Q-space trajectory imaging for multidimensional diffusion MRI of the human brain

Abstract

This work describes a new diffusion MR framework for imaging and modeling of microstructure that we call q-space trajectory imaging (QTI). The QTI framework consists of two parts: encoding and modeling. First we propose q-space trajectory encoding, which uses time-varying gradients to probe a trajectory in q-space, in contrast to traditional pulsed field gradient sequences that attempt to probe a point in q-space. Then we propose a microstructure model, the diffusion tensor distribution (DTD) model, which takes advantage of additional information provided by QTI to estimate a distributional model over diffusion tensors. We show that the QTI framework enables microstructure modeling that is not possible with the traditional pulsed gradient encoding as introduced by Stejskal and Tanner. In our analysis of QTI, we find that the well-known scalar b-value naturally extends to a tensor-valued entity, i.e., a diffusion measurement tensor, which we call the b-tensor. We show that b-tensors of rank 2 or 3 enable estimation of the mean and covariance of the DTD model in terms of a second order tensor (the diffusion tensor) and a fourth order tensor. The QTI framework has been designed to improve discrimination of the sizes, shapes, and orientations of diffusion microenvironments within tissue. We derive rotationally invariant scalar quantities describing intuitive microstructural features including size, shape, and orientation coherence measures. To demonstrate the feasibility of QTI on a clinical scanner, we performed a small pilot study comparing a group of five healthy controls with five patients with schizophrenia. The parameter maps derived from QTI were compared between the groups, and 9 out of the 14 parameters investigated showed differences between groups. The ability to measure and model the distribution of diffusion tensors, rather than a quantity that has already been averaged within a voxel, has the potential to provide a powerful paradigm for the study of complex tissue architecture.

Axon Diameters and Myelin Content Modulate Microscopic Fractional Anisotropy at Short Diffusion Times in Fixed Rat Spinal Cord

“…Methods other than D(O)DE, targeting µFA such as tailoring b-tensor shapes are emerging, with many potential applications [77][78][79][80]. However, such methods may be confounded by time-dependent diffusion effects [27,[81][82][83], whereas D(O)DE at long mixing times naturally avoids these confounds [43].…”

NMR signal for particles diffusing under potentials: From path integrals and numerical methods to a model of diffusion anisotropy

“…Under these conditions, the observed higher order terms emerge predominantly from heterogeneities within the sample rather than true compartmental effects. Heterogeneity-induced effects would be captured by the present model as well if the signal is represented as the superposition of signals associated with a distribution of confinement values, i.e., potentials, similar to what is done in multiple diffusion tensor [35] and diffusion tensor distribution [36,37] representations.…”

“…Indeed, several different pulse sequences have been studied in recent years employing pulsed [8,[39][40][41] and continuous [42][43][44] waveforms. Especially in the context of modeling local diffusion anisotropy, one is faced with the choice between the Gaussian diffusion tensor model [37,45], and restricted capped cylinder model [25,46]. Although, the restricted character of the model is desirable, the capped cylinder model had two limitations, which may have prompted some studies to adopt the diffusion tensor model instead: (i) it has only two size parameters, so is more limited than the diffusion tensor model, (ii) the solutions are rather complicated.…”

Studying neuroanatomy using MRI

“…Such sequences are also promising to better characterise restricted diffusion and probe anisotropy within a bulk isotropic voxel (e.g. [128][129][130] ) (see Box1, bottom right figure panel) and even capture diffusional water exchange, a cell membrane permeability dependent parameter 131 . While these various methods have demonstrated remarkable potential in model systems, translation into in-vivo human imaging remains limited by the hardware and measurement techniques available on human MRI scanners, though that is rapidly improving (e.g.…”