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Abstract #4367

Magnitude-regularized Phase Estimation (MAPE) with U-Net Support for Self-navigated Multi-shot Echo-planar DWI in the Brain

Malte Steinhoff1, Alfred Mertins1, and Peter Börnert2,3
1Institute for Signal Processing, University of Luebeck, Luebeck, Germany, 2Philips Research Europe, Hamburg, Germany, 3Department of Radiology, LUMC, Leiden, Netherlands

We propose a self-navigated iterative reconstruction algorithm for multi-shot DWI which effectively performs the shot phase updates with a fixed joint image prior. This framework further nicely incorporates deep learning generated image priors into the shot phase estimation while keeping the joint image production isolated. A U-Net is trained on extra-navigated data to mitigate phase cancellation artifacts. The algorithm with and without U-Net support is compared to self- and extra-navigated reference algorithms. The U-Net approach effectively mitigates phase-related signal cancellation artifacts. The improved multi-shot image prior regularizes the shot phase estimation enabling highly segmented self-navigated diffusion echo-planar imaging.

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