464-04-003 Registered Abstract

Does Denoising Reduce Cross-Vendor Variability in Diffusion MRI? A Travelling Heads Evaluation

Primary: Diffusion - Simulation/Validation

Secondary: Diffusion - Diffusion Analysis & Visualization

464-04-003 · Diffusion MRI: Registered and Clinical Abstracts · Tuesday, 12 May, 2:35 PM–3:30 PM · Digital Posters Row E

Keywords: Harmonisation Diffusion-weighted MRI Denoising MP-PCA Cross-vendor MRI

Accepted

Francesco D'Antonio ^1,2, Jose-Pedro Manzano-Patron^1,2, Olivier E Mougin³, Paul S Morgan^1,2,4, Stam Sotiropoulos^1,2, Shaun Warrington^1,2

¹Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom

²Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom

³Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom

⁴National Institute of Health & Care Research Nottingham Biomedical Research Centre, Nottingham University Hospitals NHS Trust, Nottingham, United Kingdom

Presenting Author: Francesco D'Antonio

Synopsis

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References

1. Miller KL, Alfaro-Almagro F, Bangerter NK, Thomas DL, Yacoub E, Xu J, et al. Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat Neurosci. 2016 Nov;19(11):1523–36.

2. Thompson PM, Stein JL, Medland SE, Hibar DP, Vasquez AA, Renteria ME, et al. The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data. Brain Imaging Behav. 2014 June 1;8(2):153–82.

3. Van Essen DC, Smith SM, Barch DM, Behrens TEJ, Yacoub E, Ugurbil K, et al. The WU-Minn human connectome project: an overview. Neuroimage. 2013;80:62–79.

4. Ades-Aron B, Coelho S, Lemberskiy G, Veraart J, Baete SH, Shepherd TM, et al. Denoising Improves Cross-Scanner and Cross-Protocol Test–Retest Reproducibility of Diffusion Tensor and Kurtosis Imaging. Hum Brain Mapp. 2025;46(4):e70142.

5. Warrington S, Ntata A, Mougin O, Campbell J, Torchi A, Craig M, et al. A resource for development and comparison of multimodal brain 3 T MRI harmonisation approaches. Imaging Neurosci. 2023 Dec;1:1–27.

6. Warrington S, Torchi A, Mougin O, Campbell J, Ntata A, Craig M, et al. A multi-site, multi-modal travelling-heads resource for brain MRI harmonisation. Sci Data. 2025 Apr 11;12(1):609.

7. Andersson JLR, Skare S, Ashburner J. How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. Neuroimage. 2003;20(2):870–88.

8. Mohammadi-Nejad AR, Pszczolkowski S, Auer D, Sotiropoulos S. Multi-modal neuroimaging pipelines for data preprocessing [Internet]. Zenodo; 2020. Available from: https://doi.org/10.5281/zenodo.3909526 [doi]

9. Andersson JLR, Sotiropoulos SN. An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. NeuroImage. 2016 Jan;125:1063–78.

10. Chen J, Ades-Aron B, Lee HH, Mehrin S, Pang M, Novikov DS, et al. Optimization and validation of the DESIGNER preprocessing pipeline for clinical diffusion MRI in white matter aging. Imaging Neurosci. 2024 Apr 8;2:imag–2–00125.

11. Ades-Aron B, Veraart J, Kochunov P, McGuire S, Sherman P, Kellner E, et al. Evaluation of the accuracy and precision of the diffusion parameter EStImation with Gibbs and NoisE removal pipeline. NeuroImage. 2018 Dec;183:532–43.

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http://echo.ismrm.org/p/ISMRM2026/464-04-003