Non-parametric representation and prediction of single- and multi-shell diffusion-weighted MRI data using Gaussian processes

Neuroimage. 2015 Nov 15;122:166-76. doi: 10.1016/j.neuroimage.2015.07.067. Epub 2015 Jul 30.

Abstract

Diffusion MRI offers great potential in studying the human brain microstructure and connectivity. However, diffusion images are marred by technical problems, such as image distortions and spurious signal loss. Correcting for these problems is non-trivial and relies on having a mechanism that predicts what to expect. In this paper we describe a novel way to represent and make predictions about diffusion MRI data. It is based on a Gaussian process on one or several spheres similar to the Geostatistical method of "Kriging". We present a choice of covariance function that allows us to accurately predict the signal even from voxels with complex fibre patterns. For multi-shell data (multiple non-zero b-values) the covariance function extends across the shells which means that data from one shell is used when making predictions for another shell.

Keywords: Diffusion MRI; Gaussian process; Multi-shell; Non-parametric representation.

MeSH terms

  • Artifacts
  • Brain / anatomy & histology*
  • Brain / physiology*
  • Data Interpretation, Statistical
  • Diffusion
  • Diffusion Magnetic Resonance Imaging / methods*
  • Diffusion Tensor Imaging / methods*
  • Humans
  • Image Processing, Computer-Assisted / methods
  • Models, Neurological*
  • Normal Distribution
  • Signal Processing, Computer-Assisted