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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: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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Renton AI, Dao TT, Johnstone T, Civier O, Sullivan RP, White DJ, Lyons P, Slade BM, Abbott DF, Amos TJ, Bollmann S, Botting A, Campbell MEJ, Chang J, Close TG, Dörig M, Eckstein K, Egan GF, Evas S, Flandin G, Garner KG, Garrido MI, Ghosh SS, Grignard M, Halchenko YO, Hannan AJ, Heinsfeld AS, Huber L, Hughes ME, Kaczmarzyk JR, Kasper L, Kuhlmann L, Lou K, Mantilla-Ramos YJ, Mattingley JB, Meier ML, Morris J, Narayanan A, Pestilli F, Puce A, Ribeiro FL, Rogasch NC, Rorden C, Schira MM, Shaw TB, Sowman PF, Spitz G, Stewart AW, Ye X, Zhu JD, Narayanan A, Bollmann S. Neurodesk: an accessible, flexible and portable data analysis environment for reproducible neuroimaging. Nat Methods 2024; 21:804-808. [PMID: 38191935 DOI: 10.1038/s41592-023-02145-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 11/27/2023] [Indexed: 01/10/2024]
Abstract
Neuroimaging research requires purpose-built analysis software, which is challenging to install and may produce different results across computing environments. The community-oriented, open-source Neurodesk platform ( https://www.neurodesk.org/ ) harnesses a comprehensive and growing suite of neuroimaging software containers. Neurodesk includes a browser-accessible virtual desktop, command-line interface and computational notebook compatibility, allowing for accessible, flexible, portable and fully reproducible neuroimaging analysis on personal workstations, high-performance computers and the cloud.
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Affiliation(s)
- Angela I Renton
- The University of Queensland, Queensland Brain Institute, St Lucia, Brisbane, Queensland, Australia.
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia.
| | - Thuy T Dao
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia
| | - Tom Johnstone
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn, Victoria, Australia
| | - Oren Civier
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn, Victoria, Australia
| | - Ryan P Sullivan
- The University of Sydney, School of Biomedical Engineering, Sydney, New South Wales, Australia
| | - David J White
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn, Victoria, Australia
| | - Paris Lyons
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn, Victoria, Australia
| | - Benjamin M Slade
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn, Victoria, Australia
| | - David F Abbott
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Toluwani J Amos
- School of Life Science and Technology, University of Electronic Science and Technology, Chengdu, China
| | - Saskia Bollmann
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia
| | - Andy Botting
- Australian Research Data Commons (ARDC), Sydney, New South Wales, Australia
| | - Megan E J Campbell
- School of Psychological Sciences, University of Newcastle, Newcastle, New South Wales, Australia
- Hunter Medical Research Institute Imaging Centre, Newcastle, New South Wales, Australia
| | - Jeryn Chang
- The University of Queensland, School of Biomedical Sciences, St Lucia, Brisbane, Queensland, Australia
| | - Thomas G Close
- The University of Sydney, School of Biomedical Engineering, Sydney, New South Wales, Australia
| | - Monika Dörig
- Integrative Spinal Research, Department of Chiropractic Medicine, Balgrist University Hospital, University of Zurich, Zurich, Switzerland
| | - Korbinian Eckstein
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia
| | - Gary F Egan
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
- Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia
| | - Stefanie Evas
- School of Psychology, University of Adelaide, Adelaide, South Australia, Australia
- Human Health, Health & Biosecurity, CSIRO, Adelaide, South Australia, Australia
| | - Guillaume Flandin
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Kelly G Garner
- School of Psychology, University of New South Wales, Sydney, New South Wales, Australia
- The University of Queensland, School of Psychology, St Lucia, Brisbane, Queensland, Australia
| | - Marta I Garrido
- Melbourne School of Psychological Sciences, he University of Melbourne, Melbourne, Victoria, Australia
- Graeme Clark Institute for Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia
| | - Satrajit S Ghosh
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Otolaryngology - Head and Neck Surgery, Harvard Medical School, Boston, MA, USA
| | - Martin Grignard
- GIGA CRC In-Vivo Imaging, University of Liège, Liège, Belgium
| | - Yaroslav O Halchenko
- Center for Open Neuroscience, Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
| | - Anthony J Hannan
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Anibal S Heinsfeld
- Department of Psychology, Center for Perceptual Systems, Institute for Neuroscience, Center For Learning and Memory, The University of Texas at Austin, Austin, TX, USA
| | - Laurentius Huber
- National Institute of Mental Health (NIMH), National Institutes Health, Bethesda, MD, USA
| | - Matthew E Hughes
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn, Victoria, Australia
| | - Jakub R Kaczmarzyk
- Department of Biomedical Informatics, Stony Brook University, New York, NY, USA
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, New York, NY, USA
| | - Lars Kasper
- BRAIN-TO Lab, Krembil Brain Institute, University Health Network, Toronto, Ontario, Canada
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Levin Kuhlmann
- Department of Data Science and AI, Faculty of Information Technology, Monash University, Melbourne, Victoria, Australia
| | - Kexin Lou
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia
- Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Yorguin-Jose Mantilla-Ramos
- Grupo Neuropsicología y Conducta (GRUNECO), Facultad de Medicina, Universidad de Antioquia, Medellín, Colombia
| | - Jason B Mattingley
- The University of Queensland, Queensland Brain Institute, St Lucia, Brisbane, Queensland, Australia
- The University of Queensland, School of Psychology, St Lucia, Brisbane, Queensland, Australia
| | - Michael L Meier
- Integrative Spinal Research, Department of Chiropractic Medicine, Balgrist University Hospital, University of Zurich, Zurich, Switzerland
| | - Jo Morris
- Australian Research Data Commons (ARDC), Sydney, New South Wales, Australia
| | - Akshaiy Narayanan
- School of Computer Science, The University of Auckland, Auckland, New Zealand
| | - Franco Pestilli
- Department of Psychology, Center for Perceptual Systems, Institute for Neuroscience, Center For Learning and Memory, The University of Texas at Austin, Austin, TX, USA
| | - Aina Puce
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Fernanda L Ribeiro
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia
| | - Nigel C Rogasch
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
- Discipline of Psychiatry, Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia
- Hopwood Centre for Neurobiology, Lifelong Health Theme, South Australian Health and Medical Research Institute (SAHMRI), Adelaide, South Australia, Australia
| | - Chris Rorden
- McCausland Center for Brain Imaging, Department of Psychology, University of South Carolina, Columbia, SC, USA
| | - Mark M Schira
- School of Psychology, University of Wollongong, Wollongong, New South Wales, Australia
| | - Thomas B Shaw
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia
- The University of Queensland, Centre for Advanced Imaging, St Lucia, Brisbane, Queensland, Australia
- Department of Neurology, Royal Brisbane and Women's Hospital, Brisbane, Queensland, Australia
| | - Paul F Sowman
- Macquarie University, School of Psychological Sciences, Sydney, New South Wales, Australia
| | - Gershon Spitz
- Department of Neuroscience, Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia
- Monash-Epworth Rehabilitation Research Centre, Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
| | - Ashley W Stewart
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Queensland, Australia
| | - Xincheng Ye
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia
| | - Judy D Zhu
- Macquarie University, School of Psychological Sciences, Sydney, New South Wales, Australia
| | - Aswin Narayanan
- The University of Queensland, Centre for Advanced Imaging, St Lucia, Brisbane, Queensland, Australia
| | - Steffen Bollmann
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia.
- The University of Queensland, Centre for Advanced Imaging, St Lucia, Brisbane, Queensland, Australia.
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Queensland, Australia.
- Queensland Digital Health Centre, The University of Queensland, Brisbane, Queensland, Australia.
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Roberts AG, Romano DJ, Şişman M, Dimov AV, Nguyen TD, Kovanlikaya I, Gauthier SA, Wang Y, Spincemaille P. Maximum spherical mean value filtering for whole-brain QSM. Magn Reson Med 2024; 91:1586-1597. [PMID: 38169132 DOI: 10.1002/mrm.29963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 10/30/2023] [Accepted: 11/19/2023] [Indexed: 01/05/2024]
Abstract
PURPOSE To develop a tissue field-filtering algorithm, called maximum spherical mean value (mSMV), for reducing shadow artifacts in QSM of the brain without requiring brain-tissue erosion. THEORY AND METHODS Residual background field is a major source of shadow artifacts in QSM. The mSMV algorithm filters large field-magnitude values near the border, where the maximum value of the harmonic background field is located. The effectiveness of mSMV for artifact removal was evaluated by comparing existing QSM algorithms in numerical brain simulation as well as using in vivo human data acquired from 11 healthy volunteers and 93 patients. RESULTS Numerical simulation showed that mSMV reduces shadow artifacts and improves QSM accuracy. Better shadow reduction, as demonstrated by lower QSM variation in the gray matter and higher QSM image quality score, was also observed in healthy subjects and in patients with hemorrhages, stroke, and multiple sclerosis. CONCLUSION The mSMV algorithm allows QSM maps that are substantially equivalent to those obtained using SMV-filtered dipole inversion without eroding the volume of interest.
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Affiliation(s)
- Alexandra G Roberts
- Department of Electrical and Computer Engineering, Cornell University, Ithaca, New York, USA
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Dominick J Romano
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, USA
| | - Mert Şişman
- Department of Electrical and Computer Engineering, Cornell University, Ithaca, New York, USA
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - 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
| | - Ilhami Kovanlikaya
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Susan A Gauthier
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
- Department of Neurology, Weill Cornell Medicine, New York, New York, USA
| | - Yi Wang
- Department of Electrical and Computer Engineering, Cornell University, Ithaca, New York, USA
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, USA
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Domínguez D JF, Stewart A, Burmester A, Akhlaghi H, O'Brien K, Bollmann S, Caeyenberghs K. Improving quantitative susceptibility mapping for the identification of traumatic brain injury neurodegeneration at the individual level. Z Med Phys 2024:S0939-3889(24)00001-1. [PMID: 38336583 DOI: 10.1016/j.zemedi.2024.01.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 12/19/2023] [Accepted: 01/07/2024] [Indexed: 02/12/2024]
Abstract
BACKGROUND Emerging evidence suggests that traumatic brain injury (TBI) is a major risk factor for developing neurodegenerative disease later in life. Quantitative susceptibility mapping (QSM) has been used by an increasing number of studies in investigations of pathophysiological changes in TBI. However, generating artefact-free quantitative susceptibility maps in brains with large focal lesions, as in the case of moderate-to-severe TBI (ms-TBI), is particularly challenging. To address this issue, we utilized a novel two-pass masking technique and reconstruction procedure (two-pass QSM) to generate quantitative susceptibility maps (QSMxT; Stewart et al., 2022, Magn Reson Med.) in combination with the recently developed virtual brain grafting (VBG) procedure for brain repair (Radwan et al., 2021, NeuroImage) to improve automated delineation of brain areas. We used QSMxT and VBG to generate personalised QSM profiles of individual patients with reference to a sample of healthy controls. METHODS Chronic ms-TBI patients (N = 8) and healthy controls (N = 12) underwent (multi-echo) GRE, and anatomical MRI (MPRAGE) on a 3T Siemens PRISMA scanner. We reconstructed the magnetic susceptibility maps using two-pass QSM from QSMxT. We then extracted values of magnetic susceptibility in grey matter (GM) regions (following brain repair via VBG) across the whole brain and determined if they deviate from a reference healthy control group [Z-score < -3.43 or > 3.43, relative to the control mean], with the aim of obtaining personalised QSM profiles. RESULTS Using two-pass QSM, we achieved susceptibility maps with a substantial increase in quality and reduction in artefacts irrespective of the presence of large focal lesions, compared to single-pass QSM. In addition, VBG minimised the loss of GM regions and exclusion of patients due to failures in the region delineation step. Our findings revealed deviations in magnetic susceptibility measures from the HC group that differed across individual TBI patients. These changes included both increases and decreases in magnetic susceptibility values in multiple GM regions across the brain. CONCLUSIONS We illustrate how to obtain magnetic susceptibility values at the individual level and to build personalised QSM profiles in ms-TBI patients. Our approach opens the door for QSM investigations in more severely injured patients. Such profiles are also critical to overcome the inherent heterogeneity of clinical populations, such as ms-TBI, and to characterize the underlying mechanisms of neurodegeneration at the individual level more precisely. Moreover, this new personalised QSM profiling could in the future assist clinicians in assessing recovery and formulating a neuroscience-guided integrative rehabilitation program tailored to individual TBI patients.
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Affiliation(s)
- Juan F Domínguez D
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Australia.
| | - Ashley Stewart
- School of Information Technology and Electrical Engineering, Faculty of Engineering, Architecture, and Information Technology, The University of Queensland, Brisbane, Australia
| | - Alex Burmester
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Australia
| | - Hamed Akhlaghi
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Australia; Department of Emergency Medicine, St. Vincent's Hospital, Melbourne, Australia
| | - Kieran O'Brien
- Siemens Healthcare Pty Ltd, Brisbane, Queensland, Australia
| | - Steffen Bollmann
- School of Information Technology and Electrical Engineering, Faculty of Engineering, Architecture, and Information Technology, The University of Queensland, Brisbane, Australia; Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia
| | - Karen Caeyenberghs
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Australia
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Robinson SD, Bachrata B, Eckstein K, Bollmann S, Bollmann S, Hodono S, Cloos M, Tourell M, Jin J, O'Brien K, Reutens DC, Trattnig S, Enzinger C, Barth M. Improved dynamic distortion correction for fMRI using single-echo EPI and a readout-reversed first image (REFILL). Hum Brain Mapp 2023; 44:5095-5112. [PMID: 37548414 PMCID: PMC10502646 DOI: 10.1002/hbm.26440] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 06/01/2023] [Accepted: 07/12/2023] [Indexed: 08/08/2023] Open
Abstract
The boundaries between tissues with different magnetic susceptibilities generate inhomogeneities in the main magnetic field which change over time due to motion, respiration and system instabilities. The dynamically changing field can be measured from the phase of the fMRI data and corrected. However, methods for doing so need multi-echo data, time-consuming reference scans and/or involve error-prone processing steps, such as phase unwrapping, which are difficult to implement robustly on the MRI host. The improved dynamic distortion correction method we propose is based on the phase of the single-echo EPI data acquired for fMRI, phase offsets calculated from a triple-echo, bipolar reference scan of circa 3-10 s duration using a method which avoids the need for phase unwrapping and an additional correction derived from one EPI volume in which the readout direction is reversed. This Reverse-Encoded First Image and Low resoLution reference scan (REFILL) approach is shown to accurately measure B0 as it changes due to shim, motion and respiration, even with large dynamic changes to the field at 7 T, where it led to a > 20% increase in time-series signal to noise ratio compared to data corrected with the classic static approach. fMRI results from REFILL-corrected data were free of stimulus-correlated distortion artefacts seen when data were corrected with static field mapping. The method is insensitive to shim changes and eddy current differences between the reference scan and the fMRI time series, and employs calculation steps that are simple and robust, allowing most data processing to be performed in real time on the scanner image reconstruction computer. These improvements make it feasible to routinely perform dynamic distortion correction in fMRI.
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Affiliation(s)
- Simon Daniel Robinson
- Centre of Advanced ImagingUniversity of QueenslandBrisbaneAustralia
- Department of NeurologyMedical University of GrazGrazAustria
- High Field MR Centre, Department of Biomedical Imaging and Image‐Guided TherapyMedical University of ViennaViennaAustria
- Karl Landsteiner Institute for Clinical Molecular MR in Musculoskeletal ImagingViennaAustria
| | - Beata Bachrata
- High Field MR Centre, Department of Biomedical Imaging and Image‐Guided TherapyMedical University of ViennaViennaAustria
- Karl Landsteiner Institute for Clinical Molecular MR in Musculoskeletal ImagingViennaAustria
- Department of Medical EngineeringCarinthia University of Applied SciencesKlagenfurtAustria
| | - Korbinian Eckstein
- High Field MR Centre, Department of Biomedical Imaging and Image‐Guided TherapyMedical University of ViennaViennaAustria
| | - Saskia Bollmann
- Centre of Advanced ImagingUniversity of QueenslandBrisbaneAustralia
| | - Steffen Bollmann
- School of Information Technology and Electrical EngineeringThe University of QueenslandBrisbaneAustralia
| | - Shota Hodono
- Centre of Advanced ImagingUniversity of QueenslandBrisbaneAustralia
- ARC Training Centre for Innovation in Biomedical Imaging Technology (CIBIT)The University of QueenslandBrisbaneAustralia
| | - Martijn Cloos
- Centre of Advanced ImagingUniversity of QueenslandBrisbaneAustralia
- ARC Training Centre for Innovation in Biomedical Imaging Technology (CIBIT)The University of QueenslandBrisbaneAustralia
| | - Monique Tourell
- Centre of Advanced ImagingUniversity of QueenslandBrisbaneAustralia
- Siemens Healthcare Pty Ltd.BrisbaneAustralia
| | - Jin Jin
- Siemens Healthcare Pty Ltd.BrisbaneAustralia
| | | | - David C. Reutens
- Centre of Advanced ImagingUniversity of QueenslandBrisbaneAustralia
- ARC Training Centre for Innovation in Biomedical Imaging Technology (CIBIT)The University of QueenslandBrisbaneAustralia
| | - Siegfried Trattnig
- High Field MR Centre, Department of Biomedical Imaging and Image‐Guided TherapyMedical University of ViennaViennaAustria
| | | | - Markus Barth
- Centre of Advanced ImagingUniversity of QueenslandBrisbaneAustralia
- School of Information Technology and Electrical EngineeringThe University of QueenslandBrisbaneAustralia
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6
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Coray RC, Berberat J, Zimmermann J, Seifritz E, Stock AK, Beste C, Cole DM, Unschuld PG, Quednow BB. Striatal Iron Deposition in Recreational MDMA (Ecstasy) Users. Biol Psychiatry Cogn Neurosci Neuroimaging 2023; 8:956-966. [PMID: 36848948 DOI: 10.1016/j.bpsc.2023.02.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 12/30/2022] [Accepted: 02/17/2023] [Indexed: 02/27/2023]
Abstract
BACKGROUND The common club drug MDMA (also known as ecstasy) enhances mood, sensory perception, energy, sociability, and euphoria. While MDMA has been shown to produce neurotoxicity in animal models, research on its potential neurotoxic effects in humans is inconclusive and has focused primarily on the serotonin system. METHODS We investigated 34 regular, largely pure MDMA users for signs of premature neurodegenerative processes in the form of increased iron load in comparison to a group of 36 age-, sex-, and education-matched MDMA-naïve control subjects. We used quantitative susceptibility mapping, a novel tool able to detect even small tissue (nonheme) iron accumulations. Cortical and relevant subcortical gray matter structures were grouped into 8 regions of interest and analyzed. RESULTS Significantly increased iron deposition in the striatum was evident in the MDMA user group. The effect survived correction for multiple comparisons and remained after controlling for relevant confounding factors, including age, smoking, and stimulant co-use. Although no significant linear relationship between measurements of the amounts of MDMA intake (hair analysis and self-reports) and quantitative susceptibility mapping values was observed, increased striatal iron deposition might nevertheless point to MDMA-induced neurotoxic processes. Additional factors (hyperthermia and simultaneous co-use of other substances) that possibly amplify neurotoxic effects of MDMA during the state of acute intoxication are discussed. CONCLUSIONS The demonstrated increased striatal iron accumulation may indicate that regular MDMA users have an increased risk potential for neurodegenerative diseases with progressing age.
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Affiliation(s)
- Rebecca C Coray
- Experimental and Clinical Pharmacopsychology, Department of Psychiatry, Psychotherapy, and Psychosomatics, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland; Neuroscience Center Zurich, Eidgenössische Technische Hochschule Zurich and University of Zurich, Zurich, Switzerland.
| | - Jatta Berberat
- Geriatric Psychiatry, Department of Psychiatry, University Hospitals of Geneva, University of Geneva, Geneva, Switzerland; Institute of Neuroradiology, Kantonsspital Aarau, Aarau, Switzerland
| | - Josua Zimmermann
- Experimental and Clinical Pharmacopsychology, Department of Psychiatry, Psychotherapy, and Psychosomatics, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland; Neuroscience Center Zurich, Eidgenössische Technische Hochschule Zurich and University of Zurich, Zurich, Switzerland
| | - Erich Seifritz
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Ann-Kathrin Stock
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Dresden, Germany
| | - Christian Beste
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Dresden, Germany
| | - David M Cole
- Experimental and Clinical Pharmacopsychology, Department of Psychiatry, Psychotherapy, and Psychosomatics, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland; Neuroscience Center Zurich, Eidgenössische Technische Hochschule Zurich and University of Zurich, Zurich, Switzerland; Translational Psychiatry Lab, University Psychiatric Clinics Basel, University of Basel, Basel, Switzerland
| | - Paul G Unschuld
- Geriatric Psychiatry, Department of Psychiatry, University Hospitals of Geneva, University of Geneva, Geneva, Switzerland
| | - Boris B Quednow
- Experimental and Clinical Pharmacopsychology, Department of Psychiatry, Psychotherapy, and Psychosomatics, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland; Neuroscience Center Zurich, Eidgenössische Technische Hochschule Zurich and University of Zurich, Zurich, Switzerland
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7
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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 S, 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. ArXiv 2023:arXiv:2307.02306v1. [PMID: 37461418 PMCID: PMC10350101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 07/22/2023]
Abstract
This article provides recommendations for implementing quantitative susceptibility mapping (QSM) for clinical brain research. It is a consensus of the ISMRM 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 give rise to the need in the neuroimaging community for guidelines on implementation. This article describes relevant considerations and provides specific implementation recommendations for all steps in QSM data acquisition, processing, analysis, and presentation in scientific publications. We recommend that data be acquired using a monopolar 3D multi-echo GRE sequence, that phase images be saved and exported in DICOM format and unwrapped using an exact unwrapping approach. Multi-echo images should be combined before background removal, and a brain mask created using a brain extraction tool with the incorporation of phase-quality-based masking. Background fields should be removed within the brain mask 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 whole brain as a region of interest in the analysis, and QSM results should be reported with - as a minimum - the acquisition and processing specifications listed in the last section of the article. These recommendations should facilitate clinical QSM research and lead to increased harmonization in 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, MA, United States
| | - 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
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Jeff Duyn
- Advanced MRI Section, NINDS, National Institutes of Health, Bethesda, MD, United States
| | | | - 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, MD, United States
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Chunlei Liu
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA, 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 Robinson
- High Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Austria
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences at the University at Buffalo, Buffalo, NY, USA
- Center for Biomedical Imaging, Clinical and Translational Science Institute at the University at Buffalo, Buffalo, NY, United States
| | - 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, NY, United States
| | - Sina Straub
- Department of Radiology, Mayo Clinic, Jacksonville, FL, United States
| | - Peter van Zijl
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Yi Wang
- MRI Research Institute, Departments of Radiology and Biomedical Engineering, Cornell University, New York, NY, United States
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8
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Renton AI, Dao TT, Johnstone T, Civier O, Sullivan RP, White DJ, Lyons P, Slade BM, Abbott DF, Amos TJ, Bollmann S, Botting A, Campbell MEJ, Chang J, Close TG, Eckstein K, Egan GF, Evas S, Flandin G, Garner KG, Garrido MI, Ghosh SS, Grignard M, Hannan AJ, Huber R, Kaczmarzyk JR, Kasper L, Kuhlmann L, Lou K, Mantilla-Ramos YJ, Mattingley JB, Morris J, Narayanan A, Pestilli F, Puce A, Ribeiro FL, Rogasch NC, Rorden C, Schira M, Shaw TB, Sowman PF, Spitz G, Stewart A, Ye X, Zhu JD, Hughes ME, Narayanan A, Bollmann S. Neurodesk: An accessible, flexible, and portable data analysis environment for reproducible neuroimaging. Res Sq 2023:rs.3.rs-2649734. [PMID: 36993557 PMCID: PMC10055538 DOI: 10.21203/rs.3.rs-2649734/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Neuroimaging data analysis often requires purpose-built software, which can be challenging to install and may produce different results across computing environments. Beyond being a roadblock to neuroscientists, these issues of accessibility and portability can hamper the reproducibility of neuroimaging data analysis pipelines. Here, we introduce the Neurodesk platform, which harnesses software containers to support a comprehensive and growing suite of neuroimaging software (https://www.neurodesk.org/). Neurodesk includes a browser-accessible virtual desktop environment and a command line interface, mediating access to containerized neuroimaging software libraries on various computing platforms, including personal and high-performance computers, cloud computing and Jupyter Notebooks. This community-oriented, open-source platform enables a paradigm shift for neuroimaging data analysis, allowing for accessible, flexible, fully reproducible, and portable data analysis pipelines.
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Affiliation(s)
- Angela I. Renton
- The University of Queensland, Queensland Brain Institute, St Lucia 4072, Australia
- The University of Queensland, School of Information Technology and Electrical Engineering, St Lucia 4072, Australia
| | - Thuy T. Dao
- The University of Queensland, School of Information Technology and Electrical Engineering, St Lucia 4072, Australia
| | - Tom Johnstone
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn 3122, Australia
| | - Oren Civier
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn 3122, Australia
| | - Ryan P. Sullivan
- The University of Sydney, School of Biomedical Engineering, Sydney, Australia
| | - David J. White
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn 3122, Australia
| | - Paris Lyons
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn 3122, Australia
| | - Benjamin M. Slade
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn 3122, Australia
| | - David F. Abbott
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Victoria, Australia
| | - Toluwani J. Amos
- School of Life Science and Technology, University of Electronic Science and Technology, China
| | - Saskia Bollmann
- The University of Queensland, School of Information Technology and Electrical Engineering, St Lucia 4072, Australia
| | - Andy Botting
- Australian Research Data Commons (ARDC), Australia
| | - Megan E. J. Campbell
- School of Psychological Sciences, University of Newcastle, Australia
- Hunter Medical Research Institute Imaging Centre, Newcastle, Australia
| | - Jeryn Chang
- The University of Queensland, School of Biomedical Sciences, St Lucia 4072, Australia
| | - Thomas G. Close
- The University of Sydney, School of Biomedical Engineering, Sydney, Australia
| | - Korbinian Eckstein
- The University of Queensland, School of Information Technology and Electrical Engineering, St Lucia 4072, Australia
| | - Gary F. Egan
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Victoria, Australia
- Monash Biomedical Imaging, Monash University, Victoria, Australia
| | - Stefanie Evas
- School of Psychology, University of Adelaide, Adelaide, 5000, Australia
- Human Health, Health & Biosecurity, CSIRO, Adelaide, 5000, Australia
| | - Guillaume Flandin
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Kelly G. Garner
- The University of Queensland, Queensland Brain Institute, St Lucia 4072, Australia
- The University of Queensland, School of Psychology, St Lucia 4072, Australia
| | - Marta I. Garrido
- Melbourne School of Psychological Sciences, The University of Melbourne
- Graeme Clark Institute for Biomedical Engineering, The University of Melbourne
| | - Satrajit S. Ghosh
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Otolaryngology - Head and Neck Surgery, Harvard Medical School, Boston, MA, USA
| | - Martin Grignard
- GIGA CRC In-Vivo Imaging, University of Liege, Liege, Belgium
| | - Anthony J. Hannan
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Victoria, Australia
| | - Renzo Huber
- Functional Magnetic Resonance Imaging Core Facility (FMRIF), National Institute of Mental Health (NIMH), USA
| | - Jakub R. Kaczmarzyk
- Medical Scientist Training Program, Stony Brook University, Stony Brook, NY, United States of America
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, United States of America
| | - Lars Kasper
- Techna Institute, University Health Network, Toronto, Canada
| | - Levin Kuhlmann
- Department of Data Science and AI, Faculty of Information Technology, Monash University, Clayton VIC 3800, Australia
| | - Kexin Lou
- The University of Queensland, School of Information Technology and Electrical Engineering, St Lucia 4072, Australia
| | | | - Jason B. Mattingley
- The University of Queensland, Queensland Brain Institute, St Lucia 4072, Australia
- The University of Queensland, School of Psychology, St Lucia 4072, Australia
| | - Jo Morris
- Australian Research Data Commons (ARDC), Australia
| | | | - Franco Pestilli
- Department of Psychology, Center for Perceptual Systems, Center for Theoretical and Computational Neuroscience, Center on Aging and Population Sciences, Center for Learning and Memory, The University of Texas at Austin, 108 E Dean Keeton St, Austin, TX 78712, USA
| | - Aina Puce
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Fernanda L. Ribeiro
- The University of Queensland, School of Information Technology and Electrical Engineering, St Lucia 4072, Australia
| | - Nigel C. Rogasch
- Discipline of Psychiatry, Adelaide Medical School, University of Adelaide, Australia
- Hopwood Centre for Neurobiology, Lifelong Health Theme, South Australian Health and Medical Research Institute (SAHMRI), Adelaide, SA, Australia
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Victoria, Australia
| | - Chris Rorden
- McCausland Center for Brain Imaging, Department of Psychology, University of South Carolina, Columbia SC, 29208, USA
| | - Mark Schira
- School of Psychology, University of Wollongong, Wollongong, 2522, Australia
| | - Thomas B. Shaw
- The University of Queensland, School of Information Technology and Electrical Engineering, St Lucia 4072, Australia
- The University of Queensland, Centre for Advanced Imaging, St Lucia 4072, Australia
- Department of Neurology, Royal Brisbane and Women's Hospital, Brisbane, Australia
| | - Paul F. Sowman
- Macquarie University, School of Psychological Sciences, North Ryde 2112, Australia
| | - Gershon Spitz
- Department of Neuroscience, Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Australia
- Monash-Epworth Rehabilitation Research Centre, Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, 3168, Australia
| | - Ashley Stewart
- The University of Queensland, School of Information Technology and Electrical Engineering, St Lucia 4072, Australia
| | - Xincheng Ye
- The University of Queensland, School of Information Technology and Electrical Engineering, St Lucia 4072, Australia
| | - Judy D. Zhu
- Macquarie University, School of Psychological Sciences, North Ryde 2112, Australia
| | - Matthew E. Hughes
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn 3122, Australia
| | - Aswin Narayanan
- The University of Queensland, Centre for Advanced Imaging, St Lucia 4072, Australia
| | - Steffen Bollmann
- The University of Queensland, School of Information Technology and Electrical Engineering, St Lucia 4072, Australia
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9
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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 Biomed 2022; 35:e4788. [PMID: 35704837 DOI: 10.1002/nbm.4788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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