Evidence map›Paper›PMID 32435181›Full record

ReviewFrontiers in neuroscience2020

Harmonization of Brain Diffusion MRI: Concepts and Methods.

Maíra Siqueira Pinto, Roberto Paolella, Thibo Billiet, Pieter Van Dyck, Pieter-Jan Guns, Ben Jeurissen, Annemie Ribbens, Arnold J den Dekker, Jan Sijbers

Abstract readReview
In one paragraph

Review in Frontiers in neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 89 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
89citing papers in PubMed, 2 pooled it
–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

89 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Facial Nerve Tractography of Vestibular Schwannomas: A Systematic Review of MR Acquisition and Analysis Pipelines.Journal of neuroimaging : official journal of the American Society of Neuroimaging
    Pooled it
  3. Article
  4. A Static Phantom for Harmonization of Hyperpolarized [1-Magnetic resonance in medicine · 2026
    Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Probabilistic mapping and automated segmentation of human brainstem white matter bundles.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  10. Personalized White Matter Bundle Segmentation for Early Childhood.Proceedings of SPIE--the International Society for Optical Engineering · 2026
    Article
  11. Article
  12. Review
  13. Article
  14. Article
  15. Article
  16. Article
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  19. Article
  20. Article

29 more citing papers are in PubMed but not listed here.

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

9 authors.

Maíra Siqueira PintoDepartment of Radiology, Antwerp University Hospital, University of Antwerp, Antwerp, Belgium.
Roberto Paolellaimec-Vision Lab, University of Antwerp, Antwerp, Belgium.
Thibo BillietIcometrix, Leuven, Belgium.
Pieter Van DyckDepartment of Radiology, Antwerp University Hospital, University of Antwerp, Antwerp, Belgium.
Pieter-Jan GunsPhysiopharmacology, University of Antwerp, Antwerp, Belgium.
Ben Jeurissenimec-Vision Lab, University of Antwerp, Antwerp, Belgium.
Annemie RibbensIcometrix, Leuven, Belgium.
Arnold J den Dekkerimec-Vision Lab, University of Antwerp, Antwerp, Belgium.
Jan Sijbersimec-Vision Lab, University of Antwerp, Antwerp, Belgium.

Funding

Wellcome Trust
6 · The paper itself

Abstract

MRI diffusion data suffers from significant inter- and intra-site variability, which hinders multi-site and/or longitudinal diffusion studies. This variability may arise from a range of factors, such as hardware, reconstruction algorithms and acquisition settings. To allow a reliable comparison and joint analysis of diffusion data across sites and over time, there is a clear need for robust data harmonization methods. This review article provides a comprehensive overview of diffusion data harmonization concepts and methods, and their limitations. Overall, the methods for the harmonization of multi-site diffusion images can be categorized in two main groups: diffusion parametric map harmonization (DPMH) and diffusion weighted image harmonization (DWIH). Whereas DPMH harmonizes the diffusion parametric maps (e.g., FA, MD, and MK), DWIH harmonizes the diffusion-weighted images. Defining a gold standard harmonization technique for dMRI data is still an ongoing challenge. Nevertheless, in this paper we provide two classification tools, namely a feature table and a flowchart, which aim to guide the readers in selecting an appropriate harmonization method for their study.

Indexed as

diffusion MRIharmonizationinter-scannermulti-sitenormalizationreview

Identifiers

PMID32435181
PMCPMC7218137

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.