Malte Steinhoff^{1}, Alfred Mertins^{1}, and Peter Börnert^{2,3}

^{1}Institute for Signal Processing, University of Luebeck, Luebeck, Germany, ^{2}Philips Research Europe, Hamburg, Germany, ^{3}Department 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.

The proposed method without (MAPE) and with U-Net (MAPE + U-Net) is compared to extra-navigated IRIS

In-vivo brain data with 4, 5, and 8 shots was attained from 7 healthy volunteers using a 13-channel head coil, 3T Philips Ingenia, b=1000 s/mm

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