Bayesian algorithm using spatial priors for multiexponential T2 relaxometry from multiecho spin echo MRI Academic Article uri icon

Overview

MeSH Major

  • Algorithms
  • Brain
  • Brain Mapping
  • Diffusion Tensor Imaging
  • Echo-Planar Imaging
  • Nerve Fibers, Myelinated
  • Pattern Recognition, Automated

abstract

  • Multiexponential T₂ relaxometry is a powerful research tool for detecting brain structural changes due to demyelinating diseases such as multiple sclerosis. However, because of unusually high signal-to-noise ratio requirement compared with other MR modalities and ill-posedness of the underlying inverse problem, the T₂ distributions obtained with conventional approaches are frequently prone to noise effects. In this article, a novel multivoxel Bayesian algorithm using spatial prior information is proposed. This prior takes into account the expectation that volume fractions and T₂ relaxation times of tissue compartments change smoothly within coherent brain regions. Three-dimensional multiecho spin echo MRI data were collected from five healthy volunteers at 1.5 T and myelin water fraction maps were obtained using the conventional and proposed algorithms. Compared with the conventional method, the proposed method provides myelin water fraction maps with improved depiction of brain structures and significantly lower coefficients of variance in white matter.

publication date

  • November 2012

Research

keywords

  • Academic Article

Identity

Language

  • eng

Digital Object Identifier (DOI)

  • 10.1002/mrm.24170

PubMed ID

  • 22266707

Additional Document Info

start page

  • 1536

end page

  • 43

volume

  • 68

number

  • 5