
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