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Abstract #3353

Estimation and Correction of Image Phase in diffusion weighted MRI using Deep Learning

Carolin Hecking-Veltman1,2, Carolin M. Pirkl1,2, Jonathan Dannenberg2,3, Rolf F. Schulte2, Tim Sprenger4, Bjoern H. Menze1, and Marion I. Menzel2,3

1Computer Science, Technical University Munich, Munich, Germany, 2GE Healthcare, Munich, Germany, 3Physics, Technical University Munich, Munich, Germany, 4MR Applied Science Laboratory Europe, GE Healthcare, Stockholm, Sweden

The phase of diffusion weighted MR images (DWI) is regularly discarded in clinical application although it might contain valuable information, as it is composed of phase contributions due to rigid motion, eddy currents and brain pulsation among others.

In this work we take advantage of a neural network to separate the different phase components in individual DWI phase images. This enables estimating the amount of brain pulsation from DWI and modelling brain pulsation. The gained information may be used for phase correction, which eventually will allow using real-valued DWI (instead of magnitude DWI) to eliminate the Rician bias.

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