Precise Inference and Characterization of Structural Organization (PICASO) of tissue from molecular diffusion

Abstract

Inferring the microstructure of complex media from the diffusive motion of molecules is a challenging problem in diffusion physics. In this paper, we introduce a novel representation of diffusion MRI (dMRI) signal from tissue with spatially-varying diffusivity using a diffusion disturbance function. This disturbance function contains information about the (intra-voxel) spatial fluctuations in diffusivity due to restrictions, hindrances and tissue heterogeneity of the underlying tissue substrate. We derive the short- and long-range disturbance coefficients from this disturbance function to characterize the tissue structure and organization. Moreover, we provide an exact relation between the disturbance coefficients and the time-varying moments of the diffusion propagator, as well as their relation to specific tissue microstructural information such as the intra-axonal volume fraction and the apparent axon radius. The proposed approach is quite general and can model dMRI signal for any type of gradient sequence (rectangular, oscillating, etc.) without using the Gaussian phase approximation. The relevance of the proposed PICASO model is explored using Monte-Carlo simulations and in-vivo dMRI data. The results show that the estimated disturbance coefficients can distinguish different types of microstructural organization of axons.

Retrospective harmonization of multi-site diffusion MRI data acquired with different acquisition parameters

“…b harm is the new b-value of the harmonized data, which is a parameter of choice and we set it to 1000 for all subjects and for both sites in this work (see Table 1, bottom row). For harmonizing b-values greater than 1500 s/mm 2 , one could use any of the compressed sensing methods described in (Rathi et al, 2014; Ning et al, 2015b, 2017; Fick et al, 2016, 2015a).…”

Cumulant expansions for measuring water exchange using diffusion MRI

“…Diffusion magnetic resonance imaging (dMRI) is widely used to probe microstructure of cells and permeability of porous media via the diffusive motion of water molecules. [1][2][3][4] A common approach for measuring water exchange using dMRI is to apply Kärger's model 5 to fit the dMRI signal measured by the standard Stejskal-Tanner sequence, 6 here referred to as the single diffusion encoding (SDE) sequence. 7 In the Kärger model, molecular motion in different tissue compartments is modeled by free diffusion with different diffusion coefficients.…”

Effective Potential for Magnetic Resonance Measurements of Restricted Diffusion

“…Conventional techniques for relating the NMR signal above to microstructural features of the medium vary from “localized” models in which the aggregate signal is envisioned to arise from isolated (e.g., restricted) compartments [3, 4] to more “global” models, which attempt to capture the medium’s disorder [5, 6]. Here, we propose to approach the problem of relating the NMR signal to microstructural features of the medium with an alternative paradigm wherein diffusion is thought to be taking place within a potential energy landscape.…”