Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
464-02-007 ISMRM Abstract

Green-dMRIPrep: diffusion MRI preprocessing pipeline with integrated QC, energy tracking, and carbon emissions auditing

Primary: Diffusion - Diffusion Software
Secondary: Analysis Methods - Software Tools
464-02-007 · Validating Diffusion MRI: Reproducibility, Simulations, and Phantoms · Tuesday, 12 May, 9:15 AM–10:10 AM · Digital Posters Row E
Keywords: Reproducibility Carbon Footprint Reduction Preprocessing pipeline Quality control Energy monitoring
Accepted
Muhammad Usman Akbar 1,2, Maëliss Jallais1,2, Marco Palombo1,2
1Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
2School of Computer Science and Informatics, Cardiff University, Cardiff, United Kingdom
Presenting Author: Muhammad Usman Akbar

Synopsis

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References

1. Tax, Chantal MW, et al. "What’s new and what’s next in diffusion MRI preprocessing." NeuroImage 249 (2022): 118830.
2. Tournier, J-Donald, et al. "MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation." Neuroimage 202 (2019): 116137.
3. Tipping, Michael E. "Mixtures of probabilistic principal component analysers." Neural computation 11 (1998): 435-474.
4. Veraart, Jelle, et al. "Gibbs ringing in diffusion MRI." Magnetic resonance in medicine 76.1 (2016): 301-314.
5. Andersson, Jesper LR, Stefan Skare, and John Ashburner. "How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging." Neuroimage 20.2 (2003): 870-888.
6. Andersson, Jesper LR, and Stamatios N. Sotiropoulos. "An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging." Neuroimage 125 (2016): 1063-1078.
7. Tao, S., et al. "Gradient nonlinearity calibration and correction for a compact, asymmetric magnetic resonance imaging gradient system." Physics in Medicine & Biology 62.2 (2016): N18.
8. Schmidt, V., et al.: CodeCarbon: estimate and track carbon emissions from machine learning computing (2021). https://doi.org/10.5281/zenodo.4658424 [doi]
9. National Grid ESO. Carbon Intensity API. Carbon Intensity, https://api.carbonintensity.org.uk/. Accessed 22 Oct. 2025.
10. McNabb, Carolyn B., et al. "WAND: A multi-modal dataset integrating advanced MRI, MEG, and TMS for multi-scale brain analysis." Scientific Data 12.1 (2025): 220.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/464-02-007