Motion-robust distortion-free breast diffusion-weighted MRI using DW-PROPELLER with deep learning reconstruction

Motivation: EPI-based DWI suffers from ghosting, chemical shift, and distortion artifacts. FSE-based DW-PROPELLER has been shown to overcome the above artifacts but at the cost of longer scanner time. Goal(s): To evaluate the combination of DW-PROPELLER with a deep learning (DL)-based reconstruction to provide motion-robust distortion-free high spatial resolution breast DWI. Approach: Phantom and in-vivo breast images were acquired using DW-PROPELLER followed by both conventional and DL reconstruction. Results: DW-PROPELLER with DL showed less distortion, less chemical shift artifacts, and increased SNR and sharpness compared with multi-shot DW EPI in both phantom and in-vivo breast imaging. Impact: This work demonstrated the feasibility of using a deep learning-based approach to improve image sharpness, reduce noise, and chemical shift artifacts for motion-robust and distortion-free high spatial resolution diffusion-weighted breast imaging.