Evidence map›Paper›PMID 37831659›Full record

ArticleMagnetic resonance in medicine2024

Comparison of distortion correction preprocessing pipelines for DTI in the upper limb.

Ryckie G Wade, Winnie Tam, Antonia Perumal, Sophanit Pepple, Timothy T Griffiths, Robert Flather, Hamied A Haroon, David Shelley, Sven Plein, Grainne Bourke and 1 more

Open access · hybridAbstract read
In one paragraph

Article in Magnetic resonance in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.6field-weighted citation impact, top 16% 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

3 citing papers in PubMed, 7 citations in OpenAlex.

  1. Imaging neuroscience (Cambridge, Mass.) · 2025
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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

11 authors at 3 institutions in 1 country.

Ryckie G WadeLeeds Institute for Medical Research, University of Leeds, Leeds, UK.ORCID 0000-0001-8365-6547
Winnie TamLeeds Institute for Medical Research, University of Leeds, Leeds, UK.ORCID 0000-0002-6241-4810
Antonia PerumalLeeds Institute for Medical Research, University of Leeds, Leeds, UK.ORCID 0000-0001-5550-6349
Sophanit PeppleLeeds Institute for Medical Research, University of Leeds, Leeds, UK.ORCID 0000-0002-2782-3192
Timothy T GriffithsLeeds Institute for Medical Research, University of Leeds, Leeds, UK.ORCID 0000-0002-5993-8525
Robert FlatherLeeds Institute for Medical Research, University of Leeds, Leeds, UK.ORCID 0000-0003-0458-1802
Hamied A HaroonDivision of Psychology, Communication & Human Neuroscience, The University of Manchester, Manchester, UK.ORCID 0000-0002-3506-8430
David ShelleyLeeds Teaching Hospitals Trust, Leeds, UK.
Sven PleinLeeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK.ORCID 0000-0002-0997-4384
Grainne BourkeLeeds Institute for Medical Research, University of Leeds, Leeds, UK.ORCID 0000-0003-0059-8543
Irvin TehLeeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK.ORCID 0000-0002-6705-3129
University of Leeds · GBLeeds Teaching Hospitals NHS Trust · GBUniversity of Manchester · GB

Funding

Arthritis Research UK ARUK-21078British Heart Foundation BHF-SP/14/7/31351British Heart Foundation CH/16/2/32089Medical Research Council MR/M008991/1
6 · The paper itself

Abstract

purposeDTI characterizes tissue microstructure and provides proxy measures of nerve health. Echo-planar imaging is a popular method of acquiring DTI but is susceptible to various artifacts (e.g., susceptibility, motion, and eddy currents), which may be ameliorated via preprocessing. There are many pipelines available but limited data comparing their performance, which provides the rationale for this study.

methodsDTI was acquired from the upper limb of heathy volunteers at 3T in blip-up and blip-down directions. Data were independently corrected using (i) FSL's TOPUP & eddy, (ii) FSL's TOPUP, (iii) DSI Studio, and (iv) TORTOISE. DTI metrics were extracted from the median, radial, and ulnar nerves and compared (between pipelines) using mixed-effects linear regression. The geometric similarity of corrected b = 0 images and the slice matched T1-weighted (T1w) images were computed using the Sörenson-Dice coefficient.

resultsWithout preprocessing, the similarity coefficient of the blip-up and blip-down datasets to the T1w was 0·80 and 0·79, respectively. Preprocessing improved the geometric similarity by 1% with no difference between pipelines. Compared to TOPUP & eddy, DSI Studio and TORTOISE generated 2% and 6% lower estimates of fractional anisotropy, and 6% and 13% higher estimates of radial diffusivity, respectively. Estimates of anisotropy from TOPUP & eddy versus TOPUP were not different but TOPUP reduced radial diffusivity by 3%. The agreement of DTI metrics between pipelines was poor.

conclusionsPreprocessing DTI from the upper limb improves geometric similarity but the choice of the pipeline introduces clinically important variability in diffusion parameter estimates from peripheral nerves.

Indexed as

Diffusion Magnetic Resonance ImagingDiffusion Tensor ImagingEcho-Planar ImagingHumansImage Processing, Computer-AssistedPeripheral NervesUpper ExtremityanisotropydiffusiondiffusivitydistortionDTImagnetic resonancemediannervepre-processingradialulnar

Identifiers

PMID37831659
PMCPMC10952179
OpenAlexW4387613409

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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.