Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
306-02-007 ISMRM Abstract

GAIA – Green Artificial Intelligence for Accelerated medical imaging: Sustainable and Efficient Diffusion MRI Analysis

Primary: Diffusion - Diffusion Analysis & Visualization
Secondary: Analysis Methods - Image Synthesis and Translation
306-02-007 · From Theory to Clinic: Advances in Diffusion MRI Modeling and Analysis · Monday, 11 May, 1:50 PM–3:40 PM · Auditorium 2
Keywords: Knowledge Distillation Energy efficiency Artificial Intelligence (AI) Deep Learning Carbon Footprint Reduction Signal synthesis
Accepted
Maëliss Jallais 1, Matteo Mancini2, Marco Palombo1,3,4
1Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
2Italian National Institute of Health, Rome, Italy
3School of Computer Science and Informatics, Cardiff University, Cardiff, United Kingdom
4School of Psychology, Cardiff University, Cardiff, United Kingdom
Presenting Author: Maëliss Jallais

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References

1. Kaack, L.H., Donti, P.L., Strubell, E., Kamiya G, Creutzig F, Rolnick D. Aligning artificial intelligence with climate change mitigation. Nat. Clim. Chang. 12, 518–527 (2022). https://doi.org/10.1038/s41558-022-01377-7 [doi]
2. Dhar, P. The carbon impact of artificial intelligence. Nat Mach Intell 2, 423–425 (2020). https://doi.org/10.1038/s42256-020-0219-9 [doi]
3. Hinton G, Vinyals O, Dean J. Distilling the knowledge in a neural network. Neural Information Processing System Deep Learning Workshop (2015). https://doi.org/10.48550/arXiv.1503.02531 [doi]
4. Romero, A., Ballas, N., Kahou, S.E., Chassang, A., Gatta, C., Bengio, Y. Fitnets: Hints for thin deep nets. Proceedings of the International Conference on Learning Representations (2015). https://doi.org/10.48550/arXiv.1412.6550 [doi]
5. Mancini, M., McNabb, C., Cercignani, M., Jones, D.K., Palombo, M. Synthesising ultra-strong gradients diffusion MRI with high-resolution convolutional neural networks. ISMRM (2024).
6. McNabb, C.B., Driver, I.D., Hyde, V. et al. WAND: A multi-modal dataset integrating advanced MRI, MEG, and TMS for multi-scale brain analysis. Sci Data 12, 220 (2025). https://doi.org/10.1038/s41597-024-04154-7 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/306-02-007