Abstract #5241

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

Michele Guerreri^1,2, Filip Szczepankiewicz^3,4, 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.

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