title: A ranking of diffusion MRI compartment models with in vivo human brain data.
abstract: OBJECTIVE
  Diffusion magnetic resonance imaging (MRI) microstructure imaging provides a unique noninvasive probe into tissue microstructure. The technique relies on biophysically motivated mathematical models, relating microscopic tissue features to the magnetic resonance (MR) signal. This work aims to determine which compartment models of diffusion MRI are best at describing measurements from in vivo human brain white matter.
  
  
  METHODS
  Recent work shows that three compartment models, designed to capture intra-axonal, extracellular, and isotropically restricted diffusion, best explain multi-b-value data sets from fixed rat corpus callosum. We extend this investigation to in vivo by using a live human subject on a clinical scanner. The analysis compares models of one, two, and three compartments and ranks their ability to explain the measured data. We enhance the original methodology to further evaluate the stability of the ranking.
  
  
  RESULTS
  As with fixed tissue, three compartment models explain the data best. However, a clearer hierarchical structure and simpler models emerge. We also find that splitting the scanning into shorter sessions has little effect on the ranking of models, and that the results are broadly reproducible across sessions.
  
  
  CONCLUSIONS
  Three compartments are required to explain diffusion MR measurements from in vivo corpus callosum, which informs the choice of model for microstructure imaging applications in the brain.
authors.affiliation: Department of Computer Science and Centre for Medical Image Computing, University College London, London, UK; NMR Research Unit, Department of Neuroinflammation, Institute of Neurology, University College London, London, UK.
authors.forename: Uran
authors.initials: U
authors.lastname: Ferizi
authors.forename: Torben
authors.initials: T
authors.lastname: Schneider
authors.forename: Eleftheria
authors.initials: E
authors.lastname: Panagiotaki
authors.forename: Gemma
authors.initials: G
authors.lastname: Nedjati-Gilani
authors.forename: Hui
authors.initials: H
authors.lastname: Zhang
authors.forename: Claudia A M
authors.initials: CA
authors.lastname: Wheeler-Kingshott
authors.forename: Daniel C
authors.initials: DC
authors.lastname: Alexander
mesh_terms: D001834:Body Water / Q000378:metabolism*; D001921:Brain / Q000033:anatomy & histology* / Q000378:metabolism*; D003198:Computer Simulation; D004058:Diffusion; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008959:Models, Neurological; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: brain imaging; diffusion magnetic resonance imaging; microstructure imaging; white matter