Evidence map›Paper›PMID 37461418›Full record

ArticleArXiv2023

Recommended Implementation of Quantitative Susceptibility Mapping for Clinical Research in The Brain: A Consensus of the ISMRM Electro-Magnetic Tissue Properties Study Group.

QSM Consensus Organization Committee, Berkin Bilgic, Mauro Costagli, Kwok-Shing Chan, Jeff Duyn, Christian Langkammer, Jongho Lee, Xu Li, Chunlei Liu, José P Marques and 9 more

Open access · greenAbstract readPreprint
In one paragraph

Article in ArXiv, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 5 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

19 authors at 9 institutions in 8 countries.

QSM Consensus Organization Committee
Berkin BilgicMartinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, United States.
Mauro CostagliDepartment of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Sciences (DINOGMI), University of Genoa, Genoa, Italy.
Kwok-Shing ChanDonders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands.
Jeff DuynAdvanced MRI Section, NINDS, National Institutes of Health, Bethesda, MD, United States.
Christian LangkammerDepartment of Neurology, Medical University of Graz, Graz, Austria.
Jongho LeeDepartment of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea.
Xu LiRussell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States.
Chunlei LiuDepartment of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA.
José P MarquesDonders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands.
Carlos MilovicSchool of Electrical Engineering (EIE), Pontificia Universidad Catolica de Valparaiso, Valparaiso, Chile.
Simon RobinsonHigh Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Austria.
Ferdinand SchweserBuffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences at the University at Buffalo, Buffalo, NY, USA.
Karin ShmueliDepartment of Medical Physics and Biomedical Engineering, University College London, London, UK.
Pascal SpincemailleMRI Research Institute, Department of Radiology, Weill Cornell Medicine, New York, NY, United States.
Sina StraubDepartment of Radiology, Mayo Clinic, Jacksonville, FL, United States.
Peter van ZijlRussell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States.
Yi WangMRI Research Institute, Departments of Radiology and Biomedical Engineering, Cornell University, New York, NY, United States.
ISMRM Electro-Magnetic Tissue Properties Study Group
Kennedy Krieger Institute · USHarvard University · USCornell University · USNational Institutes of Health · USPontificia Universidad Católica de Valparaíso · CLBuffalo Psychiatric Center · USRadboud University Nijmegen · NLSeoul National University · KRUniversity at Buffalo, State University of New York · US

Funding

University of Buffalo Clinical and Translational Science Institute - Supplement SchulyerUL1TR001412 · NCATS · STATE UNIVERSITY OF NEW YORK AT BUFFALO · PI MURPHY, TIMOTHY F · 2015 to 2024
$33.8M
CONNECTOME 2.0: DEVELOPING THE NEXT GENERATION HUMAN MRI SCANNER FOR BRIDGING STUDIES OF THE MICRO-, MESO- AND MACRO-CONNECTOMEU01EB026996 · NIBIB · MASSACHUSETTS GENERAL HOSPITAL · PI BASSER, PETER J., HUANG, SUSIE YI · 2018 to 2022
$14.2M
MRI CORTICOGRAPHY: DEVELOPING NEXT GENERATION MICROSCALE HUMAN CORTEX MRI SCANNERU01EB025162 · NIBIB · UNIVERSITY OF CALIFORNIA BERKELEY · PI FEINBERG, DAVID ALAN, LIU, CHUNLEI · 2017 to 2021
$13.4M
Training and Dissemination CoreP41EB030006 · NIBIB · MASSACHUSETTS GENERAL HOSPITAL · PI Susie Yi Huang · 2020 to 2026
$10.9M
TRD 4: Platforms for multi-modal and multi-scale imaging dataP41EB031771 · NIBIB · HUGO W. MOSER RES INST KENNEDY KRIEGER · PI Andreia Vasconcellos Faria · 2021 to 2026
$9.9M
Robotically-actuated, low-noise, concurrent TMS-EEG-fMRI systemR01MH127104 · NIMH · UNIVERSITY OF CALIFORNIA BERKELEY · PI LIU, CHUNLEI, LUSTIG, MICHAEL · 2021 to 2024
$6.5M
Imaging brain iron and protein aggregation with MRI for assessing Alzheimer's disease pathology and progressionR01AG070826 · NIA · UNIVERSITY OF CALIFORNIA BERKELEY · PI GRINBERG, LEA TENENHOLZ, LIU, CHUNLEI · 2021 to 2025
$3.3M
Quantitative Mapping of Substantia Nigra Iron in Parkinson's DiseaseR01NS095562 · NINDS · WEILL MEDICAL COLL OF CORNELL UNIV · PI SHTILBANS, ALEXANDER, WANG, YI · 2016 to 2022
$3.1M
MRI technology for restoring brain image quality for pediatric patients with metallic orthodontic hardwareR01EB028797 · NIBIB · MASSACHUSETTS GENERAL HOSPITAL · PI Berkin Bilgic, Jason P Stockmann · 2020 to 2026
$3.0M
No-Gd MRI for Monitoring Disease Status in Multiple SclerosisR01NS105144 · NINDS · WEILL MEDICAL COLL OF CORNELL UNIV · PI GAUTHIER, SUSAN A, GUPTA, AJAY · 2018 to 2022
$2.8M
Harmonizing data acquisition, reconstruction, and analysis for reproducible, cross-vendor, open source MRIR01EB032378 · NIBIB · BRIGHAM AND WOMEN'S HOSPITAL · PI BILGIC, BERKIN, RATHI, YOGESH · 2022 to 2025
$2.7M
Contribution of Cerebral Iron Load to Cognitive Function in Older Adults with High Risk to Develop Alzheimer's DiseaseR01AG063842 · NIA · JOHNS HOPKINS UNIVERSITY · PI LI, XU, QIAO, YE · 2020 to 2024
$2.5M
NCATS NIH HHS UL1 TR001412NIA NIH HHS R01 AG063842NIA NIH HHS R01 AG070826NIBIB NIH HHS P41 EB030006NIBIB NIH HHS P41 EB031771NIBIB NIH HHS R01 EB028797NIBIB NIH HHS R01 EB032378NIBIB NIH HHS R03 EB031175NIBIB NIH HHS U01 EB025162NIBIB NIH HHS U01 EB026996NIMH NIH HHS R01 MH127104NINDS NIH HHS R01 NS095562NINDS NIH HHS R01 NS105144NINDS NIH HHS R01 NS114227
6 · The paper itself

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.

Indexed as

Clinical Brain ResearchData AcquisitionData AnalysisISMRM Study GroupMagnetic Resonance ImagingQuantitative Susceptibility Mapping

Identifiers

PMID37461418
PMCPMC10350101
OpenAlexW4384626018

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.