Microstructural parameter estimation in vivo using diffusion MRI and structured prior information

Abstract

PurposeDiffusion MRI has recently been used with detailed models to probe tissue microstructure. Much of this work has been performed ex vivo with powerful scanner hardware, to gain sensitivity to parameters such as axon radius. By contrast, performing microstructure imaging on clinical scanners is extremely challenging.MethodsWe use an optimized dual spin‐echo diffusion protocol, and a Bayesian fitting approach, to obtain reproducible contrast (histogram overlap of up to 92%) in estimated maps of axon radius index in healthy adults at a modest, widely‐available gradient strength (35 mT m −1). A key innovation is the use of influential priors.ResultsWe demonstrate that our priors can improve precision in axon radius estimates—a 7‐fold reduction in voxelwise coefficient of variation in vivo—without significant bias. Our results may reflect true axon radius differences between white matter regions, but this interpretation should be treated with caution due to the complexity of the tissue relative to our model.ConclusionsSome sensitivity to relatively large axons (3–15 μm) may be available at clinical field and gradient strengths. Future applications at higher gradient strength will benefit from the favorable eddy current properties of the dual spin‐echo sequence, and greater precision available with suitable priors. Magn Reson Med, 2015. © 2015 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. Magn Reson Med 75:1787–1796, 2016. © 2015 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance.

Local volume fraction distributions of axons, astrocytes, and myelin in deep subcortical white matter

“…Thus, their relationship needs to be appropriately modelled and care must be taken when comparing dMRI measures with the V V distributions. Finally, the characterisation of the V V distributions in the population could inform dMRI biophysical models as prior distributions to improve the accuracy of the estimated parameters (Clayden et al, 2016).…”

“…This information has the potential to improve the performance of existing tissue models and help in the validation of new ones. For example, Clayden et al (2016) showed that by introducing structured prior information on model parameters, the accuracy in the estimation is improved. The interpretation of parameters from several existing dMRI techniques such as DTI or biophysical models has been previously validated using histological sections (cf.…”

A comparative simulation study of bayesian fitting approaches to intravoxel incoherent motion modeling in diffusion‐weighted MRI

“…Bayesian modeling has found application as a tool for image processing and parameter estimation across a broad range of MRI domains (16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27). Neil and Bretthorst (28) were the first to propose its use as an alternative to nonlinear least squares fitting for improving the quality of IVIM parameter estimates.…”

Precision Imaging: more descriptive, predictive and integrative imaging

“…Hu et al, 2012), use of structural models to infer tissue micro-structure (e.g. Lekadir et al, 2014Lekadir et al, , 2015Clayden et al, 2016), use of computational models to produce virtual images of unobservable features (e.g. Nørgaard et al, 2016;Lekadir et al, 2016), or computational imaging approaches that incorporate prior knowledge into image acquisition or reconstruction leading, for instance, to agile or portable imaging/sensing systems (York et al, 2011;Coskun and Ozcan, 2014).…”