Evidence map›Paper›PMID 27721931›Full record

ArticleTechnology and innovation2016

REDUCING CSF PARTIAL VOLUME EFFECTS TO ENHANCE DIFFUSION TENSOR IMAGING METRICS OF BRAIN MICROSTRUCTURE.

Lauren E Salminen, Thomas E Conturo, Jacob D Bolzenius, Ryan P Cabeen, Erbil Akbudak, Robert H Paul

Open access · greenAbstract read
In one paragraph

Article in Technology and innovation, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
3.7field-weighted citation impact, top 7% 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

21 citing papers in PubMed, 37 citations in OpenAlex.

  1. Trial
  2. Review
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  5. Optimization of the Data Pattern and Analysis Algorithm for the T2-based Water Suppression Diffusion MRImaging (T2wsup-dMRI) Technique.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025
    Article
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  13. Diffusion MR Imaging with T2-based Water Suppression (T2wsup-dMRI).Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2022
    Article
  14. Review
  15. Article
  16. Article
  17. Estimating Brain Connectivity With Diffusion-Weighted Magnetic Resonance Imaging: Promise and Peril.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2020
    Review
  18. 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

6 authors at 4 institutions in 2 countries.

Lauren E SalminenDepartment of Psychology, University of Missouri - Saint Louis, St. Louis, MO, USA.
Thomas E ConturoMallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA.
Jacob D BolzeniusMissouri Institute of Mental Health, St. Louis, MO, USA.
Ryan P CabeenComputer Science Department, Brown University, Providence, RI, USA.
Erbil AkbudakMallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA.
Robert H PaulMissouri Institute of Mental Health, St. Louis, MO, USA.
Mallinckrodt (United States) · USMissouri Institute of Mental Health · USBrown University · USSaint Louis University · US

Funding

Washington University Institute of Clinical and Translational SciencesUL1TR000448 · NCATS · WASHINGTON UNIVERSITY · PI EVANOFF, BRADLEY A · 2012 to 2016
$41.4M
Neuromarkers of age-related cognitive declineR01NS052470 · NINDS · UNIVERSITY OF MISSOURI-ST. LOUIS · PI PAUL, ROBERT HARRIS · 2007 to 2011
$2.7M
TRACKING NEURONAL FIBERS IN LIVING HUMAN BRAIN BY MRIR01NS039538 · NINDS · WASHINGTON UNIVERSITY · PI CONTURO, THOMAS EDWARD · 2001 to 2005
$2.1M
BRAIN MICROSTRUCTURE & BEHAVIOR IN NEWLY-DIAGNOSED TODDLERS/PRESCHOOLERS WITH ASDR21MH105822 · NIMH · WASHINGTON UNIVERSITY · PI CONTURO, THOMAS EDWARD, STRAUSS, MARK · 2015 to 2016
$423k
NCATS NIH HHS UL1 TR000448NIMH NIH HHS R21 MH105822NINDS NIH HHS R01 NS039538NINDS NIH HHS R01 NS052470United States NIMHUnited States NINDS
6 · The paper itself

Abstract

Technological advances over recent decades now allow for in vivo observation of human brain tissue through the use of neuroimaging methods. While this field originated with techniques capable of capturing macrostructural details of brain anatomy, modern methods such as diffusion tensor imaging (DTI) that are now regularly implemented in research protocols have the ability to characterize brain microstructure. DTI has been used to reveal subtle micro-anatomical abnormalities in the prodromal phase ofº various diseases and also to delineate "normal" age-related changes in brain tissue across the lifespan. Nevertheless, imaging artifact in DTI remains a significant limitation for identifying true neural signatures of disease and brain-behavior relationships. Cerebrospinal fluid (CSF) contamination of brain voxels is a main source of error on DTI scans that causes partial volume effects and reduces the accuracy of tissue characterization. Several methods have been proposed to correct for CSF artifact though many of these methods introduce new limitations that may preclude certain applications. The purpose of this review is to discuss the complexity of signal acquisition as it relates to CSF artifact on DTI scans and review methods of CSF suppression in DTI. We will then discuss a technique that has been recently shown to effectively suppress the CSF signal in DTI data, resulting in fewer errors and improved measurement of brain tissue. This approach and related techniques have the potential to significantly improve our understanding of "normal" brain aging and neuropsychiatric and neurodegenerative diseases. Considerations for next-level applications are discussed.

Indexed as

CSF suppressionDTIMRIPartial volume effects

Identifiers

PMID27721931
PMCPMC5054978
OpenAlexW2524930294

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.