Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
464-01-001 ISMRM Abstract

Comparison of Continuous Signal Representations for Multi-Tissue Spherical Deconvolution of Non-shelled Diffusion MRI Data

Accepted
Bontle Watson 1, Jacques-Donald Tournier2
1School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
2Research department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, Kings College London, London, United Kingdom
Presenting Author: Bontle Watson

Synopsis

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References

1. B. Jeurissen, J.-D. Tournier, T. Dhollander, A. Connelly, and J. Sijbers, ‘Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data’, NeuroImage, vol. 103, pp. 411–426, Dec. 2014, doi: 10.1016/j.neuroimage.2014.07.061. [doi]
2. V. J. Wedeen, P. Hagmann, W. I. Tseng, T. G. Reese, and R. M. Weisskoff, ‘Mapping complex tissue architecture with diffusion spectrum magnetic resonance imaging’, Magn. Reson. Med., vol. 54, no. 6, pp. 1377–1386, Dec. 2005, doi: 10.1002/mrm.20642. [doi]
3. D. S. Tuch, T. G. Reese, M. R. Wiegell, N. Makris, J. W. Belliveau, and V. J. Wedeen, ‘High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity’, Magn. Reson. Med., vol. 48, no. 4, pp. 577–582, Oct. 2002, doi: 10.1002/mrm.10268. [doi]
4. S. N. Sotiropoulos et al., ‘Advances in diffusion MRI acquisition and processing in the Human Connectome Project’, NeuroImage, vol. 80, pp. 125–143, Oct. 2013, doi: 10.1016/j.neuroimage.2013.05.057. [doi]
5. J. Morez, J. Sijbers, F. Vanhevel, and B. Jeurissen, ‘Constrained spherical deconvolution of nonspherically sampled diffusion MRI data’, Hum. Brain Mapp., vol. 42, no. 2, pp. 521–538, Feb. 2021, doi: 10.1002/hbm.25241. [doi]
6. J. H. Jensen, J. A. Helpern, A. Ramani, H. Lu, and K. Kaczynski, ‘Diffusional kurtosis imaging: the quantification of non-gaussian water diffusion by means of magnetic resonance imaging’, Magn. Reson. Med., vol. 53, no. 6, pp. 1432–1440, June 2005, doi: 10.1002/mrm.20508. [doi]
7. J. H. Jensen and J. A. Helpern, ‘MRI quantification of non‐Gaussian water diffusion by kurtosis analysis’, NMR Biomed., vol. 23, no. 7, pp. 698–710, Aug. 2010, doi: 10.1002/nbm.1518. [doi]
8. K. M. Bennett, K. M. Schmainda, R. Bennett (Tong), D. B. Rowe, H. Lu, and J. S. Hyde, ‘Characterization of continuously distributed cortical water diffusion rates with a stretched-exponential model’, Magn. Reson. Med., vol. 50, no. 4, pp. 727–734, 2003, doi: 10.1002/mrm.10581. [doi]
9. D. S. Novikov, E. Fieremans, S. N. Jespersen, and V. G. Kiselev, ‘Quantifying brain microstructure with diffusion MRI: Theory and parameter estimation’, NMR Biomed., vol. 32, no. 4, p. e3998, Apr. 2019, doi: 10.1002/nbm.3998. [doi]
10. T. R. Barrick, C. Ingo, M. G. Hall, and F. A. Howe, ‘Quasi‐Diffusion Imaging: Application to Ultra‐High b ‐Value and Time‐Dependent Diffusion Images of Brain Tissue’, NMR Biomed., vol. 38, no. 4, p. e70011, Apr. 2025, doi: 10.1002/nbm.70011. [doi]
11. J.-D. Tournier, B. Jeurissen, and D. Christiaens, ‘Iterative Model-Based Rician Bias Correction and its Application to Denoising in Diffusion MRI’, in Proceedings 31. Annual Meeting International Society for Magnetic Resonance in Medicine, Toronto, Canada, 2023, p. 3795. doi: https://doi.org/10.58530/2023/3795. [doi]
12. M. Froeling, C. M. W. Tax, S. B. Vos, P. R. Luijten, and A. Leemans, ‘“MASSIVE” brain dataset: Multiple acquisitions for standardization of structural imaging validation and evaluation’, Magn. Reson. Med., vol. 77, no. 5, pp. 1797–1809, 2017, doi: 10.1002/mrm.26259. [doi]

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