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Multi-Centre Reproducibility of DTI and NODDI in White Matter Tracts Segmented Using TractFinder Across Three MRI Scanners of the Same Model.

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Paper

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The authors' code

Shell · 38 lines · 1.8 KB · no license

  1. #!/bin/bash -e
  3. studySite='' # define the study site
  4. studySubject='' # define the subject ID
  5. studyPath='' # define the folder path
  6. studyData=$studyPath/$studySubject/$studySite
  8. # 1. MP-PCA denoising
  9. # uses a dilated brain mask to improve speed
  10. dwi2mask $studyData/dwi.mif - | maskfilter - dilate $studyData/preproc_mask.mif -npass 5
  11. mrview $studyData/dwi.mif -roi.load $studyData/preproc_mask.mif -roi.opacity 0.4
  12. # make sure to edit the preproc mask if too small and save it
  13. dwidenoise $studyData/dwi.mif $studyData/denoise.mif -noise $studyData/noiselevel.mif -mask $studyData/preproc_mask.mif
  14. mrview $studyData/noiselevel.mif
  16. # 2. Gibbs ringing correction
  17. mrdegibbs $studyData/denoise.mif $studyData/degibbs.mif
  18. mrview $studyData/degibbs.mif
  20. # 3. Motion & distortion correction (FSL topup / eddy)
  21. dwiextract $studyData/dwi.mif - -bzero | mrmath - mean $studyData/mean_bzero.mif -axis 3
  22. # merges the b0 (extracted by fslroi above) and the b0 flip into a single file, in this case b0pair
  23. mrcat $studyData/mean_bzero.mif $studyData/negPE.mif $studyData/b0pair.mif
  24. dwifslpreproc $studyData/degibbs.mif $studyData/topup_eddy_done.mif -rpe_pair -se_epi $studyData/b0pair.mif -pe_dir AP
  25. # input is the denoised, degibbs'ed .mif file
  26. # runs FSL's topup then eddy tools
  27. mrview $studyData/topup_eddy_done.mif
  29. # 4. Bias field correction
  30. dwibiascorrect fsl $studyData/topup_eddy_done.mif $studyData/dwi_preproc.mif -mask $studyData/preproc_mask.mif -bias $studyData/biasfield.mif
  31. # performs B1 bias field correction
  32. # outputs corrected DWI dataset and bias field map
  33. mrview $studyData/biasfield.mif
  35. # 5. Mask
  36. dwi2mask $studyData/dwi_preproc.mif $studyData/mask.mif
  37. # check this output visually, overlays mask on preprocessed data with low opacity and random colour
  38. mrview $studyData/dwi_preproc.mif -roi.load $studyData/mask.mif -roi.opacity 0.3 &

preprocessing_pipeline.sh at commit b642271, no license · at the source

Overview

Authors: Agnieszka Sierhej1,2,3, Marta M Correia4, C John Evans5, Kiran K Seunarine1,3, Jonathan D Clayden1, Nadia A S Smith6,7, Matt G Hall2, Chris A Clark1,3
ORCID iDs: Agnieszka Sierhej, Marta M Correia, C John Evans, Kiran K Seunarine, Jonathan D Clayden, Nadia A S Smith, Matt G Hall, Chris A Clark
  1. UCL GOS Institute of Child Health, University College London, London, UK
  2. National Physical Laboratory, Teddington, UK
  3. Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK
  4. MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
  5. CUBRIC, School of Psychology, Cardiff University, Cardiff, UK
  6. TÜV SÜD UK, Warrington, UK
  7. Royal Surrey NHS Foundation Trust, Guildford, UK
Institutions: National Physical Laboratory (United Kingdom); Great Ormond Street Hospital (United Kingdom); Great Ormond Street Hospital for Children NHS Foundation Trust (United Kingdom); University College London (United Kingdom); MRC Cognition and Brain Sciences Unit (United Kingdom); University of Cambridge (United Kingdom); Cardiff University (United Kingdom); Royal Surrey NHS Foundation Trust (United Kingdom); TÜV SÜD (United Kingdom) (United Kingdom)
Journal: Human brain mapping, volume 47, issue 5, article e70491
Dates: received 28 August 2025; accepted 24 February 2026; published online 30 March 2026; in print April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1002/hbm.70491 · PMID 41913049 · PMCID PMC13140897 · OpenAlex W7144003577
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), methods / tools (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Preprocessing, fMRI & imaging
Keywords: diffusion MRI, DTI, multi‐centre, NODDI, reproducibility, TractFinder, white matter
MeSH: Brain*, Diffusion Tensor Imaging*, Image Processing, Computer-Assisted*, Neurites*, White Matter*, Adult, Diffusion Magnetic Resonance Imaging, Female, Humans, Male, Neural Pathways, Reproducibility of Results (* major topic)
Topic: Advanced Neuroimaging Techniques and Applications (Radiology, Nuclear Medicine and Imaging, Medicine), according to OpenAlex
Funding: Engineering and Physical Sciences Research Council (20icas0757); National Physical Laboratory; National Measurement System under the Data Science Modelling & Analytics Applications; Department for Science, Innovation and Technology; University College London; NIHR Great Ormond Street Hospital Biomedical Research Centre
Citations: cited by 1 paper (Europe PMC); 53 references in the paper

Abstract

Quantitative imaging biomarkers (QIBs) are objective measures derived from quantitative imaging that can differentiate pathological changes from healthy biological processes. Diffusion MRI parameters derived from Diffusion Tensor Imaging (DTI) and Neurite Orientation Dispersion and Density Imaging (NODDI) could serve as potential QIBs for studying both healthy neurodevelopment and various neurological conditions. However, quantitative neuroimaging studies often require large datasets collected across multiple scanners, which introduces variability. To ensure the reliability of multi‐centre studies, the inter‐centre reproducibility of DTI and NODDI parameters must be thoroughly assessed before data collection begins. Discrepancies between results reported by previous studies can be explained by other sources of variability. The inter‐scanner reproducibility of diffusion parameters needs to be determined when the other sources of variability, such as differences in acquisition parameters, processing and ROI segmentation are controlled for. We assess the reproducibility of DTI and NODDI parameters in clinically relevant white matter (WM) tracts across three scanners of the same model, ensuring consistency in the acquisition scheme and pre‐processing pipelines. WM tract regions of interest (ROIs) are automatically segmented to standardise the analysis. Additionally, we investigate ROI and signal‐to‐noise ratio differences to better understand the sources of variability in diffusion parameters. According to the Koo and Li classification system, our results demonstrate excellent reproducibility for fractional anisotropy and mean diffusivity across scanners of the same model (ICC ≥ 0.964) when using identical acquisition schemes, pre‐processing pipelines and automated ROI segmentation. NODDI orientation dispersion index and neurite density index exhibit a similar level of reproducibility (ICC ≥ 0.942 and ICC ≥ 0.911, respectively), while free water fraction (FWF) has ICC ≥ 0.862. However, statistically significant variability was observed in the FWF, specifically within the left inferior fronto‐occipital fasciculus (CoV 9.43%) and optic radiation (CoV 9.95%), even when scanning the same cohort across sites. If there is an error in the signal fraction in one compartment in the NODDI model, the signal fractions from other compartments may likely be misestimated. The reproducibility and variability of diffusion parameters reported in this study provide guidance for future QIB research involving datasets derived from multiple scanners. These findings can help determine whether observed changes in diffusion parameters reflect meaningful biological differences or are highly influenced by measurement variability.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above.

aga-sierhej/inter_scanner_reproducibility

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: b6422713dd2a83a75c89176b8dd905d37593317d, 21 December 2025
Languages: Shell (1)
Size: 1 file, 1 script
Software Heritage: not archived
Found in: “Data Availability Statement”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: MRtrix3 (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
1 file
At the source: github.com/aga-sierhej/inter_scanner_reproducibility/tree/main

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 1 script, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data Availability Statement

Research data are not shared. Pre‐processing pipeline script available: https://github.com/aga‐sierhej/inter_scanner_reproducibility/tree/main (https://github.com/aga-sierhej/inter_scanner_reproducibility/tree/main).

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 7 keywords, 12 MeSH terms, 6 funders, 50 references.

Cite

This paper

Sierhej, A., Correia, M. M., Evans, C. J., Seunarine, K. K., Clayden, J. D., Smith, N. A. S., Hall, M. G., & Clark, C. A. (2026). Multi-Centre Reproducibility of DTI and NODDI in White Matter Tracts Segmented Using TractFinder Across Three MRI Scanners of the Same Model. Human brain mapping, 47(5), e70491. https://doi.org/10.1002/hbm.70491

BibTeX

@article{sierhej2026multi,
author = {Sierhej, Agnieszka and Correia, Marta M and Evans, C John and Seunarine, Kiran K and Clayden, Jonathan D and Smith, Nadia A S and Hall, Matt G and Clark, Chris A},
title = {{Multi-Centre Reproducibility of DTI and NODDI in White Matter Tracts Segmented Using TractFinder Across Three MRI Scanners of the Same Model}},
journal = {Human brain mapping},
year = {2026},
month = apr,
volume = {47},
number = {5},
pages = {e70491},
publisher = {Wiley},
issn = {1065-9471},
doi = {10.1002/hbm.70491},
url = {https://doi.org/10.1002/hbm.70491},
pmid = {41913049},
pmcid = {PMC13140897}
}

RIS

TY - JOUR
AU - Sierhej, Agnieszka
AU - Correia, Marta M
AU - Evans, C John
AU - Seunarine, Kiran K
AU - Clayden, Jonathan D
AU - Smith, Nadia A S
AU - Hall, Matt G
AU - Clark, Chris A
TI - Multi-Centre Reproducibility of DTI and NODDI in White Matter Tracts Segmented Using TractFinder Across Three MRI Scanners of the Same Model
T2 - Human brain mapping
J2 - Hum Brain Mapp
PY - 2026
DA - 2026/04/01
VL - 47
IS - 5
SP - e70491
SN - 1065-9471
PB - Wiley
DO - 10.1002/hbm.70491
UR - https://doi.org/10.1002/hbm.70491
LA - en
ER -

CSL-JSON

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"container-title": "Human brain mapping",
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