Decoding the microstructural properties of white matter using realistic models

Abstract

The multi-echo gradient echo (ME-GRE) magnetic resonance signal evolution in white matter has a strong dependence on the orientation of myelinated axons in respect to the main static field. Although analytical solutions, based on the Hollow Cylinder Model have been able to predict some of the behaviour the hollow cylinder model, it has been shown that realistic models of white matter offer a better description of the signal behaviout observed.In this work, we present a pipeline to (i) generate realistic 2D white matter models with its microstructure based on real axon but with arbitrary fiber volume fraction (FVF) and g-ratio. We (ii) simulate their interaction with the static magnetic field to be able to simulate their MR signal. For the first time, we (iii) demonstrate that realistic 2D models can be used to simulate an MR signal that provides a good approximation of the signal obtained from a real 3D white matter model obtained using electron microscopy. We then (iv) demonstrate in silico that 2D WM models can be used to predict microstructural parameters in a robust way if multi-echo multi-orientation data is available and the main fiber orientation in each pixel is known using DTI. A Deep Learning Network was trained and characterized in its ability to recover the desired microstructural parameters such as FVF, g-ratio, free and bound water transverse relaxation and magnetic susceptibility. Finally, the network was trained to recover these micro-structural parameters from an ex-vivo dataset acquired in 9-orientations in respect to the magnetic field and 12 echo times. We demonstrate that this is an overdetermined problem and that as few as 3 orientations can already provide comparable results for some of the decoded metrics.[Highlights] -A pipeline to generate realistic white matter models of arbitrary fiber volume fraction and g-ratio is presented; -We present a methodology to simulated the gradient echo signal from segmented 2D and 3D models of white matter, which takes into account the interaction of the static magnetic field with the anisotropic susceptibility of the myelin phospholipids; -Deep Learning Networks can be used to decode microstructural white matter parameters from the signal of multi-echo multi-orientation data;