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

Guerreri, M; Szczepankiewicz, F; Lampinen, B; Nilsson, M; Palombo, M; Capuani, S; Zhang, H; (2018) Revised NODDI model for diffusion MRI data with multiple b-tensor encodings. In: Proceedings of the Joint Annual Meeting ISMRM-ESMRMB. International Society for Magnetic Resonance in Medicine Green open access

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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.

Type: Proceedings paper
Title: Revised NODDI model for diffusion MRI data with multiple b-tensor encodings
Event: Joint Annual Meeting ISMRM-ESMRMB 2018
Location: Paris, France
Dates: 16th-21st June 2018
Open access status: An open access version is available from UCL Discovery
Publisher version: http://archive.ismrm.org/2018/5241.html
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10082897
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