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Abstract #1442

Deep learning reconstruction including de-streaking capability for motion-robust T1-weighted breast imaging

Ping N Wang1, Sagar Mandava2, Xinzeng Wang3, Ty A Cashen4, Frederick Felcz5, and James H Holmes5
1Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, United States, 2Global MR Applications and Workflow, GE Healthcare, Atlanta, GA, United States, 3Global MR Applications and Workflow, GE Healthcare, Houston, TX, United States, 4Global MR Applications and Workflow, GE Healthcare, Madison, WI, United States, 5Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States

DCE imaging is the primary technique for MR evaluation of breast cancer. A key problem is ghosting due to cardiac motion obscuring axillary breast tissue. Addressing this challenge, motion-insensitive technologies such as stack-of-stars acquisition have been proposed, which introduces its own problem of streaking. Based on success with a DL reconstruction to reduce noise, blurring, and ringing, this work investigated re-purposing this deep CNN to also mitigate streaking. Phantom imaging demonstrated improved CNR and more accurate line profiles. 15 patients undergoing a clinical MR exam were scanned with the additional motion-robust method, and images showed reduced structured/unstructured noise and blurring.

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