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
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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 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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