Abstract
This paper compares a range of compartment models for diffusion MRI data on in vivo human acquisitions from a standard 60mT/m system (Philips 3T Achieva) and a unique 300mT/m system (Siemens Connectom). The key aim is to determine whether both systems support broadly the same models or whether the Connectom higher gradient system supports significantly more complex models. A single volunteer underwent 8h of acquisition on each system to provide uniquely wide and dense sampling of the available space of pulsed-gradient spin-echo (PGSE) measurements. We select a set of promising models from the wide set of possible three-compartment models for in vivo white matter (WM) that previous work and preliminary experiments suggest as strong candidates, but extend them to fit for compartmental T2 and diffusivity. We focus on the corpus callosum where the WM fibre architecture is simplest and compare their ability to explain the measured data, using Akaike's information criterion (AIC), and to predict unseen data, using cross-validation. We also compare the stability of parameter estimates in the presence of i) noise, using bootstrapping, and ii) spatial variation, using visual assessment and comparison with anatomical knowledge. Broadly similar models emerge from the AIC and cross-validation experiments in both data sets. Specifically, a three-compartment model consisting of either a Bingham distribution of sticks or a Cylinder for the intracellular compartment, an anisotropic diffusion tensor (DT) model for the extracellular compartment, as well as an isotropic CSF compartment, performs consistently well. However, various other models also perform well and no single model emerges as clear winner. The WM data (with virtually no CSF contamination) do not support compartmental T2 but partially support compartmental diffusivity. Evaluation of parameter stability favours simpler models than those identified by AIC or cross-validation. They suggest that the level of complexity in models underpinning currently popular microstructure imaging techniques such as NODDI, CHARMED, or ActiveAx, where the number of free parameters is about 4 or 5 rather than 10 or 11, may reflect the level of complexity achievable for a useful technique on current systems, although the 300mT/m data may support more complex models.
Highlights
Magnetic resonance (MR) microstructure imaging uses mathematical models to relate MR signals in each image voxel to microscopic tissue features, and estimate and map those features over an image volume
We further look at the sensitivity of parameter estimates to noise by looking across bootstrap data sets and to spatial variation by mapping parameters over the corpus callosum, which provide a complementary evaluation of the models
For each of our compartment models, we evaluate performance with and without this constraint
Summary
Magnetic resonance (MR) microstructure imaging uses mathematical models to relate MR signals in each image voxel to microscopic tissue features, and estimate and map those features over an image volume. In diffusion MRI, the standard DT model has two key limitations: first it is too simple to explain the data over a wide range of b-values and orientations; and second, it lacks specificity to particular tissue features. A variety of alternative biophysical diffusion MRI models have emerged over the last decade to address these limitations. These models underpin the emerging generation of microstructure imaging techniques. That are starting to replace DT-imaging in a range of biological and clinical studies into tissue microstructure variation, as in e.g. The model has three geometric compartments: ellipsoids for restricted intra-axonal water, anisotropically hindered extracellular water (with diffusivity and relaxation constants different to the intracellular space) and isotropically restricted glial cell water, with exchange between all compartments.
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