Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
667-01-007 ISMRM Abstract

Comparing Denoising Techniques for Diffusion-Relaxometry Liver MRI Data Acquired at 0.55T

Primary: Diffusion - Diffusion Analysis & Visualization
Secondary: Analysis Methods - Image Enhancement
667-01-007 · Innovations in Diffusion MRI: Systems, Sequences, and Protocol Design · Thursday, 14 May, 8:30 AM–9:25 AM · Digital Posters Row H
Keywords: Low-Field MRI Denoising Multidimensional MRI Liver MRI
Accepted
Jamie Robertson 1, Philippa Bridgen2,3, Pierluigi Di Cio2,3, Inka Granlund2,3, Sarah McElroy1,2,4, Jacques-Donald Tournier5, Jo V Hajnal1,2,5,6, Andrada Ianus1,7,8
1Department of Imaging Physics & Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
2London Collaborative Ultra-High field System (LoCUS), King's College London, London, United Kingdom
3Guy's and St. Thomas' NHS Foundation Trust, London, United Kingdom
4MR Research Collaborations, Siemens Healthcare Limited, Camberley, United Kingdom
5Department of Early Life Imaging, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
6Centre for the Developing Brain, King's College London, London, United Kingdom
7Algarve Biomedical Center, Faro, Portugal
8Champalimaud Foundation, Lisboa, Portugal
Presenting Author: Jamie Robertson

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References

1. [1] P. J. Slator et al., “Combined diffusion-relaxometry microstructure imaging: Current status and future prospects,” 2021. doi: 10.1002/mrm.28963. [doi]
2. [2] J. L. Olesen, A. Ianus, L. Østergaard, N. Shemesh, and S. N. Jespersen, “Tensor denoising of multidimensional MRI data,” Magn Reson Med, vol. 89, no. 3, 2023, doi: 10.1002/mrm.29478. [doi]
3. [3] J. Veraart, D. S. Novikov, D. Christiaens, B. Ades-aron, J. Sijbers, and E. Fieremans, “Denoising of diffusion MRI using random matrix theory,” Neuroimage, vol. 142, 2016, doi: 10.1016/j.neuroimage.2016.08.016. [doi]
4. [4] B. Kang, W. Lee, H. Seo, H. Y. Heo, and H. W. Park, “Self-supervised learning for denoising of multidimensional MRI data,” Magn Reson Med, vol. 92, no. 5, pp. 1980–1994, Nov. 2024, doi: 10.1002/MRM.30197. [doi]
5. [5] S. Fadnavis, J. Batson, and E. Garyfallidis, “Patch2Self: Denoising diffusion MRI with self-supervised learning,” in Advances in Neural Information Processing Systems, 2020, doi: 10.48550/arXiv.2011.01355. [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/667-01-007