Francesco D'Antonio 1,2, Olivier E Mougin3, Steen Moeller4, Essa Yacoub4, Shaun Warrington1,2, Paul S Morgan1,2,5, Stam Sotiropoulos1,2
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
4Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, United States of America
5National Institute of Health & Care Research Nottingham Biomedical Research Centre, Nottingham University Hospitals NHS Trust, Nottingham, United Kingdom
Presenting Author: Francesco D'Antonio
References
1. 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.
2. 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.
3. Huynh K, Chang WT, Wu Y, Yap PT. Optimal shrinkage denoising breaks the noise floor in high-resolution diffusion MRI. Patterns. 2024 Apr;5(4):100954.
4. Powell E, Schneider T, Battiston M, Grussu F, Toosy A, Clayden JD, et al. SENSE EPI reconstruction with 2D phase error correction and channel-wise noise removal. Magn Reson Med. 2022;88(5):2157–66.
5. Zhang Z, Aygun E, Shih SF, Raman SS, Sung K, Wu HH. High-Resolution Prostate Diffusion MRI Using Eddy Current-Nulled Convex Optimized Diffusion Encoding and Random Matrix Theory-Based Denoising. Magma N Y N. 2024 Aug;37(4):603–19.
6. Manzano Patron JP, Moeller S, Andersson JLR, Ugurbil K, Yacoub E, Sotiropoulos SN. Denoising diffusion MRI: Considerations and implications for analysis. Imaging Neurosci. 2024 July;2:1–29.
7. París G, Pieciak T, Jones DK, Aja-Fernández S, Tristán-Vega A, Veraart J. Thermal noise lowers the accuracy of rotationally invariant harmonics of diffusion MRI data and their robustness to experimental variations. Magn Reson Med [Internet]. 2025 [cited 2025 Sept 16]; Available from: https://onlinelibrary.wiley.com/doi/abs/10.1002/mrm.70035
8. Veraart J, Fieremans E, Novikov DS. Diffusion MRI noise mapping using random matrix theory. Magn Reson Med. 2016;76(5):1582–93.
9. Veraart J, Novikov DS, Christiaens D, Ades-Aron B, Sijbers J, Fieremans E. Denoising of diffusion MRI using random matrix theory. Neuroimage. 2016;142:394–406.
10. 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.
11. Moeller S, Pisharady PK, Ramanna S, Lenglet C, Wu X, Dowdle L, et al. NOise reduction with DIstribution Corrected (NORDIC) PCA in dMRI with complex-valued parameter-free locally low-rank processing. NeuroImage. 2021 Feb;226:117539.
12. Eichner C, Cauley SF, Cohen-Adad J, Möller HE, Turner R, Setsompop K, et al. Real diffusion-weighted MRI enabling true signal averaging and increased diffusion contrast. NeuroImage. 2015 Nov 15;122:373–84.
13. Liu F, Feng J, Chen G, Shen D, Yap PT. Gaussianization of Diffusion MRI Data Using Spatially Adaptive Filtering. Med Image Anal. 2021 Feb;68:101828.
14. Liu F, Yang J, Feng M, Cui Z, He X, Zhou L, et al. Does perfect filtering really guarantee perfect phase correction for diffusion MRI data? Comput Med Imaging Graph. 2023 Jan;103:102160.
15. Pizzolato M, Gilbert G, Thiran JP, Descoteaux M, Deriche R. Adaptive phase correction of diffusion-weighted images. NeuroImage. 2020 Feb 1;206:116274.
16. Sprenger T, Sperl JI, Fernandez B, Haase A, Menzel MI. Real valued diffusion-weighted imaging using decorrelated phase filtering. Magn Reson Med. 2017;77(2):559–70.
17. D’Antonio F, Warrington S, Morgan P, Sotiropoulos S. End-to-end Complex Image Reconstruction Pipeline for GE Diffusion MRI Acquisitions. In: 2025 ISMRM & ISMRT Annual Meeting. Honolulu, Hawaii, USA; Abstract Number 0511.
18. Mohammadi-Nejad AR, Pszczolkowski S, Auer D, Sotiropoulos S. Multi-modal neuroimaging pipelines for data preprocessing. Zenodo; 2020. Available from: https://doi.org/10.5281/zenodo.3909526
[doi]
19. 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.
20. Bastiani M, Cottaar M, Fitzgibbon SP, Suri S, Alfaro-Almagro F, Sotiropoulos SN, et al. Automated quality control for within and between studies diffusion MRI data using a non-parametric framework for movement and distortion correction. NeuroImage. 2019 Jan;184:801–12.
21. Sotiropoulos SN, Moeller S, Jbabdi S, Xu J, Andersson JL, Auerbach EJ, et al. Effects of image reconstruction on fiber orientation mapping from multichannel diffusion MRI: Reducing the noise floor using SENSE. Magn Reson Med. 2013 Dec;70(6):1682–9.