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Abstract #2528

Revised-NODDI with conventional dMRI data enabled by deep learning

Michele Guerreri1,2, Hojjat Azadbakht2, and Hui Zhang1
1Computer Science & Centre for Medical Image Computing, University College London, London, United Kingdom, 2AINOSTICS, Manchester, United Kingdom


This work shows that deep learning (DL) enables revised-NODDI parameter estimation from conventional dMRI data. Revised-NODDI is a recently proposed model which overcomes some limitations of NODDI. With conventional fitting methods, revised-NODDI parameters can be robustly estimated only in the presence of data acquired with multiple tensor-valued diffusion encodings. However, this new generation of acquisitions is not yet routinely available in clinical research. We show that revised-NODDI parameters estimated using conventional dMRI data via a DL framework are comparable with the parameters estimated fitting the model to data acquired using multiple tensor-valued diffusion encoding.

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