Validation strategies for the interpretation of microstructure imaging using diffusion MRI

Research output: Contribution to journalArticle

Abstract

Extracting microanatomical information beyond the image resolution of MRI would provide valuable tools for diagnostics and neuroscientific research. A number of mathematical models already suggest microstructural interpretations of diffusion MRI (dMRI) data. Examples of such microstructural features could be cell bodies and neurites, e.g. the axon's diameter or their orientational distribution for global connectivity analysis using tractography, and have previously only been possible to access through conventional histology of post mortem tissue or invasive biopsies. The prospect of gaining the same knowledge non-invasively from the whole living human brain could push the frontiers for the diagnosis of neurological and psychiatric diseases. It could also provide a general understanding of the development and natural variability in the healthy brain across a population. However, due to a limited image resolution, most of the dMRI measures are indirect estimations and may depend on the whole chain from experimental parameter settings to model assumptions and implementation. Here, we review current literature in this field and highlight the integrative work across anatomical length scales that is needed to validate and trust a new dMRI method. We encourage interdisciplinary collaborations and data sharing in regards to applying and developing new validation techniques to improve the specificity of future dMRI methods.

Details

Authors
  • Tim B. Dyrby
  • Giorgio M. Innocenti
  • Martin Bech
  • Henrik Lundell
Organisations
External organisations
  • Technical University of Denmark
  • Karolinska Institutet
  • Swiss Federal Institute of Technology
  • Copenhagen University Hospital
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Medical Image Processing
Original languageEnglish
Pages (from-to)62-79
JournalNeuroImage
Volume182
Early online date2018 Jun 18
Publication statusPublished - 2018
Publication categoryResearch
Peer-reviewedYes