Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
667-01-008 ISMRM Abstract

Voxel-wise b-value calibration for diffusion MRI at ultra-low-field

Primary: Diffusion - DWI/DTI/DKI
Secondary: Physics & Engineering - Low-Field MRI
667-01-008 · 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 B-value Diffusion weighted imaging Bias field correction LOW-FIELD DIFFUSION MRI
Accepted
Ethan P Nott 1, Francesco Padormo2,3,4, James A Gholam5, Sean Deoni6, Rui Pedro Teixeira4, Steven C Williams7, Jo V Hajnal8,9
1Centre for Neuroimaging Sciences, King's College London, London, United Kingdom
2Imperial College London, London, United Kingdom
3MR Research Collaborations, Siemens Healthcare Limited, Camberley, United Kingdom
4Hyperfine, Inc., Guilford, United States of America
5Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
6Maternal, Newborn, Child Nutrition and Health (MNCH) Discovery and Translational (D&T) Sciences Program, Gates Foundation, Seattle, United States of America
7Centre for Neuroimaging Sciences, King's College London, London, United Kingdom
8Research Department of Imaging Physics and Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
9Research Department of Early Life Imaging Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
Presenting Author: Ethan P Nott

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References

1. Y. Lee et al., ‘A comprehensive approach for correcting voxel-wise b-value errors in diffusion MRI’, Magnetic Resonance in Medicine, vol. 83, no. 6, pp. 2173–2184, June 2020, doi: 10.1002/mrm.28078. [doi]
2. J. Gholam et al., ‘Diffusion Tensor MRI and Spherical-Deconvolution-Based Tractography on an Ultra-Low Field Portable MRI System’, June 04, 2025, arXiv: arXiv:2506.04473. doi:10.48550/arXiv.2506.04473. [doi]
3. E. Garyfallidis et al., ‘Dipy, a library for the analysis of diffusion MRI data’, Front. Neuroinform., vol. 8, Feb. 2014, doi:10.3389/fninf.2014.00008. [doi]
4. 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, pp. 394–406, Nov. 2016, doi:10.1016/j.neuroimage.2016.08.016. [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/667-01-008