Diffusion MRI Acquisition, Part II: Adding Dimensions
Filip Szczepankiewicz1,2 and Jana Hutter3

1Radiology, Brigham and Women's Hospital, Boston, MA, United States, 2Harvard Medical School, Boston, MA, United States, 3King's College London, London, United Kingdom

Synopsis

This lecture explores how diffusion-weighted experiment can be expanded to include correlations with T1 and T2 relaxation and multidimensional diffusion encoding. The exercises will include calculations relevant to the T2-dependent diffusion encoding, and to the design of non-conventional gradient waveforms.

Target Audience:

Researchers interested in future developments of diffusion MRI experiments.

Objectives/Outcomes:

Understanding limitations of conventional diffusion MRI.

Overview of diffusion-relaxation experiments.

Overview of multidimensional diffusion encoding.

Adding relaxation

The addition of relaxation contrasts facilitates two major improvements. Primarily, it allows for the disambiguation of compartments that differ with respect to T1 or T2 relaxation rates, but are otherwise similar. This means that we may leverage the correlation between chemical and microstructural features to distinguish them (de Almeida Martins & Topgaard 2018; Benjamini & Basser 2018). Furthermore, it enables quantification of the content of a voxel in terms of relative volumes as opposed to signal fractions. The lecture will survey how correlation experiments can be designed to combine T1, T2 and diffusion weighting (Hutter et al. 2018; De Santis et al. 2016), how novel parameter spaces can thereby be accessed and how the data can be used to uncover microstructural information (Veraart et al. 2018).

Adding multidimensional encoding

Multidimensional diffusion encoding means that the diffusion encoding is applied along multiple spatial directions for a single shot (Cory et al. 1990). Doing so allows us to investigate microscopic anisotropy and the heterogeneity of isotopic diffusivities; features that cannot be disentangled by using conventional diffusion encoding alone (Mitra 1995). The lecture describes the theory and underlying mechanism of such diffusion encoding, and covers practical implementations of such experiments using double diffusion encoding (Shemesh et al. 2016) as well as arbitrary gradient waveforms (Westin et al. 2016; Sjölund et al. 2015).

Practical exercises

Exercises will include calculations of ADC and signal fraction parameters for variable echo and repetition times to demonstrate that relaxation is a potential confounder. We will also explore the design of gradient waveform for multidimensional diffusion encoding and how they relate to simple model systems.

Acknowledgements

No acknowledgement found.

References

de Almeida Martins, J.P. & Topgaard, D., 2018. Multidimensional correlation of nuclear relaxation rates and diffusion tensors for model-free investigations of heterogeneous anisotropic porous materials. Scientific reports, 8(1), p.2488.

De Santis, S. et al., 2016. Including diffusion time dependence in the extra-axonal space improves in vivo estimates of axonal diameter and density in human white matter. NeuroImage, 130, pp.91–103. http://dx.doi.org/10.1016/j.neuroimage.2016.01.047.

Benjamini, D. & Basser, P.J., 2018. Towards clinically feasible relaxation-diffusion correlation MRI using MADCO. Microporous and mesoporous materials: the official journal of the International Zeolite Association, 269, pp.93–96.

Hutter, J. et al., 2018. Integrated and efficient diffusion-relaxometry using ZEBRA. Scientific reports, 8(1), p.15138.

Veraart, J., Novikov, D.S. & Fieremans, E., 2018. TE dependent Diffusion Imaging (TEdDI) distinguishes between compartmental T relaxation times. NeuroImage, 182, pp.360–369.

Cory, D.G., Garroway, A.N. & Miller, J.B., 1990. Applications of Spin Transport as a Probe of Local Geometry. Abstracts of Papers of the American Chemical Society.

Mitra, P.P., 1995. Multiple wave-vector extensions of the NMR pulsed-field-gradient spin-echo diffusion measurement. Physical review. B, Condensed matter, 51(21), pp.15074–15078.

Shemesh, N. et al., 2016. Conventions and nomenclature for double diffusion encoding NMR and MRI. Magnetic resonance in medicine: official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine, 75(1), pp.82–87.

Sjölund, J. et al., 2015. Constrained optimization of gradient waveforms for generalized diffusion encoding. Journal of magnetic resonance , 261, pp.157–168.

Westin, C.-F. et al., 2016. Q-space trajectory imaging for multidimensional diffusion MRI of the human brain. NeuroImage, 135, pp.345–362.

Proc. Intl. Soc. Mag. Reson. Med. 27 (2019)