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Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

M. Molina-Romero, PA. Gómez, S. Albarqouni, JI. Sperl, MI. Menzel, B. Menze
Deep learning with synthetic data for free water elimination in diffusion MRI
Proceedings of the International Society of Magnetic Resonance in Medicine (ISMRM), Paris, France, June 2018 (bib)

Diffusion metrics are typically biased by Cerebrospinal fluid (CSF) contamination. In this work, we present a deep learning based solution to remove the CSF contribution. First, we train an artificial neural network with synthetic data to estimate the tissue volume fraction. Second, we use the resulting network to predict estimates of the tissue volume fraction for real data, and use them to correct for CSF contamination. Results show corrected CSF contribution which, in turn, indicates that the tissue volume fraction can be estimated using this joint data generation and deep learning approach.
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