Synopsis

Most studies using diffusion MRI today rely on a pair of gradient pulses to do the diffusion encoding. However, this approach is fundamentally limited in several ways. This talk will provide examples of these limitations and show how to use advanced gradient waveforms to overcome them. Examples from recent papers will demonstrate how such advanced encodings can radically change the interpretation of diffusion MRI data.

Encoding and acquisition: Advanced Diffusion Encoding Gradient Waveforms

• Confusing microscopic anisotropy and orientation dispersion. With PGSE, effects of microscopic anisotropy are entangled with orientation dispersion (Mitra, 1995), which leads to degeneracy problems when using the standard model of white matter diffusion (Lampinen et al., 2019; Novikov et al., 2019). Gradient waveforms that enable so-called tensor-valued diffusion encoding provides orthogonal information can be used to solve the degeneracy problem and enable accurate estimation of the model parameters (Coelho et al., 2019; Reisert et al., 2019). However, problems remain when taking compartmental differences in T2-relaxation into account (Lampinen et al., 2019), which need to be addressed by acquiring data with multiple echo times (Lampinen, 2020).

• Perfusion mapping - an example of multiexponential fitting as an ill-posed problem. The IVIM method relies on a high pseudo-diffusion of capillary blood to map the perfusion fraction (Le Bihan et al., 1986). However, estimates of the perfusion fraction are sensitive to noise due to the ill-posed nature of the multiexponential fitting procedure at the heart of the IVIM method. By varying the level of flow compensation, it is possible to obtain more accurate estimates of the perfusion fraction (Ahlgren et al., 2016; Wetscherek et al., 2014).

• Diffusion time dependence - restricted diffusion and water exchange. Effects of water exchange act on the signal in opposite ways to effects of restricted diffusion with PGSE (Nilsson et al., 2013b). Thus, these effects are difficult to disentangle which leads to noisy parameter estimates. Methods based on double-diffusion encoding, such as filter exchange imaging, can overcome this problem by constructing a protocol with specific sensitivity to exchange (Lasič et al., 2011; Nilsson et al., 2013a). Methods using oscillating gradients can also improve the estimation of restricted diffusion (Aggarwal et al., 2011; Does et al., 2003; Drobnjak et al., 2016; Reynaud, 2017).

Acknowledgements

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