PT  - JOURNAL ARTICLE
AU  - Luke J. Edwards
AU  - Kerrin J. Pine
AU  - Nikolaus Weiskopf
AU  - Siawoosh Mohammadi
TI  - NODDI-DTI: extracting neurite orientation and dispersion parameters from a diffusion tensor
AID  - 10.1101/077099
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 077099
4099  - http://biorxiv.org/content/early/2017/01/21/077099.short
4100  - http://biorxiv.org/content/early/2017/01/21/077099.full
AB  - NODDI-DTI is a simplification of the NODDI model that, when its underlying assumptions are met, allows extraction of biophysical parameters from standard single-shell DTI data, permitting biophysical analysis of DTI datasets.In contrast to the NODDI signal model, the NODDI-DTI signal model contains no CSF compartment, restricting its application to voxels with very low CSF partial-volume contamination. This simplification allowed derivation of simple analytical relations between parameters representing axon density, v and dispersion, τ, and DTI invariants (MD and FA) through moment expansion of the NODDI-DTI signal model. These relations formally allow extraction of biophysical parameters from DTI data. It should be emphasised that the NODDI-DTI model inherits the strong assumptions of the NODDI model, and so is nominally restricted to analysis of healthy brains.NODDI-DTI parameter estimates were computed by applying the proposed analytical relations to DTI parameters extracted from the first shell of data, and compared to parameters extracted by fitting the NODDI-DTI model to (i) both shells (recommended) and (ii) the first shell (as for DTI) of data in the white matter of three different in vivo diffusion datasets. NODDI-DTI parameters estimated from DTI and NODDI-DTI parameters estimated by fitting the model to the first shell of data gave similar errors compared to two-shell NODDI-DTI estimates. The NODDI-DTI method gave unphysical parameter estimates in a small percentage of voxels, reflecting voxelwise DTI estimation error or NODDI-DTI model invalidity. In the course of evaluating the NODDI-DTI model it was found that diffusional kurtosis strongly biased DTI-based MD values, and so a novel heuristic correction requiring only DTI data was derived and used to account for this bias.Our results demonstrate that NODDI-DTI is a promising model and technique to interpret restricted datasets acquired for DTI analysis with greater biophysical specificity, though its limitations must be borne in mind.