Evidence map›Paper›PMID 27120169›Full record

ReviewNMR in biomedicine2017

Susceptibility tensor imaging (STI) of the brain.

Wei Li, Chunlei Liu, Timothy Q Duong, Peter C M van Zijl, Xu Li

Open access · greenAbstract readReview
In one paragraph

Review in NMR in biomedicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed
6.3field-weighted citation impact, top 3% of its field
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

31 citing papers in PubMed, 83 citations in OpenAlex.

  1. Article
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  5. Review
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  7. Vasculature-informed spatial smoothing of white matter functional magnetic resonance imaging.Proceedings of SPIE--the International Society for Optical Engineering · 2025
    Article
  8. Review
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  12. Toward a realistic in silico abdominal phantom for QSM.Magnetic resonance in medicine · 2023
    Article
  13. QSM Throughout the Body.Journal of magnetic resonance imaging : JMRI · 2023
    Review
  14. Article
  15. WaveSep: A Flexible Wavelet-Based Approach for Source Separation in Susceptibility Imaging.Machine learning in clinical neuroimaging : 6th international workshop, MLCN 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, proceedings. MLCN (Workshop) (6th : 2023 : Vancouver, B.C.) · 2023
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 4 institutions in 1 country.

Wei LiResearch Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA.
Chunlei LiuBrain Imaging and Analysis Center, School of Medicine, Duke University, Durham, NC, USA.
Timothy Q DuongResearch Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA.
Peter C M van ZijlF. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA.
Xu LiF. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA.
The University of Texas at San Antonio · USDuke University · USJohns Hopkins Medicine · USJohns Hopkins University · US

Funding

TR&D 4: Algorithms for functional and anatomical brain analysisP41EB015909 · NIBIB · HUGO W. MOSER RES INST KENNEDY KRIEGER · PI VAN ZIJL, PETER CM · 2012 to 2020
$13.1M
Ultra-Resolution Imaging of Brain Circuitry and its Development in Mental HealthR01MH096979 · NIMH · UNIVERSITY OF CALIFORNIA BERKELEY · PI LIU, CHUNLEI · 2013 to 2017
$1.8M
Susceptibility MRI as a marker for diagnosis and clinical disability in MSR01NS079653 · NINDS · DUKE UNIVERSITY · PI LIU, CHUNLEI · 2014 to 2015
$460k
NIBIB NIH HHS P41 EB015909NIMH NIH HHS R01 MH096979NINDS NIH HHS R01 NS079653
6 · The paper itself

Abstract

Susceptibility tensor imaging (STI) is a recently developed MRI technique that allows quantitative determination of orientation-independent magnetic susceptibility parameters from the dependence of gradient echo signal phase on the orientation of biological tissues with respect to the main magnetic field. By modeling the magnetic susceptibility of each voxel as a symmetric rank-2 tensor, individual magnetic susceptibility tensor elements as well as the mean magnetic susceptibility and magnetic susceptibility anisotropy can be determined for brain tissues that would still show orientation dependence after conventional scalar-based quantitative susceptibility mapping to remove such dependence. Similar to diffusion tensor imaging, STI allows mapping of brain white matter fiber orientations and reconstruction of 3D white matter pathways using the principal eigenvectors of the susceptibility tensor. In contrast to diffusion anisotropy, the main determinant factor of the susceptibility anisotropy in brain white matter is myelin. Another unique feature of the susceptibility anisotropy of white matter is its sensitivity to gadolinium-based contrast agents. Mechanistically, MRI-observed susceptibility anisotropy is mainly attributed to the highly ordered lipid molecules in the myelin sheath. STI provides a consistent interpretation of the dependence of phase and susceptibility on orientation at multiple scales. This article reviews the key experimental findings and physical theories that led to the development of STI, its practical implementations, and its applications for brain research. Copyright © 2016 John Wiley & Sons, Ltd.

Indexed as

AnimalsBrainDiffusion Tensor ImagingHumansImage EnhancementImage Interpretation, Computer-AssistedImaging, Three-DimensionalNeuroimagingReproducibility of ResultsSensitivity and SpecificityWhite Matterbrain imagingfiber trackinggradient echo MRIphase contrastquantitative susceptibility mappingsusceptibility tensor imagingwhite matter

Identifiers

PMID27120169
PMCPMC5083244
OpenAlexW2344176705

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.