Meeting Banner
Abstract #5241

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

Michele Guerreri1,2, Filip Szczepankiewicz3,4, Björn Lampinen5, Markus Nilsson3, Marco Palombo6, Silvia Capuani2, and Hui Zhang6

1SAIMLAL, Sapienza, università di Roma, Rome, Italy, 2Institute for Complex Systems, CNR, Rome, Italy, 3Clinical Sciences Lund, Department of Radiology, Lund University, Lund, Sweden, 4Random Walk Imaging AB, Lund University, Lund, Sweden, 5Clinical Sciences Lund, Department of Medical Radiation Physics, Lund University, Lund, Sweden, 6Department of Computer Science & Centre for Medical Image Computing, University College London, London, United Kingdom

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.

This abstract and the presentation materials are available to members only; a login is required.

Join Here