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

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.