Probing tissue microstructure with restriction spectrum imaging: Histological and theoretical validation

Abstract

Diffusion magnetic resonance imaging (dMRI) is a powerful tool for studying biological tissue microarchitectures in vivo. Recently, there has been increased effort to develop quantitative dMRI methods to probe both length scale and orientation information in diffusion media. Diffusion spectrum imaging (DSI) is one such approach that aims to resolve such information on the basis of the three-dimensional diffusion propagator at each voxel. However, in practice only the orientation component of the propagator function is preserved when deriving the orientation distribution function. Here, we demonstrate how a straightforward extension of the linear spherical deconvolution (SD) model can be used to probe tissue orientation structures over a range (or “spectrum”) of length scales with minimal assumptions on the underlying microarchitecture. Using high b-value Cartesian q-space data on a fixed rat brain sample, we demonstrate how this “restriction spectrum imaging” (RSI) model allows for separating the volume fraction and orientation distribution of hindered and restricted diffusion, which we argue stems primarily from diffusion in the extra- and intra-neurite water compartment, respectively. Moreover, we demonstrate how empirical RSI estimates of the neurite orientation distribution and volume fraction capture important additional structure not afforded by traditional DSI or fixed-scale SD-like reconstructions, particularly in grey matter. We conclude that incorporating length scale information in geometric models of diffusion offers promise for advancing state-of-the-art dMRI methods beyond white matter into grey matter structures while allowing more detailed quantitative characterization of water compartmentalization and histoarchitecture of healthy and diseased tissue.

COMMIT: Convex Optimization Modeling for Microstructure Informed Tractography

“…Computationally expensive nonlinear procedures are normally required to fit such models; however, when the response functions are known (or can be determined) a priori, these models can be efficiently estimated by means of systems of linear equations [27], [29]. Hence, in a voxel, the predicted signal S (q) at q-space location q ∈ R 3 can be expressed as a linear combination with the following general formulation:…”

Age effects and sex differences in human brain white matter of young to middle-aged adults: A DTI, NODDI, and q-space study

“…More complex models with more than three compartments have been proposed, however they come at the cost of prolonged imaging time. Two examples of such models include: (1) composite hindered and restricted model of diffusion (CHARMED) with one anisotropic hindered diffusion compartment and multiple anisotropic restricted compartments for crossing fibers (Assaf and Basser 2005), and (2) restriction spectrum imaging (RSI) with axons modeled by a spectrum of rigid sticks of various sizes (White et al 2013). Neurite Orientation Dispersion and Density imaging (NODDI) models the diffusion-weighted signal as a combination of three basic compartments of CSF, extracellular, and intracellular described above.…”

Pre-operative cellularity mapping and intra-MRI surgery: potential for improving neurosurgical biopsies

“…We have previously shown that RSI afford finer resolution of molecular diffusion relative to conventional DWI, affording assessment of a gradation of diffusional restrictions, ranging from free to partial restriction to absolute restriction [25]. With RSI, we were able to determine regions of high cellularity within the tumor that failed detection by conventional DWI [26].…”