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Abstract #0721

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

Pingni Wang1, Debosmita Biswas2, Lisa Wilmes3, Nola Hylton3, Bonnie N Joe3, Michael Senff4, Arnaud Guidon5, Patricia Lan6, Xinzeng Wang7, and Savannah C Partridge2
1Research and Scientific Affairs, GE Healthcare, Menlo Park, CA, United States, 2Radiology, University of Washington, Seattle, WA, United States, 3Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States, 4Fred Hutchinson Cancer Center, Seattle, WA, United States, 5GE Healthcare, Boston, MD, United States, 6GE Healthcare, Menlo Park, CA, United States, 7GE Healthcare, Houston, TX, United States

Synopsis

Keywords: Breast, Breast

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.

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Keywords

propeller