Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
470-05-110 ISMRM Abstract

Linear and B-tensor Encoding Comparison for Diffusion Microstructure Parameter Estimation on Ultra High Performance Gradient

Primary: Diffusion - Microstructure
Secondary: Diffusion - Diffusion Modeling
470-05-110 · Advanced Diffusion Modeling for Microstructure Mapping · Tuesday, 12 May, 1:40 PM–2:35 PM · Traditional Posters | Exhibition Hall
Keywords: Microstructural Parameter Estimation Single Diffusion Encoding Multi Diffusion Encoding Ultra High Performance Gradients
Accepted
Mahsa Rajabi1, Chu-Yu Lee 2,3, Merry Mani4
1Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, United States of America
2University of Iowa, Iowa City, United States of America
3Department of Radiology, University of Iowa, Iowa City, United States of America
4Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, United States of America
Presenting Author: Chu-Yu Lee

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References

1. E. O. Stejskal and J. E. Tanner, “Spin Diffusion Measurements: Spin Echoes in the Presence of a Time‐Dependent Field Gradient,” The Journal of Chemical Physics, vol. 42, no. 1, pp. 288–292, Jan. 1965, doi: https://doi.org/10.1063/1.1695690. [doi]
2. C. Lee, R. Ghorbani, M. Rajabi, and M. Mani, “Advanced microstructure imaging at high b‐values and high resolution combining ultra‐high performance gradient diffusion imaging and model‐based deep learning demonstrated using 3D multi‐slab acquisition,” Magnetic Resonance in Medicine, Aug. 2025, doi: https://doi.org/10.1002/mrm.70046. [doi]
3. D. Topgaard, “Multidimensional diffusion MRI,” Journal of Magnetic Resonance, vol. 275, pp. 98–113, Feb. 2017, doi: https://doi.org/10.1016/j.jmr.2016.12.007. [doi]
4. W. Carl-Fredrik, “Q-space trajectory imaging (QTI) for multidimensional diffusion MRI,” Frontiers in Physics, vol. 4, 2016, doi: https://doi.org/10.3389/conf.fphy.2016.01.00016. [doi]
5. F. Szczepankiewicz, C.-F. Westin, and M. Nilsson, “Gradient waveform design for tensor-valued encoding in diffusion MRI,” Journal of Neuroscience Methods, vol. 348, p. 109007, Jan. 2021, doi: https://doi.org/10.1016/j.jneumeth.2020.109007. [doi]
6. S. Coelho et al., “Reproducibility of the Standard Model of diffusion in white matter on clinical MRI systems,” NeuroImage, vol. 257, p. 119290, May 2022, doi: https://doi.org/10.1016/j.neuroimage.2022.119290. [doi]
7. S. Coelho, J. M. Pozo, S. N. Jespersen, D. K. Jones, and A. F. Frangi, “Resolving degeneracy in diffusion MRI biophysical model parameter estimation using double diffusion encoding,” Magnetic Resonance in Medicine, vol. 82, no. 1, pp. 395–410, Mar. 2019, doi: https://doi.org/10.1002/mrm.27714. [doi]
8. J. L. R. Andersson and S. N. Sotiropoulos, “An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging,” NeuroImage, vol. 125, pp. 1063–1078, Jan. 2016, doi: https://doi.org/10.1016/j.neuroimage.2015.10.019. [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/470-05-110