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.
|Publication status||Published - 2020|
|Event||Annual Meeting of the International Society of Magnetic Resonance in Medicine 2020|
- Virtual Conference
Duration: 08.08.2020 → 14.08.2020
|Conference||Annual Meeting of the International Society of Magnetic Resonance in Medicine 2020|
|Period||08.08.20 → 14.08.20|