Revised NODDI model for diffusion MRI data with multiple b-tensor encodings

Guerreri, Michele, Szczepankiewicz, Filip, Lampinen, Bjorn, Nilsson, Markus, Palombo, Marco ORCID: https://orcid.org/0000-0003-4892-7967, Capuani, Silvia and Zhang, Gary Hui 2018. Revised NODDI model for diffusion MRI data with multiple b-tensor encodings. Presented at: Joint Annual Meeting ISMRM-ESMRMB 2018, 16-21 June 2018.

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Abstract

This work proposes a revision of the NODDI model to relate brain tissue microstructure to the new generation of diffusion MRI data with multiple b-tensor encodings. NODDI was developed originally for conventional multi-shell diffusion data acquired with linear tensor encoding (LTE). While adequate for LTE data, it has been shown to be incompatible with data using spherical tensor encoding (STE). We embed a different set of assumptions in NODDI, while retaining the tortuosity constraint, to accommodate both LTE and STE data. Experiments with human data with multiple b-tensor encodings confirm the efficacy of the revision.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Completion
Status:	Unpublished
Schools:	Psychology
Date of First Compliant Deposit:	20 May 2022
Last Modified:	10 Nov 2022 10:42
URI:	https://orca.cardiff.ac.uk/id/eprint/147873

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