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Cherukara MT, Shmueli K. Comparing repeatability metrics for quantitative susceptibility mapping in the head and neck. MAGMA (NEW YORK, N.Y.) 2025:10.1007/s10334-025-01229-3. [PMID: 40024974 DOI: 10.1007/s10334-025-01229-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2024] [Revised: 01/09/2025] [Accepted: 01/21/2025] [Indexed: 03/04/2025]
Abstract
OBJECTIVE Quantitative susceptibility mapping (QSM) is a technique that has been demonstrated to be highly repeatable in the brain. As QSM is applied to other parts of the body, it is necessary to investigate metrics for quantifying repeatability, to enable optimization of repeatable QSM reconstruction pipelines beyond the brain. MATERIALS AND METHODS MRI data were acquired in the head and neck (HN) region in ten healthy volunteers, who underwent six acquisitions across two sessions. QSMs were reconstructed using six representative state-of-the-art techniques. Repeatability of the susceptibility values was compared using voxel-wise metrics (normalized root mean squared error and XSIM) and ROI-based metrics (within-subject and between-subject standard deviation, coefficient of variation (CV), intraclass correlation coefficient (ICC)). RESULTS Both within-subject and between-subject variations were smaller than the variation between QSM dipole inversion methods, in most ROIs. autoNDI produced the most repeatable susceptibility values, with ICC > 0.75 in three of six HN ROIs with an average ICC of 0.66 across all ROIs. Joint consideration of standard deviation and ICC offered the best metric of repeatability for comparisons between QSM methods, given typical distributions of positive and negative QSM values. DISCUSSION Repeatability of QSM in the HN region is highly dependent on the dipole inversion method chosen, but the most repeatable methods (autoNDI, QSMnet, TFI) are only moderately repeatable in most HN ROIs.
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Affiliation(s)
- Matthew T Cherukara
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK.
| | - Karin Shmueli
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK
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Li Y, Li W, Paez A, Cao D, Sun Y, Gu C, Zhang K, Miao X, Liu P, Li W, Pillai JJ, Lu H, van Zijl PCM, Earley C, Li X, Hua J. Imaging arterial and venous vessels using Iron Dextran enhanced multi-echo 3D gradient echo MRI at 7T. NMR IN BIOMEDICINE 2024; 37:e5251. [PMID: 39187441 DOI: 10.1002/nbm.5251] [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: 03/21/2024] [Revised: 08/04/2024] [Accepted: 08/16/2024] [Indexed: 08/28/2024]
Abstract
Iron Dextran is a widely used iron oxide compound to treat iron-deficiency anemia patients in the clinic. Similar to other iron oxide compounds such as Ferumoxytol, it can also be used off-label as an intravascular magnetic resonance imaging (MRI) contrast agent due to its strong iron-induced T2 and T2* shortening effects. In this study, we seek to evaluate the feasibility of using Iron Dextran enhanced multi-echo susceptibility weighted imaging (SWI) MRI at 7T to image arterial and venous blood vessels in the human brain. Phantom experiments were performed to measure the r2* relaxivity for Iron Dextran in blood, based on which the SWI sequence was optimized. Pre- and post-infusion MR images were acquired in human subjects from which maps of arteries and veins were extracted. The post-contrast SWI images showed enhanced susceptibility difference between blood and the surrounding tissue in both arteries and veins. Our results showed that the proposed Iron Dextran enhanced multi-echo SWI approach allowed the visualization of blood vessels with diameters down to ~100 μm, including small blood vessels supplying and draining small brain structures such as the hippocampus. We conclude that Iron Dextran can be an alternative iron-based MRI contrast agent for blood vessel imaging in the human brain.
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Affiliation(s)
- Yinghao Li
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
- Division of MRI Research, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Wei Li
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
- Division of MRI Research, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, International Science and Technology, Cooperation base of Child development and Critical Disorders, Chongqing Key Laboratory of Child, Neurodevelopment and Cognitive Disorders, Chongqing, China
| | - Adrian Paez
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
- Division of MRI Research, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Di Cao
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
- Division of MRI Research, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Yuanqi Sun
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
- Division of MRI Research, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Chunming Gu
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
- Division of MRI Research, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Kaihua Zhang
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
- Division of MRI Research, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- School of Psychology, Shandong Normal University, Jinan, China
| | - Xinyuan Miao
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
- Division of MRI Research, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Peiying Liu
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
- Division of MRI Research, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Wenbo Li
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
- Division of MRI Research, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Jay J Pillai
- Division of Neuroradiology, Mayo Clinic College of Medicine and Science, Rochester, Minnesota, USA
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Hanzhang Lu
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
- Division of MRI Research, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Peter C M van Zijl
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
- Division of MRI Research, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Christopher Earley
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Xu Li
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
- Division of MRI Research, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Jun Hua
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
- Division of MRI Research, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
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3
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Li C, Buch S, Sun Z, Muccio M, Jiang L, Chen Y, Haacke EM, Zhang J, Wisniewski TM, Ge Y. In vivo mapping of hippocampal venous vasculature and oxygenation using susceptibility imaging at 7T. Neuroimage 2024; 291:120597. [PMID: 38554779 PMCID: PMC11115460 DOI: 10.1016/j.neuroimage.2024.120597] [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: 09/12/2023] [Revised: 03/19/2024] [Accepted: 03/28/2024] [Indexed: 04/02/2024] Open
Abstract
Mapping the small venous vasculature of the hippocampus in vivo is crucial for understanding how functional changes of hippocampus evolve with age. Oxygen utilization in the hippocampus could serve as a sensitive biomarker for early degenerative changes, surpassing hippocampal tissue atrophy as the main source of information regarding tissue degeneration. Using an ultrahigh field (7T) susceptibility-weighted imaging (SWI) sequence, it is possible to capture oxygen-level dependent contrast of submillimeter-sized vessels. Moreover, the quantitative susceptibility mapping (QSM) results derived from SWI data allow for the simultaneous estimation of venous oxygenation levels, thereby enhancing the understanding of hippocampal function. In this study, we proposed two potential imaging markers in a cohort of 19 healthy volunteers aged between 20 and 74 years. These markers were: 1) hippocampal venous density on SWI images and 2) venous susceptibility (Δχvein) in the hippocampus-associated draining veins (the inferior ventricular veins (IVV) and the basal veins of Rosenthal (BVR) using QSM images). They were chosen specifically to help characterize the oxygen utilization of the human hippocampus and medial temporal lobe (MTL). As part of the analysis, we demonstrated the feasibility of measuring hippocampal venous density and Δχvein in the IVV and BVR at 7T with high spatial resolution (0.25 × 0.25 × 1 mm3). Our results demonstrated the in vivo reconstruction of the hippocampal venous system, providing initial evidence regarding the presence of the venous arch structure within the hippocampus. Furthermore, we evaluated the age effect of the two quantitative estimates and observed a significant increase in Δχvein for the IVV with age (p=0.006, r2 = 0.369). This may suggest the potential application of Δχvein in IVV as a marker for assessing changes in atrophy-related hippocampal oxygen utilization in normal aging and neurodegenerative diseases such as AD and dementia.
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Affiliation(s)
- Chenyang Li
- Department of Radiology, Center for Biomedical Imaging, NYU Grossman School of Medicine, New York, NY, USA; Vilcek Institute of Graduate Biomedical Sciences, NYU Grossman School of Medicine, New York, NY, USA
| | - Sagar Buch
- Department of Neurology, Wayne State University School of Medicine, Detroit, MI, USA
| | - Zhe Sun
- Department of Radiology, Center for Biomedical Imaging, NYU Grossman School of Medicine, New York, NY, USA; Vilcek Institute of Graduate Biomedical Sciences, NYU Grossman School of Medicine, New York, NY, USA
| | - Marco Muccio
- Department of Radiology, Center for Biomedical Imaging, NYU Grossman School of Medicine, New York, NY, USA
| | - Li Jiang
- Department of Radiology, Center for Biomedical Imaging, NYU Grossman School of Medicine, New York, NY, USA
| | - Yongsheng Chen
- Department of Neurology, Wayne State University School of Medicine, Detroit, MI, USA
| | - E Mark Haacke
- Department of Neurology, Wayne State University School of Medicine, Detroit, MI, USA; Department of Radiology, Wayne State University School of Medicine, Detroit, MI, USA
| | - Jiangyang Zhang
- Department of Radiology, Center for Biomedical Imaging, NYU Grossman School of Medicine, New York, NY, USA
| | | | - Yulin Ge
- Department of Radiology, Center for Biomedical Imaging, NYU Grossman School of Medicine, New York, NY, USA.
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4
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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: 47] [Impact Index Per Article: 47.0] [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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Huck J, Jäger A, Schneider U, Grahl S, Fan AP, Tardif C, Villringer A, Bazin P, Steele CJ, Gauthier CJ. Modeling venous bias in resting state functional MRI metrics. Hum Brain Mapp 2023; 44:4938-4955. [PMID: 37498014 PMCID: PMC10472917 DOI: 10.1002/hbm.26431] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 04/12/2023] [Accepted: 05/11/2023] [Indexed: 07/28/2023] Open
Abstract
Resting-state (rs) functional magnetic resonance imaging (fMRI) is used to detect low-frequency fluctuations in the blood oxygen-level dependent (BOLD) signal across brain regions. Correlations between temporal BOLD signal fluctuations are commonly used to infer functional connectivity. However, because BOLD is based on the dilution of deoxyhemoglobin, it is sensitive to veins of all sizes, and its amplitude is biased by draining veins. These biases affect local BOLD signal location and amplitude, and may also influence BOLD-derived connectivity measures, but the magnitude of this venous bias and its relation to vein size and proximity is unknown. Here, veins were identified using high-resolution quantitative susceptibility maps and utilized in a biophysical model to investigate systematic venous biases on common local rsfMRI-derived measures. Specifically, we studied the impact of vein diameter and distance to veins on the amplitude of low-frequency fluctuations (ALFF), fractional ALFF (fALFF), Hurst exponent (HE), regional homogeneity (ReHo), and eigenvector centrality values in the grey matter. Values were higher across all distances in smaller veins, and decreased with increasing vein diameter. Additionally, rsfMRI values associated with larger veins decrease with increasing distance from the veins. ALFF and ReHo were the most biased by veins, while HE and fALFF exhibited the smallest bias. Across all metrics, the amplitude of the bias was limited in voxel-wise data, confirming that venous structure is not the dominant source of contrast in these rsfMRI metrics. Finally, the models presented can be used to correct this venous bias in rsfMRI metrics.
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Affiliation(s)
- Julia Huck
- Department of PhysicsConcordia UniversityMontrealQuebecCanada
- PERFORM CenterMontrealQuebecCanada
| | - Anna‐Thekla Jäger
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- Center for Stroke Research Berlin (CSB)Charité ‐ Universitätsmedizin BerlinBerlinGermany
| | - Uta Schneider
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Sophia Grahl
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Audrey P. Fan
- Department of Biomedical EngineeringUniversity of CaliforniaDavisCaliforniaUSA
- Department of NeurologyUniversity of CaliforniaDavisCaliforniaUSA
| | - Christine Tardif
- Faculty of Medicine and Health Sciences, Department of Biomedical EngineeringMcGill UniversityMontrealQuebecCanada
- McConnell Brain Imaging CentreMontreal Neurological InstituteMontrealQuebecCanada
| | - Arno Villringer
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- Center for Stroke Research Berlin (CSB)Charité ‐ Universitätsmedizin BerlinBerlinGermany
- Clinic for Cognitive NeurologyUniversity of LeipzigLeipzigGermany
- IFB Adiposity DiseasesLeipzig University Medical CentreLeipzigGermany
| | - Pierre‐Louis Bazin
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- Faculty of Social and Behavioural SciencesUniversity of AmsterdamAmsterdamThe Netherlands
| | - Christopher J. Steele
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- Department of PsychologyConcordia UniversityMontrealQuebecCanada
| | - Claudine J. Gauthier
- Department of PhysicsConcordia UniversityMontrealQuebecCanada
- PERFORM CenterMontrealQuebecCanada
- Montreal Heart InstituteMontrealQuebecCanada
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Biondetti E, Cho J, Lee H. Cerebral oxygen metabolism from MRI susceptibility. Neuroimage 2023; 276:120189. [PMID: 37230206 PMCID: PMC10335841 DOI: 10.1016/j.neuroimage.2023.120189] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 04/26/2023] [Accepted: 05/23/2023] [Indexed: 05/27/2023] Open
Abstract
This article provides an overview of MRI methods exploiting magnetic susceptibility properties of blood to assess cerebral oxygen metabolism, including the tissue oxygen extraction fraction (OEF) and the cerebral metabolic rate of oxygen (CMRO2). The first section is devoted to describing blood magnetic susceptibility and its effect on the MRI signal. Blood circulating in the vasculature can have diamagnetic (oxyhemoglobin) or paramagnetic properties (deoxyhemoglobin). The overall balance between oxygenated and deoxygenated hemoglobin determines the induced magnetic field which, in turn, modulates the transverse relaxation decay of the MRI signal via additional phase accumulation. The following sections of this review then illustrate the principles underpinning susceptibility-based techniques for quantifying OEF and CMRO2. Here, it is detailed whether these techniques provide global (OxFlow) or local (Quantitative Susceptibility Mapping - QSM, calibrated BOLD - cBOLD, quantitative BOLD - qBOLD, QSM+qBOLD) measurements of OEF or CMRO2, and what signal components (magnitude or phase) and tissue pools they consider (intravascular or extravascular). Validations studies and potential limitations of each method are also described. The latter include (but are not limited to) challenges in the experimental setup, the accuracy of signal modeling, and assumptions on the measured signal. The last section outlines the clinical uses of these techniques in healthy aging and neurodegenerative diseases and contextualizes these reports relative to results from gold-standard PET.
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Affiliation(s)
- Emma Biondetti
- Department of Neuroscience, Imaging and Clinical Sciences, "D'Annunzio University" of Chieti-Pescara, Chieti, Italy; Institute for Advanced Biomedical Technologies, "D'Annunzio University" of Chieti-Pescara, Chieti, Italy
| | - Junghun Cho
- Department of Biomedical Engineering, University at Buffalo, The State University of New York, New York, USA
| | - Hyunyeol Lee
- School of Electronic and Electrical Engineering, Kyungpook National University, Daegu, Republic of Korea; Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
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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: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [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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8
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Kiersnowski OC, Karsa A, Wastling SJ, Thornton JS, Shmueli K. Investigating the effect of oblique image acquisition on the accuracy of QSM and a robust tilt correction method. Magn Reson Med 2023; 89:1791-1808. [PMID: 36480002 PMCID: PMC10953050 DOI: 10.1002/mrm.29550] [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: 09/23/2022] [Revised: 10/28/2022] [Accepted: 11/16/2022] [Indexed: 12/13/2022]
Abstract
PURPOSE Quantitative susceptibility mapping (QSM) is used increasingly for clinical research where oblique image acquisition is commonplace, but its effects on QSM accuracy are not well understood. THEORY AND METHODS The QSM processing pipeline involves defining the unit magnetic dipole kernel, which requires knowledge of the direction of the main magnetic fieldB ^ 0 $$ {\hat{\boldsymbol{B}}}_{\mathbf{0}} $$ with respect to the acquired image volume axes. The direction ofB ^ 0 $$ {\hat{\boldsymbol{B}}}_{\mathbf{0}} $$ is dependent on the axis and angle of rotation in oblique acquisition. Using both a numerical brain phantom and in vivo acquisitions in 5 healthy volunteers, we analyzed the effects of oblique acquisition on magnetic susceptibility maps. We compared three tilt-correction schemes at each step in the QSM pipeline: phase unwrapping, background field removal and susceptibility calculation, using the RMS error and QSM-tuned structural similarity index. RESULTS Rotation of wrapped phase images gave severe artifacts. Background field removal with projection onto dipole fields gave the most accurate susceptibilities when the field map was first rotated into alignment withB ^ 0 $$ {\hat{\boldsymbol{B}}}_{\mathbf{0}} $$ . Laplacian boundary value and variable-kernel sophisticated harmonic artifact reduction for phase data background field removal methods gave accurate results without tilt correction. For susceptibility calculation, thresholded k-space division, iterative Tikhonov regularization, and weighted linear total variation regularization, all performed most accurately when local field maps were rotated into alignment withB ^ 0 $$ {\hat{\boldsymbol{B}}}_{\mathbf{0}} $$ before susceptibility calculation. CONCLUSION For accurate QSM, oblique acquisition must be taken into account. Rotation of images into alignment withB ^ 0 $$ {\hat{\boldsymbol{B}}}_{\mathbf{0}} $$ should be carried out after phase unwrapping and before background-field removal. We provide open-source tilt-correction code to incorporate easily into existing pipelines: https://github.com/o-snow/QSM_TiltCorrection.git.
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Affiliation(s)
- Oliver C. Kiersnowski
- Department of Medical Physics and Biomedical EngineeringUniversity College LondonLondonUnited Kingdom
| | - Anita Karsa
- Department of Medical Physics and Biomedical EngineeringUniversity College LondonLondonUnited Kingdom
| | - Stephen J. Wastling
- Neuroradiological Academic UnitUCL Queen Square Institute of NeurologyLondonUnited Kingdom
- Lysholm Department of NeuroradiologyNational Hospital for Neurology and NeurosurgeryLondonUnited Kingdom
| | - John S. Thornton
- Neuroradiological Academic UnitUCL Queen Square Institute of NeurologyLondonUnited Kingdom
- Lysholm Department of NeuroradiologyNational Hospital for Neurology and NeurosurgeryLondonUnited Kingdom
| | - Karin Shmueli
- Department of Medical Physics and Biomedical EngineeringUniversity College LondonLondonUnited Kingdom
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Sub-acute Changes on MRI Measures of Cerebral Blood Flow and Venous Oxygen Saturation in Concussed Australian Rules Footballers. SPORTS MEDICINE - OPEN 2022; 8:45. [PMID: 35362855 PMCID: PMC8975948 DOI: 10.1186/s40798-022-00435-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 03/17/2022] [Indexed: 12/03/2022]
Abstract
Background Sports-related concussion (SRC) is common in collision sport athletes. There is growing evidence that repetitive SRC can have serious neurological consequences, particularly when the repetitive injuries occur when the brain has yet to fully recover from the initial injury. Hence, there is a need to identify biomarkers that are capable of determining SRC recovery so that they can guide clinical decisions pertaining to return-to-play. Cerebral venous oxygen saturation (SvO2) and cerebral blood flow (CBF) can be measured using magnetic resonance imaging (MRI) and may provide insights into changing energy demands and recovery following SRC. Results In this study we therefore investigated SvO2 and CBF in a cohort of concussed amateur Australian Football athletes (i.e., Australia’s most participated collision sport). Male and female Australian footballers (n = 13) underwent MRI after being cleared to return to play following a mandatory 13-day recovery period and were compared to a group of control Australian footballers (n = 16) with no recent history of SRC (i.e., > 3 months since last SRC). Despite the concussed Australian footballers being cleared to return to play at the time of MRI, we found evidence of significantly increased susceptibility in the global white matter (p = 0.020) and a trend (F5,21 = 2.404, p = 0.071) for reduced relative CBF (relCBF) compared to the control group. Further, there was evidence of an interaction between sex and injury in straight sinus susceptibility values (F1,25 = 3.858, p = 0.061) which were decreased in female SRC athletes (p = 0.053). Of note, there were significant negative correlations between straight sinus susceptibility and relCBF suggesting impaired metabolic function after SRC. Conclusions These findings support the use of quantitative susceptibility mapping (QSM) and relCBF as sensitive indicators of SRC, and raise further concerns related to SRC guidelines that allow for return-to-play in less than two weeks.
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Naji N, Lauzon ML, Seres P, Stolz E, Frayne R, Lebel C, Beaulieu C, Wilman AH. Multisite reproducibility of quantitative susceptibility mapping and effective transverse relaxation rate in deep gray matter at 3 T using locally optimized sequences in 24 traveling heads. NMR IN BIOMEDICINE 2022; 35:e4788. [PMID: 35704837 DOI: 10.1002/nbm.4788] [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: 04/01/2022] [Revised: 05/28/2022] [Accepted: 06/05/2022] [Indexed: 06/15/2023]
Abstract
Iron concentration in the human brain plays a crucial role in several neurodegenerative diseases and can be monitored noninvasively using quantitative susceptibility mapping (QSM) and effective transverse relaxation rate (R2 *) mapping from multiecho T2 *-weighted images. Large population studies enable better understanding of pathologies and can benefit from pooling multisite data. However, reproducibility may be compromised between sites and studies using different hardware and sequence protocols. This work investigates QSM and R2 * reproducibility at 3 T using locally optimized sequences from three centers and two vendors, and investigates possible reduction of cross-site variability through postprocessing approaches. Twenty-four healthy subjects traveled between three sites and were scanned twice at each site. Scan-rescan measurements from seven deep gray matter regions were used for assessing within-site and cross-site reproducibility using intraclass correlation coefficient (ICC) and within-subject standard deviation (SDw) measures. In addition, multiple QSM and R2 * postprocessing options were investigated with the aim to minimize cross-site sequence-related variations, including: mask generation approach, echo-timing selection, harmonizing spatial resolution, field map estimation, susceptibility inversion method, and linear field correction for magnitude images. The same-subject cross-site region of interest measurements for QSM and R2 * were highly correlated (R2 ≥ 0.94) and reproducible (mean ICC of 0.89 and 0.82 for QSM and R2 *, respectively). The mean cross-site SDw was 4.16 parts per billion (ppb) for QSM and 1.27 s-1 for R2 *. For within-site measurements of QSM and R2 *, the mean ICC was 0.97 and 0.87 and mean SDw was 2.36 ppb and 0.97 s-1 , respectively. The precision level is regionally dependent and is reduced in the frontal lobe, near brain edges, and in white matter regions. Cross-site QSM variability (mean SDw) was reduced up to 46% through postprocessing approaches, such as masking out less reliable regions, matching available echo timings and spatial resolution, avoiding the use of the nonconsistent magnitude contrast between scans in field estimation, and minimizing streaking artifacts.
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Affiliation(s)
- Nashwan Naji
- Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
| | - M Louis Lauzon
- Departments of Radiology and Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
- Seaman Family MR Research Centre, Foothills Medical Centre, Calgary, Alberta, Canada
| | - Peter Seres
- Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
| | - Emily Stolz
- Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
| | - Richard Frayne
- Departments of Radiology and Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
- Seaman Family MR Research Centre, Foothills Medical Centre, Calgary, Alberta, Canada
| | - Catherine Lebel
- Department of Radiology, Alberta Children's Hospital Research Institute and Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
| | - Christian Beaulieu
- Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
| | - Alan H Wilman
- Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
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Li C, Rusinek H, Chen J, Bokacheva L, Vedvyas A, Masurkar AV, Haacke EM, Wisniewski T, Ge Y. Reduced white matter venous density on MRI is associated with neurodegeneration and cognitive impairment in the elderly. Front Aging Neurosci 2022; 14:972282. [PMID: 36118685 PMCID: PMC9475309 DOI: 10.3389/fnagi.2022.972282] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Accepted: 08/15/2022] [Indexed: 11/13/2022] Open
Abstract
High-resolution susceptibility weighted imaging (SWI) provides unique contrast to small venous vasculature. The conspicuity of these mesoscopic veins, such as deep medullary veins in white matter, is subject to change from SWI venography when venous oxygenation in these veins is altered due to oxygenated blood susceptibility changes. The changes of visualization in small veins shows potential to depict regional changes of oxygen utilization and/or vascular density changes in the aging brain. The goal of this study was to use WM venous density to quantify small vein visibility in WM and investigate its relationship with neurodegenerative features, white matter hyperintensities (WMHs), and cognitive/functional status in elderly subjects (N = 137). WM venous density was significantly associated with neurodegeneration characterized by brain atrophy (β = 0.046± 0.01, p < 0.001), but no significant association was found between WM venous density and WMHs lesion load (p = 0.3963). Further analysis of clinical features revealed a negative trend of WM venous density with the sum-of-boxes of Clinical Dementia Rating and a significant association with category fluency (1-min animal naming). These results suggest that WM venous density on SWI can be used as a sensitive marker to characterize cerebral oxygen metabolism and different stages of cognitive and functional status in neurodegenerative diseases.
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Affiliation(s)
- Chenyang Li
- Department of Radiology, Center for Biomedical Imaging, NYU Grossman School of Medicine, New York, NY, United States
- Vilcek Institute of Graduate Biomedical Sciences, NYU Grossman School of Medicine, New York, NY, United States
| | - Henry Rusinek
- Department of Radiology, Center for Biomedical Imaging, NYU Grossman School of Medicine, New York, NY, United States
- Department of Psychiatry, NYU Grossman School of Medicine, New York, NY, United States
| | - Jingyun Chen
- Department of Radiology, Center for Biomedical Imaging, NYU Grossman School of Medicine, New York, NY, United States
- Department of Neurology, NYU Grossman School of Medicine, New York, NY, United States
| | - Louisa Bokacheva
- Department of Neurology, NYU Grossman School of Medicine, New York, NY, United States
| | - Alok Vedvyas
- Department of Neurology, NYU Grossman School of Medicine, New York, NY, United States
| | - Arjun V. Masurkar
- Department of Neurology, NYU Grossman School of Medicine, New York, NY, United States
| | - E. Mark Haacke
- Department of Radiology, Wayne State University School of Medicine, Detroit, MI, United States
| | - Thomas Wisniewski
- Department of Psychiatry, NYU Grossman School of Medicine, New York, NY, United States
- Department of Neurology, NYU Grossman School of Medicine, New York, NY, United States
- Departments of Pathology, NYU Grossman School of Medicine, New York, NY, United States
| | - Yulin Ge
- Department of Radiology, Center for Biomedical Imaging, NYU Grossman School of Medicine, New York, NY, United States
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Murdoch R, Stotesbury H, Hales PW, Kawadler JM, Kölbel M, Clark CA, Kirkham FJ, Shmueli K. A Comparison of MRI Quantitative Susceptibility Mapping and TRUST-Based Measures of Brain Venous Oxygen Saturation in Sickle Cell Anaemia. Front Physiol 2022; 13:913443. [PMID: 36105280 PMCID: PMC9465016 DOI: 10.3389/fphys.2022.913443] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 06/15/2022] [Indexed: 11/13/2022] Open
Abstract
In recent years, interest has grown in the potential for magnetic resonance imaging (MRI) measures of venous oxygen saturation (Yv) to improve neurological risk prediction. T2-relaxation-under-spin-tagging (TRUST) is an MRI technique which has revealed changes in Yv in patients with sickle cell anemia (SCA). However, prior studies comparing Yv in patients with SCA relative to healthy controls have reported opposing results depending on whether the calibration model, developed to convert blood T2 to Yv, is based on healthy human hemoglobin (HbA), bovine hemoglobin (HbBV) or sickle hemoglobin (HbS). MRI Quantitative Susceptibility Mapping (QSM) is an alternative technique that may hold promise for estimating Yv in SCA as blood magnetic susceptibility is linearly dependent upon Yv, and no significant difference has been found between the magnetic susceptibility of HbA and HbS. Therefore, the aim of this study was to compare estimates of Yv using QSM and TRUST with five published calibration models in healthy controls and patients with SCA. 17 patients with SCA and 13 healthy controls underwent MRI. Susceptibility maps were calculated from a multi-parametric mapping acquisition and Yv was calculated from the mean susceptibility in a region of interest in the superior sagittal sinus. TRUST estimates of T2, within a similar but much smaller region, were converted to Yv using five different calibration models. Correlation and Bland-Altman analyses were performed to compare estimates of Yv between TRUST and QSM methods. For each method, t-tests were also used to explore group-wise differences between patients with SCA and healthy controls. In healthy controls, significant correlations were observed between QSM and TRUST measures of Yv, while in SCA, there were no such correlations. The magnitude and direction of group-wise differences in Yv varied with method. The TRUST-HbBV and QSM methods suggested decreased Yv in SCA relative to healthy controls, while the TRUST-HbS (p < 0.01) and TRUST-HbA models suggested increased Yv in SCA as in previous studies. Further validation of all MRI measures of Yv, relative to ground truth measures such as O15 PET and jugular vein catheterization, is required in SCA before QSM or TRUST methods can be considered for neurological risk prediction.
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Affiliation(s)
- Russell Murdoch
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Hanne Stotesbury
- Developmental Neurosciences, UCL Great Ormond Street Institute of Child Health, London, United Kingdom
| | - Patrick W. Hales
- Developmental Neurosciences, UCL Great Ormond Street Institute of Child Health, London, United Kingdom
| | - Jamie M. Kawadler
- Developmental Neurosciences, UCL Great Ormond Street Institute of Child Health, London, United Kingdom
| | - Melanie Kölbel
- Developmental Neurosciences, UCL Great Ormond Street Institute of Child Health, London, United Kingdom
| | - Christopher A. Clark
- Developmental Neurosciences, UCL Great Ormond Street Institute of Child Health, London, United Kingdom
| | - Fenella J. Kirkham
- Developmental Neurosciences, UCL Great Ormond Street Institute of Child Health, London, United Kingdom
- Clinical and Experimental Sciences, University of Southampton, Southampton, United Kingdom
| | - Karin Shmueli
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
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Biondetti E, Karsa A, Grussu F, Battiston M, Yiannakas MC, Thomas DL, Shmueli K. Multi-echo quantitative susceptibility mapping: how to combine echoes for accuracy and precision at 3 Tesla. Magn Reson Med 2022; 88:2101-2116. [PMID: 35766450 PMCID: PMC9545116 DOI: 10.1002/mrm.29365] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 04/29/2022] [Accepted: 05/27/2022] [Indexed: 12/04/2022]
Abstract
Purpose To compare different multi‐echo combination methods for MRI QSM. Given the current lack of consensus, we aimed to elucidate how to optimally combine multi‐echo gradient‐recalled echo signal phase information, either before or after applying Laplacian‐base methods (LBMs) for phase unwrapping or background field removal. Methods Multi‐echo gradient‐recalled echo data were simulated in a numerical head phantom, and multi‐echo gradient‐recalled echo images were acquired at 3 Tesla in 10 healthy volunteers. To enable image‐based estimation of gradient‐recalled echo signal noise, 5 volunteers were scanned twice in the same session without repositioning. Five QSM processing pipelines were designed: 1 applied nonlinear phase fitting over TEs before LBMs; 2 applied LBMs to the TE‐dependent phase and then combined multiple TEs via either TE‐weighted or SNR‐weighted averaging; and 2 calculated TE‐dependent susceptibility maps via either multi‐step or single‐step QSM and then combined multiple TEs via magnitude‐weighted averaging. Results from different pipelines were compared using visual inspection; summary statistics of susceptibility in deep gray matter, white matter, and venous regions; phase noise maps (error propagation theory); and, in the healthy volunteers, regional fixed bias analysis (Bland–Altman) and regional differences between the means (nonparametric tests). Results Nonlinearly fitting the multi‐echo phase over TEs before applying LBMs provided the highest regional accuracy of χ and the lowest phase noise propagation compared to averaging the LBM‐processed TE‐dependent phase. This result was especially pertinent in high‐susceptibility venous regions. Conclusion For multi‐echo QSM, we recommend combining the signal phase by nonlinear fitting before applying LBMs. Click here for author‐reader discussions
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Affiliation(s)
- Emma Biondetti
- Institute for Advanced Biomedical Technologies, Department of Neuroscience, Imaging and Clinical Sciences, "D'Annunzio University" of Chieti-Pescara, Chieti, Italy.,Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Anita Karsa
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Francesco Grussu
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom.,Radiomics Group, Vall d'Hebron Institute of Oncology, Barcelona, Spain
| | - Marco Battiston
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Marios C Yiannakas
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - David L Thomas
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom.,Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Karin Shmueli
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
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