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Abstract #2527

Deep learning framework for accelerating revised-NODDI parameter estimation with tensor-valued diffusion encoding

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 demonstrates the feasibility of using deep learning (DL) to accelerate revised-NODDI parameter estimation with data acquired using tensor-valued diffusion encoding (TVDE). Revised-NODDI is a recently proposed version of NODDI which showed improved compatibility with TVDE. Thanks to this compatibility the model has an extra free parameter to be estimated which, with conventional fitting methods, further slowdown NODDI’s time-demanding parameter estimation. DL methods can vastly accelerate this process. We show that accurate estimation of revised-NODDI parameters can be obtained via a DL framework. We compare the results with those obtained with conventional fitting methods.

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