title: White matter compartment models for in vivo diffusion MRI at 300mT/m.
abstract: This paper compares a range of compartment models for diffusion MRI data on in vivo human acquisitions from a standard 60mT/m system (Philips 3T Achieva) and a unique 300mT/m system (Siemens Connectom). The key aim is to determine whether both systems support broadly the same models or whether the Connectom higher gradient system supports significantly more complex models. A single volunteer underwent 8h of acquisition on each system to provide uniquely wide and dense sampling of the available space of pulsed-gradient spin-echo (PGSE) measurements. We select a set of promising models from the wide set of possible three-compartment models for in vivo white matter (WM) that previous work and preliminary experiments suggest as strong candidates, but extend them to fit for compartmental T2 and diffusivity. We focus on the corpus callosum where the WM fibre architecture is simplest and compare their ability to explain the measured data, using Akaike's information criterion (AIC), and to predict unseen data, using cross-validation. We also compare the stability of parameter estimates in the presence of i) noise, using bootstrapping, and ii) spatial variation, using visual assessment and comparison with anatomical knowledge. Broadly similar models emerge from the AIC and cross-validation experiments in both data sets. Specifically, a three-compartment model consisting of either a Bingham distribution of sticks or a Cylinder for the intracellular compartment, an anisotropic diffusion tensor (DT) model for the extracellular compartment, as well as an isotropic CSF compartment, performs consistently well. However, various other models also perform well and no single model emerges as clear winner. The WM data (with virtually no CSF contamination) do not support compartmental T2 but partially support compartmental diffusivity. Evaluation of parameter stability favours simpler models than those identified by AIC or cross-validation. They suggest that the level of complexity in models underpinning currently popular microstructure imaging techniques such as NODDI, CHARMED, or ActiveAx, where the number of free parameters is about 4 or 5 rather than 10 or 11, may reflect the level of complexity achievable for a useful technique on current systems, although the 300mT/m data may support more complex models.
authors.affiliation: Department of Computer Science and Centre for Medical Image Computing, University College London, UK; Department of Neuroinflammation, Institute of Neurology, University College London, UK; Department of Radiology, New York University School of Medicine, USA. Electronic address: uran.ferizi@med.nyu.edu.
authors.forename: Uran
authors.initials: U
authors.lastname: Ferizi
authors.affiliation: Department of Neuroinflammation, Institute of Neurology, University College London, UK.
authors.forename: Torben
authors.initials: T
authors.lastname: Schneider
authors.affiliation: A.A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, USA.
authors.forename: Thomas
authors.initials: T
authors.lastname: Witzel
authors.affiliation: A.A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, USA.
authors.forename: Lawrence L
authors.initials: LL
authors.lastname: Wald
authors.affiliation: Department of Computer Science and Centre for Medical Image Computing, University College London, UK.
authors.forename: Hui
authors.initials: H
authors.lastname: Zhang
authors.affiliation: Department of Neuroinflammation, Institute of Neurology, University College London, UK.
authors.forename: Claudia A M
authors.initials: CA
authors.lastname: Wheeler-Kingshott
authors.affiliation: Department of Computer Science and Centre for Medical Image Computing, University College London, UK.
authors.forename: Daniel C
authors.initials: DC
authors.lastname: Alexander
mesh_terms: D003337:Corpus Callosum / Q000166:cytology*; D038524:Diffusion Magnetic Resonance Imaging; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008959:Models, Neurological; D008962:Models, Theoretical; D066127:White Matter / Q000166:cytology*
keywords: Connectom; brain microstructure; diffusion MRI; model selection