New dMRI methodology to calculate q-space measures

A new paper entitled Micro-structure diffusion scalar measures from reduced MRI acquisitions has been published in PLOS ONE. The work is written by member of the LPI (Santiago Aja-Fernández, Rodrigo de Luis-García, Antonio Tristán-Vega)  in collaboration with reserachers from AGH University of Science and Technology, Krakow, Poland (Tomasz Pieciak) and CUBRIC, University of Cardiff, UK Maryam Afzali, Malwina Molendowska). In the work, we propose a method to effectively calculate apparent RTOP, RTPP, and RTAP from single shell diffusion MRI. The method avoids the calculation of the whole EAP by assuming the diffusion anisotropy is roughly independent from the radial direction. With such an assumption we achieve closed-form expressions for the measures using information from one single shell. At the same time, the closed forms provided make the method computationally efficient and robust.

Methods are implemented in MATLAB and they will be publicly release soon. We can submitted them no upon request.

[Link to paper]


In diffusion MRI, the Ensemble Average diffusion Propagator (EAP) provides relevant micro-structural information and meaningful descriptive maps of the white matter previously obscured by traditional techniques like Diffusion Tensor Imaging (DTI). The direct estimation of the EAP, however, requires a dense sampling of the Cartesian q-space involving a huge amount of samples (diffusion gradients) for proper reconstruction. A collection of more efficient techniques have been proposed in the last decade based on parametric representations of the EAP, but they still imply acquiring a large number of diffusion gradients with different b-values (shells). Paradoxically, this has come together with an effort to find scalar measures gathering all the q-space micro-structural information probed in one single index or set of indices. Among them, the return-to-origin (RTOP), return-to-plane (RTPP), and return-to-axis (RTAP) probabilities have rapidly gained popularity.

In this work, we propose the so-called “Apparent Measures Using Reduced Acquisitions” (AMURA) aimed at computing scalar indices that can mimic the sensitivity of state of the art EAP-based measures to micro-structural changes. AMURA drastically reduces both the number of samples needed and the computational complexity of the estimation of diffusion properties by assuming the diffusion anisotropy is roughly independent from the radial direction. This simplification allows us to compute closed-form expressions from single-shell information, so that AMURA remains compatible with standard acquisition protocols commonly used even in clinical practice. Additionally, the analytical form of AMURA-based measures, as opposed to the iterative, non-linear reconstruction ubiquitous to full EAP techniques, turns the newly introduced apparent RTOP, RTPP, and RTAP both robust and efficient to compute.

Creation Date: 
11 Mar 2020