Estimating axial diffusivity in the NODDI model

Howard, Amy F. D., Cottaar, Michiel, Drakesmith, Mark ORCID: https://orcid.org/0000-0001-8574-9560, Fan, Qiuyun, Huang, Susie Y., Jones, Derek K. ORCID: https://orcid.org/0000-0003-4409-8049, Lange, Frederik J., Mollink, Jeroen, Umesh Rudrapatna, Suryanarayana ORCID: https://orcid.org/0000-0001-7114-566X, Tian, Qiyuan, Miller, Karla L and Jbabdi, Saad 2022. Estimating axial diffusivity in the NODDI model. NeuroImage 262 , 119535. 10.1016/j.neuroimage.2022.119535

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Abstract

To estimate microstructure-related parameters from diffusion MRI data, biophysical models make strong, simplifying assumptions about the underlying tissue. The extent to which many of these assumptions are valid remains an open research question. This study was inspired by the disparity between the estimated intra-axonal axial diffusivity from literature and that typically assumed by the Neurite Orientation Dispersion and Density Imaging (NODDI) model ( d ∥ = 1.7 μ m 2 /ms ). We first demonstrate how changing the assumed axial diffusivity results in considerably different NODDI parameter estimates. Second, we illustrate the ability to estimate axial diffusivity as a free parameter of the model using high b-value data and an adapted NODDI framework. Using both simulated and in vivo data we investigate the impact of fitting to either real-valued or magnitude data, with Gaussian and Rician noise characteristics respectively, and what happens if we get the noise assumptions wrong in this high b-value and thus low SNR regime. Our results from real-valued human data estimate intra-axonal axial diffusivities of ∼ 2 − 2.5 μ m 2 /ms , in line with current literature. Crucially, our results demonstrate the importance of accounting for both a rectified noise floor and/or a signal offset to avoid biased parameter estimates when dealing with low SNR data.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Psychology Cardiff University Brain Research Imaging Centre (CUBRIC)
Additional Information:	License information from Publisher: LICENSE 1: URL: http://creativecommons.org/licenses/by-nc-nd/4.0/, Start Date: 2022-08-02
Publisher:	Elsevier
ISSN:	1053-8119
Date of First Compliant Deposit:	5 August 2022
Date of Acceptance:	1 August 2022
Last Modified:	12 May 2023 21:12
URI:	https://orca.cardiff.ac.uk/id/eprint/151726

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