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Abstract #1981

dtiRIM: A recurrent inference machine for diffusion tensor estimation

Emanoel R. Sabidussi1, Stefan Klein1, Ben Jeurissen2, and Dirk H. J. Poot1
1Radiology, Erasmus Medical Center, Rotterdam, Netherlands, 2Department of Physics, Imec-Vision Lab, University of Antwerp, Antwerp, Belgium


In Diffusion MRI, the large variability of acquisition schemes limits broader use of Deep Learning for parameter estimation, with data-specific models needed for high-quality predictions. To reduce dependency on training data, we present dtiRIM, a recurrent neural network that learns a regularized solution to a model-based inverse problem. Using the diffusion model allows independent parameters (e.g. gradient directions) to also influence the estimation. We show that a single dtiRIM model predicts diffusion parameters for multiple datasets with lower error than the current state-of-the-art. Our study suggests that dtiRIM has the potential to be the first general learning method for DTI.

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