Multiplexed sensitivity-encoding (MUSE) DWI with deep learning-based reconstruction in breast MR imaging: A comparison with conventional DWI

Motivation: Conventional DWI has limitations due to low spatial resolution and geometry distortion. Multiplexed sensitivity-encoding (MUSE) DWI can obtain images with higher resolution and less distortion but require longer acquisition time. Goal(s): Our aim was to apply deep-learning based reconstruction (DLR) in MUSE DWI for breast imaging, and to investigate if DLR can shorten the scan time while maintaining image quality of MUSE. Approach: We compared quantitative parameters and subjective image quality of MUSE, MUSE-DLR, and conventional DWI. Results: MUSE-DLR showed improved image quality than MUSE with slightly longer acquisition time compared to conventional DWI. Impact: MUSE DWI with deep-learning based reconstruction can enhance the accuracy of clinical breast imaging while maintaining an acceptable scanning time, and also has the potential to improve diffusion imaging in other parts of the human body.